From 5e379b765d6d7e9c4a41cfa0426b2386d813b47b Mon Sep 17 00:00:00 2001
From: YuanJianhao508 <820010508@qq.com>
Date: Wed, 29 May 2024 14:54:50 +0100
Subject: [PATCH] first update

---
 README.md                                     |    55 +-
 llava/__init__.py                             |     1 +
 llava/constants.py                            |    18 +
 llava/conversation.py                         |   398 +
 llava/eval/eval_gpt_mmvet.py                  |   276 +
 llava/eval/eval_gpt_review.py                 |   113 +
 llava/eval/eval_gpt_review_bench.py           |   121 +
 llava/eval/eval_gpt_review_visual.py          |   118 +
 llava/eval/eval_gqa.py                        |   499 +
 llava/eval/eval_pope.py                       |    81 +
 llava/eval/eval_science_qa.py                 |   114 +
 llava/eval/eval_science_qa_gpt4.py            |   104 +
 llava/eval/eval_science_qa_gpt4_requery.py    |   149 +
 llava/eval/eval_textvqa.py                    |    65 +
 .../eval/generate_webpage_data_from_table.py  |   111 +
 llava/eval/m4c_evaluator.py                   |   334 +
 llava/eval/model_qa.py                        |    85 +
 llava/eval/model_vqa.py                       |   112 +
 llava/eval/model_vqa_loader.py                |   148 +
 llava/eval/model_vqa_mmbench.py               |   175 +
 llava/eval/model_vqa_science.py               |   150 +
 llava/eval/qa_baseline_gpt35.py               |    74 +
 llava/eval/run_llava.py                       |    97 +
 llava/eval/summarize_gpt_review.py            |    60 +
 .../table/caps_boxes_coco2014_val_80.jsonl    |    80 +
 llava/eval/table/model.jsonl                  |     5 +
 llava/eval/table/prompt.jsonl                 |     4 +
 llava/eval/table/question.jsonl               |    80 +
 llava/eval/table/reviewer.jsonl               |     4 +
 llava/eval/table/rule.json                    |    11 +
 .../video/eval_benchmark_1_correctness.py     |   191 +
 .../eval_benchmark_2_detailed_orientation.py  |   191 +
 llava/eval/video/eval_benchmark_3_context.py  |   191 +
 llava/eval/video/eval_benchmark_4_temporal.py |   190 +
 .../video/eval_benchmark_5_consistency.py     |   198 +
 llava/eval/video/eval_video_qa.py             |   206 +
 .../run_inference_benchmark_consistency.py    |    96 +
 .../video/run_inference_benchmark_general.py  |    87 +
 llava/eval/video/run_inference_video_qa.py    |   171 +
 .../eval/video/run_inference_video_qa_act.py  |   171 +
 llava/eval/webpage/index.html                 |   162 +
 llava/eval/webpage/script.js                  |   245 +
 llava/eval/webpage/styles.css                 |   105 +
 llava/mm_utils.py                             |   123 +
 llava/model/__init__.py                       |     2 +
 llava/model/apply_delta.py                    |    48 +
 llava/model/builder.py                        |   173 +
 llava/model/consolidate.py                    |    29 +
 llava/model/language_model/llava_llama.py     |   145 +
 llava/model/language_model/llava_llama_v1.py  |   155 +
 llava/model/language_model/llava_mpt.py       |   113 +
 llava/model/llava_arch.py                     |   346 +
 llava/model/make_delta.py                     |    52 +
 llava/model/multimodal_encoder/builder.py     |    69 +
 .../model/multimodal_encoder/clip_encoder.py  |    78 +
 llava/model/multimodal_encoder/mae_encoder.py |    80 +
 llava/model/multimodal_projector/builder.py   |   257 +
 llava/model/utils.py                          |    20 +
 llava/serve/__init__.py                       |     0
 llava/serve/cli.py                            |   145 +
 llava/serve/controller.py                     |   298 +
 llava/serve/eval_custom.py                    |   159 +
 llava/serve/eval_custom_chunck.py             |   184 +
 llava/serve/eval_custom_predsig.py            |   166 +
 llava/serve/examples/desert.jpg               |   Bin 0 -> 262144 bytes
 llava/serve/examples/extreme_ironing.jpg      |   Bin 0 -> 62587 bytes
 llava/serve/examples/sample_demo_1.mp4        |   Bin 0 -> 262144 bytes
 llava/serve/examples/sample_demo_13.mp4       |   Bin 0 -> 262144 bytes
 llava/serve/examples/sample_demo_22.mp4       |   Bin 0 -> 262144 bytes
 llava/serve/examples/sample_demo_3.mp4        |   Bin 0 -> 262144 bytes
 llava/serve/examples/sample_demo_8.mp4        |   Bin 0 -> 262144 bytes
 llava/serve/examples/sample_demo_9.mp4        |   Bin 0 -> 262144 bytes
 llava/serve/examples/sample_img_13.png        |   Bin 0 -> 48079 bytes
 llava/serve/examples/sample_img_22.png        |   Bin 0 -> 60836 bytes
 llava/serve/examples/sample_img_8.png         |   Bin 0 -> 262144 bytes
 llava/serve/examples/waterview.jpg            |   Bin 0 -> 95499 bytes
 llava/serve/gradio_utils.py                   |   141 +
 llava/serve/gradio_web_server.py              |   253 +
 llava/serve/model_worker.py                   |   285 +
 llava/serve/register_worker.py                |    26 +
 llava/serve/test_message.py                   |    62 +
 llava/serve/video_caption.py                  |   148 +
 llava/train/llama_flash_attn_monkey_patch.py  |   115 +
 .../train/llama_xformers_attn_monkey_patch.py |   129 +
 llava/train/llava_trainer.py                  |   176 +
 llava/train/train.py                          |  1095 +
 llava/train/train_mem.py                      |    18 +
 llava/train/train_xformers.py                 |    13 +
 llava/utils.py                                |   126 +
 pyproject.toml                                |    36 +
 retrieval/BDDX_RAG_hybird_vpmatch.json        | 22796 +++++++++++++++
 retrieval/BDDX_RAG_neg_3.json                 | 17697 ++++++++++++
 retrieval/BDDX_RAG_pos_3.json                 | 19893 ++++++++++++++
 retrieval/BDDX_RAG_tuned_vpmatch_t13.json     | 22928 ++++++++++++++++
 retrieval/BDDX_RAG_visual_vpmatch.json        | 22796 +++++++++++++++
 retrieval/bddx_vpath_info_match.json          |  1878 ++
 retrieval/check_sim.py                        |    49 +
 retrieval/embeddings_project.npz              |   Bin 0 -> 1048576 bytes
 retrieval/final_match.py                      |   238 +
 retrieval/matching.py                         |   327 +
 retrieval/project.py                          |   105 +
 retrieval/projector/best_model.pth            |   Bin 0 -> 786432 bytes
 retrieval/rag.py                              |    85 +
 retrieval/train.py                            |   195 +
 retrieval/train_projector.sh                  |     5 +
 retrieval/vpath_info_match.py                 |    55 +
 scripts/finetune.sh                           |    46 +
 scripts/pretrain.sh                           |    43 +
 scripts/zero2.json                            |    23 +
 scripts/zero3.json                            |    28 +
 scripts/zero3_offload.json                    |    56 +
 video_process/create_bddx_json.py             |   170 +
 112 files changed, 120523 insertions(+), 1 deletion(-)
 create mode 100644 llava/__init__.py
 create mode 100644 llava/constants.py
 create mode 100644 llava/conversation.py
 create mode 100644 llava/eval/eval_gpt_mmvet.py
 create mode 100644 llava/eval/eval_gpt_review.py
 create mode 100644 llava/eval/eval_gpt_review_bench.py
 create mode 100644 llava/eval/eval_gpt_review_visual.py
 create mode 100644 llava/eval/eval_gqa.py
 create mode 100644 llava/eval/eval_pope.py
 create mode 100644 llava/eval/eval_science_qa.py
 create mode 100644 llava/eval/eval_science_qa_gpt4.py
 create mode 100644 llava/eval/eval_science_qa_gpt4_requery.py
 create mode 100644 llava/eval/eval_textvqa.py
 create mode 100644 llava/eval/generate_webpage_data_from_table.py
 create mode 100644 llava/eval/m4c_evaluator.py
 create mode 100644 llava/eval/model_qa.py
 create mode 100644 llava/eval/model_vqa.py
 create mode 100644 llava/eval/model_vqa_loader.py
 create mode 100644 llava/eval/model_vqa_mmbench.py
 create mode 100644 llava/eval/model_vqa_science.py
 create mode 100644 llava/eval/qa_baseline_gpt35.py
 create mode 100644 llava/eval/run_llava.py
 create mode 100644 llava/eval/summarize_gpt_review.py
 create mode 100644 llava/eval/table/caps_boxes_coco2014_val_80.jsonl
 create mode 100644 llava/eval/table/model.jsonl
 create mode 100644 llava/eval/table/prompt.jsonl
 create mode 100644 llava/eval/table/question.jsonl
 create mode 100644 llava/eval/table/reviewer.jsonl
 create mode 100644 llava/eval/table/rule.json
 create mode 100644 llava/eval/video/eval_benchmark_1_correctness.py
 create mode 100644 llava/eval/video/eval_benchmark_2_detailed_orientation.py
 create mode 100644 llava/eval/video/eval_benchmark_3_context.py
 create mode 100644 llava/eval/video/eval_benchmark_4_temporal.py
 create mode 100644 llava/eval/video/eval_benchmark_5_consistency.py
 create mode 100644 llava/eval/video/eval_video_qa.py
 create mode 100644 llava/eval/video/run_inference_benchmark_consistency.py
 create mode 100644 llava/eval/video/run_inference_benchmark_general.py
 create mode 100644 llava/eval/video/run_inference_video_qa.py
 create mode 100644 llava/eval/video/run_inference_video_qa_act.py
 create mode 100644 llava/eval/webpage/index.html
 create mode 100644 llava/eval/webpage/script.js
 create mode 100644 llava/eval/webpage/styles.css
 create mode 100644 llava/mm_utils.py
 create mode 100644 llava/model/__init__.py
 create mode 100644 llava/model/apply_delta.py
 create mode 100644 llava/model/builder.py
 create mode 100644 llava/model/consolidate.py
 create mode 100644 llava/model/language_model/llava_llama.py
 create mode 100644 llava/model/language_model/llava_llama_v1.py
 create mode 100644 llava/model/language_model/llava_mpt.py
 create mode 100644 llava/model/llava_arch.py
 create mode 100644 llava/model/make_delta.py
 create mode 100644 llava/model/multimodal_encoder/builder.py
 create mode 100644 llava/model/multimodal_encoder/clip_encoder.py
 create mode 100644 llava/model/multimodal_encoder/mae_encoder.py
 create mode 100644 llava/model/multimodal_projector/builder.py
 create mode 100644 llava/model/utils.py
 create mode 100644 llava/serve/__init__.py
 create mode 100644 llava/serve/cli.py
 create mode 100644 llava/serve/controller.py
 create mode 100644 llava/serve/eval_custom.py
 create mode 100644 llava/serve/eval_custom_chunck.py
 create mode 100644 llava/serve/eval_custom_predsig.py
 create mode 100644 llava/serve/examples/desert.jpg
 create mode 100644 llava/serve/examples/extreme_ironing.jpg
 create mode 100644 llava/serve/examples/sample_demo_1.mp4
 create mode 100644 llava/serve/examples/sample_demo_13.mp4
 create mode 100644 llava/serve/examples/sample_demo_22.mp4
 create mode 100644 llava/serve/examples/sample_demo_3.mp4
 create mode 100644 llava/serve/examples/sample_demo_8.mp4
 create mode 100644 llava/serve/examples/sample_demo_9.mp4
 create mode 100644 llava/serve/examples/sample_img_13.png
 create mode 100644 llava/serve/examples/sample_img_22.png
 create mode 100644 llava/serve/examples/sample_img_8.png
 create mode 100644 llava/serve/examples/waterview.jpg
 create mode 100644 llava/serve/gradio_utils.py
 create mode 100644 llava/serve/gradio_web_server.py
 create mode 100644 llava/serve/model_worker.py
 create mode 100644 llava/serve/register_worker.py
 create mode 100644 llava/serve/test_message.py
 create mode 100644 llava/serve/video_caption.py
 create mode 100644 llava/train/llama_flash_attn_monkey_patch.py
 create mode 100644 llava/train/llama_xformers_attn_monkey_patch.py
 create mode 100644 llava/train/llava_trainer.py
 create mode 100644 llava/train/train.py
 create mode 100644 llava/train/train_mem.py
 create mode 100644 llava/train/train_xformers.py
 create mode 100644 llava/utils.py
 create mode 100644 pyproject.toml
 create mode 100644 retrieval/BDDX_RAG_hybird_vpmatch.json
 create mode 100644 retrieval/BDDX_RAG_neg_3.json
 create mode 100644 retrieval/BDDX_RAG_pos_3.json
 create mode 100644 retrieval/BDDX_RAG_tuned_vpmatch_t13.json
 create mode 100644 retrieval/BDDX_RAG_visual_vpmatch.json
 create mode 100644 retrieval/bddx_vpath_info_match.json
 create mode 100644 retrieval/check_sim.py
 create mode 100644 retrieval/embeddings_project.npz
 create mode 100644 retrieval/final_match.py
 create mode 100644 retrieval/matching.py
 create mode 100644 retrieval/project.py
 create mode 100644 retrieval/projector/best_model.pth
 create mode 100644 retrieval/rag.py
 create mode 100644 retrieval/train.py
 create mode 100644 retrieval/train_projector.sh
 create mode 100644 retrieval/vpath_info_match.py
 create mode 100644 scripts/finetune.sh
 create mode 100644 scripts/pretrain.sh
 create mode 100644 scripts/zero2.json
 create mode 100644 scripts/zero3.json
 create mode 100644 scripts/zero3_offload.json
 create mode 100644 video_process/create_bddx_json.py

diff --git a/README.md b/README.md
index b1accae..3efd4d0 100644
--- a/README.md
+++ b/README.md
@@ -12,4 +12,57 @@ Official GitHub repository for "RAG-Driver: Generalisable Driving Explanations w
 **RAG-Driver** is a Multi-Modal Large Language Model with Retrieval-augmented In-context Learning capacity designed for generalisable and explainable end-to-end driving with strong zeroshot generalisation capacity.
 
 ## News <a name="highlight"></a>
-Codes and models will be released soon
+## 📰 News
+* **[2024.05.27]**  Code update is in Progress, this repo is under active maintenance. 
+
+
+## TODO List
+- [ ] Uploading the processed version of BDDX.
+- [ ] Uploading the model checkpoint.
+- [ ] Releasing Spoken-SAX dataset.
+- [ ] further cleaning of retrieval engine codebase
+
+## Usage
+
+### Requirements and Installation
+* Python >= 3.10
+* Pytorch == 2.0.1
+* CUDA Version >= 11.7
+* Install required packages:
+```bash
+git clone https://github.com/YuanJianhao508/RAG-Driver.git
+cd RAG-DRIVER
+conda create -n ragdriver python=3.10 -y
+conda activate ragdriver
+pip install --upgrade pip  # enable PEP 660 support
+pip install -e .
+pip install -e ".[train]"
+pip install flash-attn --no-build-isolation
+pip install decord opencv-python git+https://github.com/facebookresearch/pytorchvideo.git@28fe037d212663c6a24f373b94cc5d478c8c1a1d
+```
+
+### Instruction Tuning on BDD-X dataset
+
+```bash
+bash ./scripts/finetune.sh
+```
+
+- Download pre-trained Video-LLaVA LLM and projector checkpoint from [here](https://huggingface.co/LanguageBind/Video-LLaVA-7B) and [here](https://huggingface.co/LanguageBind/Video-LLaVA-Pretrain-7B) and specify path in '--model_name_or_path' and '--pretrain_mm_mlp_adapter'.
+- Download pre-trained LanguageBind encoder from [here](https://huggingface.co/LanguageBind/LanguageBind_Video_merge) and specify path in '--video_tower'.
+- Change the batch size '--per_device_train_batch_size' and gradient accumulation step '--gradient_accumulation_steps' based on the number of gpu available, please ensure the effective batch size (i.e. --per_device_train_batch_size * gradient accumulation step * number of gpus) equals '128'.
+
+
+## Citations
+If you find our paper and code useful in your research, please consider citing:
+```BibTeX
+@article{yuan2024rag,
+  title={RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model},
+  author={Yuan, Jianhao and Sun, Shuyang and Omeiza, Daniel and Zhao, Bo and Newman, Paul and Kunze, Lars and Gadd, Matthew},
+  journal={arXiv preprint arXiv:2402.10828},
+  year={2024}
+}
+}
+```
+
+## Acknowledgement
+This repo is built on [Video-LLaVA](https://github.com/haotian-liu/LLaVA) and [ADAPT](https://github.com/jxbbb/ADAPT). We thank all the authors for their open-sourced codebase. 
\ No newline at end of file
diff --git a/llava/__init__.py b/llava/__init__.py
new file mode 100644
index 0000000..4d1f016
--- /dev/null
+++ b/llava/__init__.py
@@ -0,0 +1 @@
+from .model import LlavaLlamaForCausalLM
diff --git a/llava/constants.py b/llava/constants.py
new file mode 100644
index 0000000..f1bcfae
--- /dev/null
+++ b/llava/constants.py
@@ -0,0 +1,18 @@
+CONTROLLER_HEART_BEAT_EXPIRATION = 30
+WORKER_HEART_BEAT_INTERVAL = 15
+
+LOGDIR = "."
+
+# Model Constants
+IGNORE_INDEX = -100
+X_TOKEN_INDEX = {'IMAGE': -200, 'VIDEO': -201, 'AUDIO': -202, 'THERMAL': -203, 'DEPTH': -204}
+X_INDEX_TOKEN = {v: k for k, v in X_TOKEN_INDEX.items()}
+# IMAGE_TOKEN_INDEX = -200
+DEFAULT_X_TOKEN = {'IMAGE': "<image>", 'VIDEO': "<video>", 'AUDIO': "<audio>", 'THERMAL': "<thermal>", 'DEPTH': "<depth>"}
+# DEFAULT_IMAGE_TOKEN = "<image>"
+DEFAULT_X_PATCH_TOKEN = {'IMAGE': "<im_patch>", 'VIDEO': "<vi_patch>", 'AUDIO': "<au_patch>", 'THERMAL': "<th_patch>", 'DEPTH': "<de_patch>"}
+# DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
+DEFAULT_X_START_TOKEN = {'IMAGE': "<im_start>", 'VIDEO': "<vi_start>", 'AUDIO': "<au_start>", 'THERMAL': "<th_start>", 'DEPTH': "<de_start>"}
+# DEFAULT_IM_START_TOKEN = "<im_start>"
+DEFAULT_X_END_TOKEN = {'IMAGE': "<im_end>", 'VIDEO': "<vi_end>", 'AUDIO': "<au_end>", 'THERMAL': "<th_end>", 'DEPTH': "<de_end>"}
+# DEFAULT_IM_END_TOKEN = "<im_end>"
diff --git a/llava/conversation.py b/llava/conversation.py
new file mode 100644
index 0000000..6545ce1
--- /dev/null
+++ b/llava/conversation.py
@@ -0,0 +1,398 @@
+import dataclasses
+from enum import auto, Enum
+from typing import List, Tuple
+
+
+class SeparatorStyle(Enum):
+    """Different separator style."""
+    SINGLE = auto()
+    TWO = auto()
+    MPT = auto()
+    PLAIN = auto()
+    LLAMA_2 = auto()
+
+
+@dataclasses.dataclass
+class Conversation:
+    """A class that keeps all conversation history."""
+    system: str
+    roles: List[str]
+    messages: List[List[str]]
+    offset: int
+    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
+    sep: str = "###"
+    sep2: str = None
+    version: str = "Unknown"
+
+    skip_next: bool = False
+
+    def get_prompt(self):
+        messages = self.messages
+        if len(messages) > 0 and type(messages[0][1]) is tuple:
+            messages = self.messages.copy()
+            init_role, init_msg = messages[0].copy()
+            init_msg = init_msg[0].replace("<image>", "").strip()
+            if 'mmtag' in self.version:
+                messages[0] = (init_role, init_msg)
+                messages.insert(0, (self.roles[0], "<Image><image></Image>"))
+                messages.insert(1, (self.roles[1], "Received."))
+            else:
+                messages[0] = (init_role, "<image>\n" + init_msg)
+
+        if self.sep_style == SeparatorStyle.SINGLE:
+            ret = self.system + self.sep
+            for role, message in messages:
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    ret += role + ": " + message + self.sep
+                else:
+                    ret += role + ":"
+        elif self.sep_style == SeparatorStyle.TWO:
+            seps = [self.sep, self.sep2]
+            ret = self.system + seps[0]
+            for i, (role, message) in enumerate(messages):
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    ret += role + ": " + message + seps[i % 2]
+                else:
+                    ret += role + ":"
+        elif self.sep_style == SeparatorStyle.MPT:
+            ret = self.system + self.sep
+            for role, message in messages:
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    ret += role + message + self.sep
+                else:
+                    ret += role
+        elif self.sep_style == SeparatorStyle.LLAMA_2:
+            wrap_sys = lambda msg: f"<<SYS>>\n{msg}\n<</SYS>>\n\n"
+            wrap_inst = lambda msg: f"[INST] {msg} [/INST]"
+            ret = ""
+
+            for i, (role, message) in enumerate(messages):
+                if i == 0:
+                    assert message, "first message should not be none"
+                    assert role == self.roles[0], "first message should come from user"
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    if i == 0: message = wrap_sys(self.system) + message
+                    if i % 2 == 0:
+                        message = wrap_inst(message)
+                        ret += self.sep + message
+                    else:
+                        ret += " " + message + " " + self.sep2
+                else:
+                    ret += ""
+            ret = ret.lstrip(self.sep)
+        elif self.sep_style == SeparatorStyle.PLAIN:
+            seps = [self.sep, self.sep2]
+            ret = self.system
+            for i, (role, message) in enumerate(messages):
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    ret += message + seps[i % 2]
+                else:
+                    ret += ""
+        else:
+            raise ValueError(f"Invalid style: {self.sep_style}")
+
+        return ret
+
+    def append_message(self, role, message):
+        self.messages.append([role, message])
+
+    def get_images(self, return_pil=False):
+        images = []
+        for i, (role, msg) in enumerate(self.messages[self.offset:]):
+            if i % 2 == 0:
+                if type(msg) is tuple:
+                    import base64
+                    from io import BytesIO
+                    from PIL import Image
+                    msg, image, image_process_mode = msg
+                    if image_process_mode == "Pad":
+                        def expand2square(pil_img, background_color=(122, 116, 104)):
+                            width, height = pil_img.size
+                            if width == height:
+                                return pil_img
+                            elif width > height:
+                                result = Image.new(pil_img.mode, (width, width), background_color)
+                                result.paste(pil_img, (0, (width - height) // 2))
+                                return result
+                            else:
+                                result = Image.new(pil_img.mode, (height, height), background_color)
+                                result.paste(pil_img, ((height - width) // 2, 0))
+                                return result
+                        image = expand2square(image)
+                    elif image_process_mode in ["Default", "Crop"]:
+                        pass
+                    elif image_process_mode == "Resize":
+                        image = image.resize((336, 336))
+                    else:
+                        raise ValueError(f"Invalid image_process_mode: {image_process_mode}")
+                    max_hw, min_hw = max(image.size), min(image.size)
+                    aspect_ratio = max_hw / min_hw
+                    max_len, min_len = 800, 400
+                    shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
+                    longest_edge = int(shortest_edge * aspect_ratio)
+                    W, H = image.size
+                    if longest_edge != max(image.size):
+                        if H > W:
+                            H, W = longest_edge, shortest_edge
+                        else:
+                            H, W = shortest_edge, longest_edge
+                        image = image.resize((W, H))
+                    if return_pil:
+                        images.append(image)
+                    else:
+                        buffered = BytesIO()
+                        image.save(buffered, format="PNG")
+                        img_b64_str = base64.b64encode(buffered.getvalue()).decode()
+                        images.append(img_b64_str)
+        return images
+
+    def to_gradio_chatbot(self):
+        ret = []
+        for i, (role, msg) in enumerate(self.messages[self.offset:]):
+            if i % 2 == 0:
+                if type(msg) is tuple:
+                    import base64
+                    from io import BytesIO
+                    msg, image, image_process_mode = msg
+                    max_hw, min_hw = max(image.size), min(image.size)
+                    aspect_ratio = max_hw / min_hw
+                    max_len, min_len = 800, 400
+                    shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
+                    longest_edge = int(shortest_edge * aspect_ratio)
+                    W, H = image.size
+                    if H > W:
+                        H, W = longest_edge, shortest_edge
+                    else:
+                        H, W = shortest_edge, longest_edge
+                    image = image.resize((W, H))
+                    buffered = BytesIO()
+                    image.save(buffered, format="JPEG")
+                    img_b64_str = base64.b64encode(buffered.getvalue()).decode()
+                    img_str = f'<img src="data:image/png;base64,{img_b64_str}" alt="user upload image" />'
+                    msg = img_str + msg.replace('<image>', '').strip()
+                    ret.append([msg, None])
+                else:
+                    ret.append([msg, None])
+            else:
+                ret[-1][-1] = msg
+        return ret
+
+    def copy(self):
+        return Conversation(
+            system=self.system,
+            roles=self.roles,
+            messages=[[x, y] for x, y in self.messages],
+            offset=self.offset,
+            sep_style=self.sep_style,
+            sep=self.sep,
+            sep2=self.sep2,
+            version=self.version)
+
+    def dict(self):
+        if len(self.get_images()) > 0:
+            return {
+                "system": self.system,
+                "roles": self.roles,
+                "messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages],
+                "offset": self.offset,
+                "sep": self.sep,
+                "sep2": self.sep2,
+            }
+        return {
+            "system": self.system,
+            "roles": self.roles,
+            "messages": self.messages,
+            "offset": self.offset,
+            "sep": self.sep,
+            "sep2": self.sep2,
+        }
+
+
+conv_vicuna_v0 = Conversation(
+    system="A chat between a curious human and an artificial intelligence assistant. "
+           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
+    roles=("Human", "Assistant"),
+    messages=(
+        ("Human", "What are the key differences between renewable and non-renewable energy sources?"),
+        ("Assistant",
+            "Renewable energy sources are those that can be replenished naturally in a relatively "
+            "short amount of time, such as solar, wind, hydro, geothermal, and biomass. "
+            "Non-renewable energy sources, on the other hand, are finite and will eventually be "
+            "depleted, such as coal, oil, and natural gas. Here are some key differences between "
+            "renewable and non-renewable energy sources:\n"
+            "1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable "
+            "energy sources are finite and will eventually run out.\n"
+            "2. Environmental impact: Renewable energy sources have a much lower environmental impact "
+            "than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, "
+            "and other negative effects.\n"
+            "3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically "
+            "have lower operational costs than non-renewable sources.\n"
+            "4. Reliability: Renewable energy sources are often more reliable and can be used in more remote "
+            "locations than non-renewable sources.\n"
+            "5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different "
+            "situations and needs, while non-renewable sources are more rigid and inflexible.\n"
+            "6. Sustainability: Renewable energy sources are more sustainable over the long term, while "
+            "non-renewable sources are not, and their depletion can lead to economic and social instability.\n")
+    ),
+    offset=2,
+    sep_style=SeparatorStyle.SINGLE,
+    sep="###",
+)
+
+
+
+conv_vicuna_v1 = Conversation(
+    system="A chat between a curious user and an artificial intelligence assistant. "
+    "The assistant gives helpful, detailed, and polite answers to the user's questions.",
+    roles=("USER", "ASSISTANT"),
+    version="v1",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.TWO,
+    sep=" ",
+    sep2="</s>",
+)
+
+conv_llama_2 = Conversation(
+    system="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.""",
+    roles=("USER", "ASSISTANT"),
+    version="llama_v2",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.LLAMA_2,
+    sep="<s>",
+    sep2="</s>",
+)
+
+conv_llava_llama_2 = Conversation(
+    system="You are a helpful language and vision assistant. "
+           "You are able to understand the visual content that the user provides, "
+           "and assist the user with a variety of tasks using natural language.",
+    roles=("USER", "ASSISTANT"),
+    version="llama_v2",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.LLAMA_2,
+    sep="<s>",
+    sep2="</s>",
+)
+
+conv_mpt = Conversation(
+    system="""<|im_start|>system
+A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""",
+    roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
+    version="mpt",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.MPT,
+    sep="<|im_end|>",
+)
+
+conv_llava_plain = Conversation(
+    system="",
+    roles=("", ""),
+    messages=(
+    ),
+    offset=0,
+    sep_style=SeparatorStyle.PLAIN,
+    sep="\n",
+)
+
+conv_llava_v0 = Conversation(
+    system="A chat between a curious human and an artificial intelligence assistant. "
+           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
+    roles=("Human", "Assistant"),
+    messages=(
+    ),
+    offset=0,
+    sep_style=SeparatorStyle.SINGLE,
+    sep="###",
+)
+
+conv_llava_v0_mmtag = Conversation(
+    system="A chat between a curious user and an artificial intelligence assistant. "
+           "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
+           "The visual content will be provided with the following format: <Image>visual content</Image>.",
+    roles=("Human", "Assistant"),
+    messages=(
+    ),
+    offset=0,
+    sep_style=SeparatorStyle.SINGLE,
+    sep="###",
+    version="v0_mmtag",
+)
+
+conv_llava_v1 = Conversation(
+    system="A chat between a curious human and an artificial intelligence assistant. "
+           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
+    roles=("USER", "ASSISTANT"),
+    version="v1",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.TWO,
+    sep=" ",
+    sep2="</s>",
+)
+
+conv_llava_v1_mmtag = Conversation(
+    system="A chat between a curious user and an artificial intelligence assistant. "
+           "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
+           "The visual content will be provided with the following format: <Image>visual content</Image>.",
+    roles=("USER", "ASSISTANT"),
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.TWO,
+    sep=" ",
+    sep2="</s>",
+    version="v1_mmtag",
+)
+
+
+################################## For Driving
+conv_driving = Conversation(
+    system="A chat between a curious user and an artificial intelligence assistant designed for autonomous driving. "
+    "The assistant gives helpful, detailed, and polite answers to the user's questions including driving action justification and control signal.",
+    roles=("USER", "ASSISTANT"),
+    version="v1",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.TWO,
+    sep=" ",
+    sep2="</s>",
+)
+
+default_conversation = conv_vicuna_v0
+conv_templates = {
+    "default": conv_vicuna_v0,
+    "v0": conv_vicuna_v0,
+    "v1": conv_vicuna_v1,
+    "vicuna_v1": conv_vicuna_v1,
+    "llama_2": conv_llama_2,
+
+    "plain": conv_llava_plain,
+    "v0_plain": conv_llava_plain,
+    "llava_v0": conv_llava_v0,
+    "v0_mmtag": conv_llava_v0_mmtag,
+    "llava_v1": conv_llava_v1,
+    "v1_mmtag": conv_llava_v1_mmtag,
+    "llava_llama_2": conv_llava_llama_2,
+
+    "mpt": conv_mpt,
+    "driving": conv_driving,
+}
+
+
+if __name__ == "__main__":
+    print(default_conversation.get_prompt())
diff --git a/llava/eval/eval_gpt_mmvet.py b/llava/eval/eval_gpt_mmvet.py
new file mode 100644
index 0000000..9d306e9
--- /dev/null
+++ b/llava/eval/eval_gpt_mmvet.py
@@ -0,0 +1,276 @@
+import argparse
+
+import openai
+import json
+import os
+from tqdm import tqdm
+import pandas as pd
+import numpy as np
+from collections import Counter
+import time
+
+
+
+parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.')
+parser.add_argument('--mmvet_path')
+parser.add_argument('--ckpt_name')
+parser.add_argument('--result_path')
+args = parser.parse_args()
+
+
+openai.api_base = ""
+openai.api_key = ''
+
+gpt_model = "gpt-4-0613"
+
+
+prompt = """Compare the ground truth and prediction from AI models, to give a correctness score for the prediction. <AND> in the ground truth means it is totally right only when all elements in the ground truth are present in the prediction, and <OR> means it is totally right when any one element in the ground truth is present in the prediction. The correctness score is 0.0 (totally wrong), 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or 1.0 (totally right). Just complete the last space of the correctness score.
+
+Question | Ground truth | Prediction | Correctness
+--- | --- | --- | ---
+What is x in the equation? | -1 <AND> -5 | x = 3 | 0.0
+What is x in the equation? | -1 <AND> -5 | x = -1 | 0.5
+What is x in the equation? | -1 <AND> -5 | x = -5 | 0.5
+What is x in the equation? | -1 <AND> -5 | x = -5 or 5 | 0.5
+What is x in the equation? | -1 <AND> -5 | x = -1 or x = -5 | 1.0
+Can you explain this meme? | This meme is poking fun at the fact that the names of the countries Iceland and Greenland are misleading. Despite its name, Iceland is known for its beautiful green landscapes, while Greenland is mostly covered in ice and snow. The meme is saying that the person has trust issues because the names of these countries do not accurately represent their landscapes. | The meme talks about Iceland and Greenland. It's pointing out that despite their names, Iceland is not very icy and Greenland isn't very green. | 0.4
+Can you explain this meme? | This meme is poking fun at the fact that the names of the countries Iceland and Greenland are misleading. Despite its name, Iceland is known for its beautiful green landscapes, while Greenland is mostly covered in ice and snow. The meme is saying that the person has trust issues because the names of these countries do not accurately represent their landscapes. | The meme is using humor to point out the misleading nature of Iceland's and Greenland's names. Iceland, despite its name, has lush green landscapes while Greenland is mostly covered in ice and snow. The text 'This is why I have trust issues' is a playful way to suggest that these contradictions can lead to distrust or confusion. The humor in this meme is derived from the unexpected contrast between the names of the countries and their actual physical characteristics. | 1.0
+"""
+
+# load metadata
+# Download mm-vet.zip and `unzip mm-vet.zip` and change the path below
+mmvet_path = args.mmvet_path
+use_sub_set = False
+decimal_places = 1  # number of decimal places to round to
+
+if use_sub_set:
+    bard_set_file = os.path.join(mmvet_path, "bard_set.json")
+    with open(bard_set_file, 'r') as f:
+        sub_set = json.load(f)
+    sub_set_name = 'bardset'
+    sub_set_name = sub_set_name + '_'
+else:
+    sub_set = None
+    sub_set_name = ''
+
+mmvet_metadata = os.path.join(mmvet_path, "mm-vet.json")
+with open(mmvet_metadata, 'r') as f:
+    data = json.load(f)
+
+counter = Counter()
+cap_set_list = []
+cap_set_counter = []
+len_data = 0
+for id, value in data.items():
+    if sub_set is not None and id not in sub_set:
+        continue
+    question = value["question"]
+    answer = value["answer"]
+    cap = value["capability"]
+    cap = set(cap)
+    counter.update(cap)
+    if cap not in cap_set_list:
+        cap_set_list.append(cap)
+        cap_set_counter.append(1)
+    else:
+        cap_set_counter[cap_set_list.index(cap)] += 1
+
+    len_data += 1
+
+sorted_list = counter.most_common()
+columns = [k for k, v in sorted_list]
+columns.append("total")
+columns.append("std")
+columns.append('runs')
+df = pd.DataFrame(columns=columns)
+
+cap_set_sorted_indices = np.argsort(-np.array(cap_set_counter))
+new_cap_set_list = []
+new_cap_set_counter = []
+for index in cap_set_sorted_indices:
+    new_cap_set_list.append(cap_set_list[index])
+    new_cap_set_counter.append(cap_set_counter[index])
+
+cap_set_list = new_cap_set_list
+cap_set_counter = new_cap_set_counter
+cap_set_names = ["_".join(list(cap_set)) for cap_set in cap_set_list]
+
+columns2 = cap_set_names
+columns2.append("total")
+columns2.append("std")
+columns2.append('runs')
+df2 = pd.DataFrame(columns=columns2)
+
+
+
+
+
+
+
+
+###### change your model name ######
+model = args.ckpt_name
+result_path = args.result_path
+num_run = 1 # we set it as 5 in the paper
+model_results_file = os.path.join(result_path, f"{model}.json")
+
+# grade results for each sample to svae
+grade_file = f'{model}_{gpt_model}-grade-{num_run}runs.json'
+grade_file = os.path.join(result_path, grade_file)
+
+# score results regarding capabilities/capability integration to save
+cap_score_file = f'{model}_{sub_set_name}{gpt_model}-cap-score-{num_run}runs.csv'
+cap_score_file = os.path.join(result_path, cap_score_file)
+cap_int_score_file = f'{model}_{sub_set_name}{gpt_model}-cap-int-score-{num_run}runs.csv'
+cap_int_score_file = os.path.join(result_path, cap_int_score_file)
+
+with open(model_results_file) as f:
+    results = json.load(f)
+if os.path.exists(grade_file):
+    with open(grade_file, 'r') as f:
+        grade_results = json.load(f)
+else:
+    grade_results = {}
+
+
+def need_more_runs():
+    need_more_runs = False
+    if len(grade_results) > 0:
+        for k, v in grade_results.items():
+            if len(v['score']) < num_run:
+                need_more_runs = True
+                break
+    return need_more_runs or len(grade_results) < len_data
+
+
+while need_more_runs():
+    for j in range(num_run):
+        print(f'eval run {j}')
+        for id, line in tqdm(data.items()):
+            if sub_set is not None and id not in sub_set:
+                continue
+            if id in grade_results and len(grade_results[id]['score']) >= (j + 1):
+                continue
+
+            model_pred = results[id]
+
+            question = prompt + '\n' + ' | '.join(
+                [line['question'], line['answer'].replace("<AND>", " <AND> ").replace("<OR>", " <OR> "), model_pred,
+                 ""])
+            messages = [
+                {"role": "user", "content": question},
+            ]
+
+            if id not in grade_results:
+                sample_grade = {'model': [], 'content': [], 'score': []}
+            else:
+                sample_grade = grade_results[id]
+
+            grade_sample_run_complete = False
+            temperature = 0.0
+
+            while not grade_sample_run_complete:
+                try:
+                    response = openai.ChatCompletion.create(
+                        model=gpt_model,
+                        max_tokens=3,
+                        temperature=temperature,
+                        messages=messages)
+                    # print(response['model'])
+                    content = response['choices'][0]['message']['content']
+                    flag = True
+                    try_time = 1
+                    while flag:
+                        try:
+                            content = content.split(' ')[0].strip()
+                            score = float(content)
+                            if score > 1.0 or score < 0.0:
+                                assert False
+                            flag = False
+                        except:
+                            question = prompt + '\n' + ' | '.join(
+                                [line['question'], line['answer'].replace("<AND>", " <AND> ").replace("<OR>", " <OR> "),
+                                 model_pred, ""]) + "\nPredict the correctness of the answer (digit): "
+                            messages = [
+                                {"role": "user", "content": question},
+                            ]
+                            response = openai.ChatCompletion.create(
+                                model=gpt_model,
+                                max_tokens=3,
+                                temperature=temperature,
+                                messages=messages)
+                            # print(response)
+                            content = response['choices'][0]['message']['content']
+                            try_time += 1
+                            temperature += 0.5
+                            print(f"{id} try {try_time} times")
+                            print(content)
+                            if try_time > 5:
+                                score = 0.0
+                                flag = False
+                    grade_sample_run_complete = True
+                except:
+                    # gpt4 may have token rate limit
+                    print("sleep 1s")
+                    time.sleep(1)
+
+            if len(sample_grade['model']) >= j + 1:
+                sample_grade['model'][j] = response['model']
+                sample_grade['content'][j] = content
+                sample_grade['score'][j] = score
+            else:
+                sample_grade['model'].append(response['model'])
+                sample_grade['content'].append(content)
+                sample_grade['score'].append(score)
+            grade_results[id] = sample_grade
+
+            with open(grade_file, 'w') as f:
+                json.dump(grade_results, f, indent=4)
+
+assert not need_more_runs()
+cap_socres = {k: [0.0] * num_run for k in columns[:-2]}
+counter['total'] = len_data
+
+cap_socres2 = {k: [0.0] * num_run for k in columns2[:-2]}
+counter2 = {columns2[i]: cap_set_counter[i] for i in range(len(cap_set_counter))}
+counter2['total'] = len_data
+
+for k, v in grade_results.items():
+    if sub_set is not None and k not in sub_set:
+        continue
+    for i in range(num_run):
+        score = v['score'][i]
+        caps = set(data[k]['capability'])
+        for c in caps:
+            cap_socres[c][i] += score
+
+        cap_socres['total'][i] += score
+
+        index = cap_set_list.index(caps)
+        cap_socres2[cap_set_names[index]][i] += score
+        cap_socres2['total'][i] += score
+
+for k, v in cap_socres.items():
+    cap_socres[k] = np.array(v) / counter[k] * 100
+
+std = round(cap_socres['total'].std(), decimal_places)
+total_copy = cap_socres['total'].copy()
+runs = str(list(np.round(total_copy, decimal_places)))
+
+for k, v in cap_socres.items():
+    cap_socres[k] = round(v.mean(), decimal_places)
+
+cap_socres['std'] = std
+cap_socres['runs'] = runs
+df.loc[model] = cap_socres
+
+for k, v in cap_socres2.items():
+    cap_socres2[k] = round(np.mean(np.array(v) / counter2[k] * 100), decimal_places)
+cap_socres2['std'] = std
+cap_socres2['runs'] = runs
+df2.loc[model] = cap_socres2
+
+df.to_csv(cap_score_file)
+df2.to_csv(cap_int_score_file)
+print(df)
+print(df2)
\ No newline at end of file
diff --git a/llava/eval/eval_gpt_review.py b/llava/eval/eval_gpt_review.py
new file mode 100644
index 0000000..8af4559
--- /dev/null
+++ b/llava/eval/eval_gpt_review.py
@@ -0,0 +1,113 @@
+import argparse
+import json
+import os
+
+import openai
+import tqdm
+import ray
+import time
+
+NUM_SECONDS_TO_SLEEP = 3
+
+@ray.remote(num_cpus=4)
+def get_eval(content: str, max_tokens: int):
+    while True:
+        try:
+            response = openai.ChatCompletion.create(
+                model='gpt-4',
+                messages=[{
+                    'role': 'system',
+                    'content': 'You are a helpful and precise assistant for checking the quality of the answer.'
+                }, {
+                    'role': 'user',
+                    'content': content,
+                }],
+                temperature=0.2,  # TODO: figure out which temperature is best for evaluation
+                max_tokens=max_tokens,
+            )
+            break
+        except openai.error.RateLimitError:
+            pass
+        except Exception as e:
+            print(e)
+        time.sleep(NUM_SECONDS_TO_SLEEP)
+
+    print('success!')
+    return response['choices'][0]['message']['content']
+
+
+def parse_score(review):
+    try:
+        score_pair = review.split('\n')[0]
+        score_pair = score_pair.replace(',', ' ')
+        sp = score_pair.split(' ')
+        if len(sp) == 2:
+            return [float(sp[0]), float(sp[1])]
+        else:
+            print('error', review)
+            return [-1, -1]
+    except Exception as e:
+        print(e)
+        print('error', review)
+        return [-1, -1]
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.')
+    parser.add_argument('-q', '--question')
+    # parser.add_argument('-a', '--answer')
+    parser.add_argument('-a', '--answer-list', nargs='+', default=[])
+    parser.add_argument('-r', '--rule')
+    parser.add_argument('-o', '--output')
+    parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output')
+    args = parser.parse_args()
+
+    ray.init()
+
+    f_q = open(os.path.expanduser(args.question))
+    f_ans1 = open(os.path.expanduser(args.answer_list[0]))
+    f_ans2 = open(os.path.expanduser(args.answer_list[1]))
+    rule_dict = json.load(open(os.path.expanduser(args.rule), 'r'))
+
+    review_file = open(f'{args.output}', 'w')
+
+    js_list = []
+    handles = []
+    idx = 0
+    for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2):
+        # if idx == 1:
+        #     break
+
+        ques = json.loads(ques_js)
+        ans1 = json.loads(ans1_js)
+        ans2 = json.loads(ans2_js)
+
+        category = json.loads(ques_js)['category']
+        if category in rule_dict:
+            rule = rule_dict[category]
+        else:
+            rule = rule_dict['default']
+        prompt = rule['prompt']
+        role = rule['role']
+        content = (f'[Question]\n{ques["text"]}\n\n'
+                   f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n'
+                   f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n'
+                   f'[System]\n{prompt}\n\n')
+        js_list.append({
+            'id': idx+1,
+            'question_id': ques['question_id'],
+            'answer1_id': ans1['answer_id'],
+            'answer2_id': ans2['answer_id'],
+            'category': category})
+        idx += 1
+        handles.append(get_eval.remote(content, args.max_tokens))
+        # To avoid the rate limit set by OpenAI
+        time.sleep(NUM_SECONDS_TO_SLEEP)
+
+    reviews = ray.get(handles)
+    for idx, review in enumerate(reviews):
+        scores = parse_score(review)
+        js_list[idx]['content'] = review
+        js_list[idx]['tuple'] = scores
+        review_file.write(json.dumps(js_list[idx]) + '\n')
+    review_file.close()
diff --git a/llava/eval/eval_gpt_review_bench.py b/llava/eval/eval_gpt_review_bench.py
new file mode 100644
index 0000000..06160f2
--- /dev/null
+++ b/llava/eval/eval_gpt_review_bench.py
@@ -0,0 +1,121 @@
+import argparse
+import json
+import os
+
+import openai
+import time
+
+NUM_SECONDS_TO_SLEEP = 0.5
+
+
+def get_eval(content: str, max_tokens: int):
+    while True:
+        try:
+            response = openai.ChatCompletion.create(
+                model='gpt-4-0314',
+                messages=[{
+                    'role': 'system',
+                    'content': 'You are a helpful and precise assistant for checking the quality of the answer.'
+                }, {
+                    'role': 'user',
+                    'content': content,
+                }],
+                temperature=0.2,  # TODO: figure out which temperature is best for evaluation
+                max_tokens=max_tokens,
+            )
+            break
+        except openai.error.RateLimitError:
+            pass
+        except Exception as e:
+            print(e)
+        time.sleep(NUM_SECONDS_TO_SLEEP)
+
+    return response['choices'][0]['message']['content']
+
+
+def parse_score(review):
+    try:
+        score_pair = review.split('\n')[0]
+        score_pair = score_pair.replace(',', ' ')
+        sp = score_pair.split(' ')
+        if len(sp) == 2:
+            return [float(sp[0]), float(sp[1])]
+        else:
+            print('error', review)
+            return [-1, -1]
+    except Exception as e:
+        print(e)
+        print('error', review)
+        return [-1, -1]
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.')
+    parser.add_argument('-q', '--question')
+    parser.add_argument('-c', '--context')
+    parser.add_argument('-a', '--answer-list', nargs='+', default=[])
+    parser.add_argument('-r', '--rule')
+    parser.add_argument('-o', '--output')
+    parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output')
+    args = parser.parse_args()
+
+    f_q = open(os.path.expanduser(args.question))
+    f_ans1 = open(os.path.expanduser(args.answer_list[0]))
+    f_ans2 = open(os.path.expanduser(args.answer_list[1]))
+    rule_dict = json.load(open(os.path.expanduser(args.rule), 'r'))
+
+    if os.path.isfile(os.path.expanduser(args.output)):
+        cur_reviews = [json.loads(line) for line in open(os.path.expanduser(args.output))]
+    else:
+        cur_reviews = []
+
+    review_file = open(f'{args.output}', 'a')
+
+    context_list = [json.loads(line) for line in open(os.path.expanduser(args.context))]
+    image_to_context = {context['image']: context for context in context_list}
+
+    handles = []
+    idx = 0
+    for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2):
+        ques = json.loads(ques_js)
+        ans1 = json.loads(ans1_js)
+        ans2 = json.loads(ans2_js)
+
+        inst = image_to_context[ques['image']]
+
+        if isinstance(inst['caption'], list):
+            cap_str = '\n'.join(inst['caption'])
+        else:
+            cap_str = inst['caption']
+
+        category = 'llava_bench_' + json.loads(ques_js)['category']
+        if category in rule_dict:
+            rule = rule_dict[category]
+        else:
+            assert False, f"Visual QA category not found in rule file: {category}."
+        prompt = rule['prompt']
+        role = rule['role']
+        content = (f'[Context]\n{cap_str}\n\n'
+                   f'[Question]\n{ques["text"]}\n\n'
+                   f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n'
+                   f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n'
+                   f'[System]\n{prompt}\n\n')
+        cur_js = {
+            'id': idx+1,
+            'question_id': ques['question_id'],
+            'answer1_id': ans1.get('answer_id', ans1['question_id']),
+            'answer2_id': ans2.get('answer_id', ans2['answer_id']),
+            'category': category
+        }
+        if idx >= len(cur_reviews):
+            review = get_eval(content, args.max_tokens)
+            scores = parse_score(review)
+            cur_js['content'] = review
+            cur_js['tuple'] = scores
+            review_file.write(json.dumps(cur_js) + '\n')
+            review_file.flush()
+        else:
+            print(f'Skipping {idx} as we already have it.')
+        idx += 1
+        print(idx)
+    review_file.close()
diff --git a/llava/eval/eval_gpt_review_visual.py b/llava/eval/eval_gpt_review_visual.py
new file mode 100644
index 0000000..d6e407a
--- /dev/null
+++ b/llava/eval/eval_gpt_review_visual.py
@@ -0,0 +1,118 @@
+import argparse
+import json
+import os
+
+import openai
+import time
+
+NUM_SECONDS_TO_SLEEP = 0.5
+
+
+def get_eval(content: str, max_tokens: int):
+    while True:
+        try:
+            response = openai.ChatCompletion.create(
+                model='gpt-4-0314',
+                messages=[{
+                    'role': 'system',
+                    'content': 'You are a helpful and precise assistant for checking the quality of the answer.'
+                }, {
+                    'role': 'user',
+                    'content': content,
+                }],
+                temperature=0.2,  # TODO: figure out which temperature is best for evaluation
+                max_tokens=max_tokens,
+            )
+            break
+        except openai.error.RateLimitError:
+            pass
+        except Exception as e:
+            print(e)
+        time.sleep(NUM_SECONDS_TO_SLEEP)
+
+    return response['choices'][0]['message']['content']
+
+
+def parse_score(review):
+    try:
+        score_pair = review.split('\n')[0]
+        score_pair = score_pair.replace(',', ' ')
+        sp = score_pair.split(' ')
+        if len(sp) == 2:
+            return [float(sp[0]), float(sp[1])]
+        else:
+            print('error', review)
+            return [-1, -1]
+    except Exception as e:
+        print(e)
+        print('error', review)
+        return [-1, -1]
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.')
+    parser.add_argument('-q', '--question')
+    parser.add_argument('-c', '--context')
+    parser.add_argument('-a', '--answer-list', nargs='+', default=[])
+    parser.add_argument('-r', '--rule')
+    parser.add_argument('-o', '--output')
+    parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output')
+    args = parser.parse_args()
+
+    f_q = open(os.path.expanduser(args.question))
+    f_ans1 = open(os.path.expanduser(args.answer_list[0]))
+    f_ans2 = open(os.path.expanduser(args.answer_list[1]))
+    rule_dict = json.load(open(os.path.expanduser(args.rule), 'r'))
+
+    if os.path.isfile(os.path.expanduser(args.output)):
+        cur_reviews = [json.loads(line) for line in open(os.path.expanduser(args.output))]
+    else:
+        cur_reviews = []
+
+    review_file = open(f'{args.output}', 'a')
+
+    context_list = [json.loads(line) for line in open(os.path.expanduser(args.context))]
+    image_to_context = {context['image']: context for context in context_list}
+
+    handles = []
+    idx = 0
+    for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2):
+        ques = json.loads(ques_js)
+        ans1 = json.loads(ans1_js)
+        ans2 = json.loads(ans2_js)
+
+        inst = image_to_context[ques['image']]
+        cap_str = '\n'.join(inst['captions'])
+        box_str = '\n'.join([f'{instance["category"]}: {instance["bbox"]}' for instance in inst['instances']])
+
+        category = json.loads(ques_js)['category']
+        if category in rule_dict:
+            rule = rule_dict[category]
+        else:
+            assert False, f"Visual QA category not found in rule file: {category}."
+        prompt = rule['prompt']
+        role = rule['role']
+        content = (f'[Context]\n{cap_str}\n\n{box_str}\n\n'
+                   f'[Question]\n{ques["text"]}\n\n'
+                   f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n'
+                   f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n'
+                   f'[System]\n{prompt}\n\n')
+        cur_js = {
+            'id': idx+1,
+            'question_id': ques['question_id'],
+            'answer1_id': ans1.get('answer_id', ans1['question_id']),
+            'answer2_id': ans2.get('answer_id', ans2['answer_id']),
+            'category': category
+        }
+        if idx >= len(cur_reviews):
+            review = get_eval(content, args.max_tokens)
+            scores = parse_score(review)
+            cur_js['content'] = review
+            cur_js['tuple'] = scores
+            review_file.write(json.dumps(cur_js) + '\n')
+            review_file.flush()
+        else:
+            print(f'Skipping {idx} as we already have it.')
+        idx += 1
+        print(idx)
+    review_file.close()
diff --git a/llava/eval/eval_gqa.py b/llava/eval/eval_gqa.py
new file mode 100644
index 0000000..9b66f86
--- /dev/null
+++ b/llava/eval/eval_gqa.py
@@ -0,0 +1,499 @@
+# Evaluation code for GQA.
+# Computes a suite of metrics such as accuracy, consistency, plausibility and scores per question type and length.
+# Visit https://gqadataset.org/ for all information about the dataset, including examples, visualizations, paper and slides.
+#
+#
+# Metrics:
+# - Accuracy: Standard accuracy, computed over the balanced version of the dataset, which is more robust against
+#             cheating by making educated guesses. For each question-answer pair (q,a), we give 1 point if the
+#             predicted answer p matches a and 0 otherwise, and average over all questions in the dataset.
+#
+# - Consistency: A metric for the level of model's consistency across different questions. For each question-answer
+#                pair (q,a), we define a set Eq={q1, q2, ..., qn} of entailed questions, the answers to which can
+#                be unambiguously inferred given (q,a).
+#                Denote Q the set of all questions the model answered correctly. For each question q in Q, we
+#                measure the model's accuracy over the entailed questions Eq to get the score sq and finally
+#                average these results across all questions in Q.
+#
+# - Validity: Measures whether the model gives a "valid" answer - one that can theoretically be an answer
+#             to the question (e.g. a color to a color question, yes/no to a binary question etc.).
+#             We provide a set of valid answers to each questions over the final answer vocabulary, in
+#             the choices file, and use it to compute average validity across the dataset.
+#
+# - Plausibility: Measures whether the model answers are plausible, e.g. one that make sense in the real world,
+#                 e.g. not answering "purple" to a question about apple color (unless it's really purple).
+#                 We provide a set of all plausible answers to each questions, computed by looking at all
+#                 attributes and relations hold for various objects throughout the whole dataset scene graphs,
+#                 and use it to compute average model plausibility across the data.
+#
+# - Grounding: Only for attention models. Measures whether the model looks at the relevant regions in the
+#              image when answering a question. Each question in the dataset is annotated with the visual regions
+#              they refer to, which are then used to compute the level to which the model has a correct visual attention,
+#              which will allow to identify whether it really answers based on the image of by language-based guesses.
+#              Supports both spatial features and object-based features.
+#
+# - Distribution: Measures the overall match between the true answer distribution for different questions,
+#                 vs the overall distribution predicted by the model through its answers for all the data.
+#                 We use chi-square statistic to measure the degree of similarity between the distributions,
+#                 giving indication to the level of overall world-knowledge of the model
+#
+# - Accuracy per type: accuracy per question structural types (logic, compare, choose), and semantic type
+#                      (questions about attributes, relations, categories, objects or the whole scene).
+#
+# - Accuracy for length: accuracy as a function of the question length, in terms of (1) words number, and semantic
+#                        complexity - number of reasoning steps.
+#
+# We may support additional metrics (e.g. coverage) in the future.
+#
+#
+# Files format:
+# - predictions file format: JSON array: [{"questionId": str, "prediction": str}]
+# - attentions file format: JSON array:
+#   Spatial attention: [{"questionId": str, "attention": [mapSize x mapSize: float] }].
+#   Object-based attention:[{"questionId": str, "attention": [[x0, y0, x1, y1, float] x #regions] }]. 0 < x,y < 1.
+# - questions and choices files are provided as part of the dataset.
+#   see https://gqadataset.org/download.html for information about their format.
+#
+#
+# If you have any questions or comments, please feel free to send an email,
+# at dorarad@cs.stanford.edu. We hope you'll enjoy using the GQA dataset! :)
+#
+#
+
+from collections import defaultdict
+from tqdm import tqdm
+import argparse
+import os.path
+import glob
+import json
+import math
+
+##### Arguments
+##########################################################################################
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--tier', default="val", type=str, help="Tier, e.g. train, val")
+parser.add_argument('--scenes', default="{tier}_sceneGraphs.json", type=str, help="Scene graphs file name format.")
+parser.add_argument('--questions', default="{tier}_all_questions.json", type=str, help="Questions file name format.")
+parser.add_argument('--choices', default="{tier}_choices.json", type=str, help="Choices file name format.")
+parser.add_argument('--predictions', default="{tier}_predictions.json", type=str, help="Answers file name format.")
+parser.add_argument('--attentions', default="{tier}_attentions.json", type=str, help="Attentions file name format.")
+parser.add_argument('--consistency', action="store_true",
+                    help="True to compute consistency score (Need to provide answers to questions in val_all_questions.json).")
+parser.add_argument('--grounding', action="store_true",
+                    help="True to compute grounding score (If model uses attention).")
+parser.add_argument('--objectFeatures', action="store_true",
+                    help="True for object-based attention (False for spatial).")
+parser.add_argument('--mapSize', default=7, type=int,
+                    help="Optional, only to get attention score. Images features map size, mapSize * mapSize")
+args = parser.parse_args()
+
+print(
+    "Please make sure to use our provided visual features as gqadataset.org for better comparability. We provide both spatial and object-based features trained on GQA train set.")
+print(
+    "In particular please avoid using features from https://github.com/peteanderson80/bottom-up-attention since they were trained on images contained in the GQA validation set and thus may give false scores improvement.\n")
+
+if not args.consistency:
+    print("Please consider using --consistency to compute consistency scores for entailed questions.")
+    print("If you do so, please provide answers to all questions in val_all_questions.json.\n")
+
+if not args.grounding:
+    print("Please consider using --grounding to compute attention scores.")
+    print("If you do so, please provide attention maps through --attentions.\n")
+
+
+##### Files Loading
+##########################################################################################
+
+def loadFile(name):
+    # load standard json file
+    if os.path.isfile(name):
+        with open(name) as file:
+            data = json.load(file)
+    # load file chunks if too big
+    elif os.path.isdir(name.split(".")[0]):
+        data = {}
+        chunks = glob.glob('{dir}/{dir}_*.{ext}'.format(dir=name.split(".")[0], ext=name.split(".")[1]))
+        for chunk in chunks:
+            with open(chunk) as file:
+                data.update(json.load(file))
+    else:
+        raise Exception("Can't find {}".format(name))
+    return data
+
+
+# Load scene graphs
+print("Loading scene graphs...")
+try:
+    scenes = loadFile(args.scenes.format(tier=args.tier))
+except:
+    print('Failed to load scene graphs -- cannot evaluate grounding')
+    scenes = None  # for testdev
+
+# Load questions
+print("Loading questions...")
+questions = loadFile(args.questions)
+
+# Load choices
+print("Loading choices...")
+try:
+    choices = loadFile(args.choices.format(tier=args.tier))
+except:
+    print('Failed to load choices -- cannot evaluate validity or plausibility')
+    choices = None  # for testdev
+
+# Load predictions and turn them into a dictionary
+print("Loading predictions...")
+predictions = loadFile(args.predictions.format(tier=args.tier))
+predictions = {p["questionId"]: p["prediction"] for p in predictions}
+
+# Make sure all question have predictions
+for qid in questions:
+    if (qid not in predictions) and (args.consistency or questions[qid]["isBalanced"]):
+        print("no prediction for question {}. Please add prediction for all questions.".format(qid))
+        raise Exception("missing predictions")
+
+# Load attentions and turn them into a dictionary
+attentions = None
+if args.grounding:
+    with open(args.attentions.format(tier=args.tier)) as attentionsFile:
+        attentions = json.load(attentionsFile)
+        attentions = {a["questionId"]: a["attention"] for a in attentions}
+
+
+##### Scores data structures initialization
+##########################################################################################
+
+# book to float
+def toScore(b):
+    return float(1 if b else 0)
+
+
+# Compute average of a list
+def avg(l):
+    if len(l) == 0:
+        return 0
+    return float(sum(l)) / len(l)
+
+
+def wavg(l, w):
+    if sum(w) == 0:
+        return None
+    return float(sum(l[i] * w[i] for i in range(len(l)))) / sum(w)
+
+
+# Initialize data structure to track all metrics: e.g. accuracy, validity and plausibility, as well as
+# accuracy per question type, length and number of reasoning steps.
+scores = {
+    "accuracy": [],  # list of accuracies per question (1 if correct else 0). Will be averaged ultimately.
+    "binary": [],  # list of accuracies per a binary question (1 if correct else 0). Will be averaged ultimately.
+    "open": [],  # list of accuracies per an open question (1 if correct else 0). Will be averaged ultimately.
+    "validity": [],  # list of validity per question (1 if valid else 0).
+    "plausibility": [],  # list of plausibility per question (1 if plausible else 0).
+    "consistency": [],  # list of consistency scores for entailed questions.
+    "accuracyPerStructuralType": defaultdict(list),
+    # list of question accuracies for each structural type (e.g. compare, logic questions).
+    "accuracyPerSemanticType": defaultdict(list),
+    # list of question accuracies for each semantic type (e.g. questions about an object, an attribute, a relation).
+    "accuracyPerLength": defaultdict(list),  # list of question accuracies per question's word number.
+    "accuracyPerSteps": defaultdict(list),
+    # list of question accuracies per question's reasoning length (steps number).
+    "grounding": []  # list of grounding scores for each question.
+}
+
+# Initialize golden and predicted histograms per each question group. Used to compute the distribution metric.
+dist = {
+    "gold": defaultdict(lambda: defaultdict(int)),
+    "predicted": defaultdict(lambda: defaultdict(int))
+}
+
+
+##### Question lengths - words numbers and reasoning steps number
+##########################################################################################
+
+# Compute question length (words number)
+def getWordsNum(question):
+    return len(question["question"].split())
+
+
+# Compute number of reasoning steps (excluding the final "querying" step which doesn't increase effective reasoning length)
+def getStepsNum(question):
+    return len([c for c in question["semantic"] if not (any([o in "{}: {}".format(c["operation"], c["argument"])
+                                                             for o in ["exist", "query: name", "choose name"]]))])
+
+
+##### Functions for question annotations
+##########################################################################################
+
+# Utility function for converting question annotations string keys to slices
+def toSlice(strSlice):
+    sliceLims = (int(n) for n in strSlice.split(':'))
+    return apply(slice, sliceLims)
+
+
+# Utility function for converting question annotations string keys to indexes list:
+# "1" => [0]
+# "1:3" => [1, 2]
+# "4:9:2" => [4, 6, 8]
+def intsFromSlice(strSlice):
+    slice_obj = get_slice_obj(slicearg)
+    return (range(slice_obj.start or 0, slice_obj.stop or -1, slice_obj.step or 1))
+
+
+##### Functions for validity and plausibility
+##########################################################################################
+
+def belongs(element, group, question):
+    # normalization ()
+    if "Common" in question["types"]["detailed"]:
+        group = ["color", "material", "shape"]
+
+    return element in group
+
+
+##### Functions for consistency scores (for entailed questions ("inferred"))
+##########################################################################################
+
+def updateConsistency(questionId, question, questions):
+    inferredQuestions = [eid for eid in question["entailed"] if eid != questionId]
+
+    if correct and len(inferredQuestions) > 0:
+
+        cosnsitencyScores = []
+        for eid in inferredQuestions:
+            gold = questions[eid]["answer"]
+            predicted = predictions[eid]
+            score = toScore(predicted == gold)
+            cosnsitencyScores.append(score)
+
+        scores["consistency"].append(avg(cosnsitencyScores))
+
+
+##### Functions for grounding score (optional, only for attention models)
+##########################################################################################
+
+# Utility functions for working with bounding boxes.
+# c = (x0, y0, x1, y1), r = (r0, r1)
+
+def yrange(c):
+    return (c[1], c[3])
+
+
+def xrange(c):
+    return (c[0], c[2])
+
+
+def length(r):
+    if r is None:
+        return 0
+    return float(r[1] - r[0])
+
+
+def size(c):
+    return length(xrange(c)) * length(yrange(c))
+
+
+def intersection(r1, r2):
+    ir = (max(r1[0], r2[0]), min(r1[1], r2[1]))
+    if ir[1] > ir[0]:
+        return ir
+    return None
+
+
+def intersectionSize(c1, c2):
+    return length(intersection(xrange(c1), xrange(c2))) * length(intersection(yrange(c1), yrange(c2)))
+
+
+def intersectionRate(c1, c2):
+    return float(intersectionSize(c1, c2)) / size(c1)
+
+
+# Get spatial cell
+def getCell(i, j):
+    edge = float(1) / args.mapSize
+    return (edge * i, edge * j, edge * (i + 1), edge * (j + 1))
+
+
+# Get bounding box of objectId in sceneGraph
+def getRegion(sceneGraph, objectId):
+    obj = sceneGraph["objects"][objectId]
+    x0 = float(obj["x"]) / sceneGraph["width"]
+    y0 = float(obj["y"]) / sceneGraph["height"]
+    x1 = float(obj["x"] + obj["w"]) / sceneGraph["width"]
+    y1 = float(obj["y"] + obj["h"]) / sceneGraph["height"]
+    return (x0, y0, x1, y1)
+
+
+# Compute grounding score. Computer amount of attention (probability) given to each of the regions
+# the question and answers refer to.
+def computeGroundingScore(question, sceneGraph, attentionMap):
+    ## prepare gold regions
+    regions = []
+    # add question regions
+    regions += [getRegion(sceneGraph, pointer) for pointer in question["annotations"]["question"].values()]
+    # add answer regions
+    regions += [getRegion(sceneGraph, pointer) for pointer in question["annotations"]["fullAnswer"].values()]
+    # add all the image if the question refers to the whole scene
+    if any(("scene" in c) for c in question["semantic"]):
+        regions.append((0, 0, 1, 1))
+
+    # prepare attention map
+    if args.objectFeatures:
+        cells = [((x0, y0, x1, y1), attention) for x0, y0, x1, y1, attention in cells]
+    else:
+        cells = [(getCell(i, j), attentionMap[i][j]) for i in range(args.mapSize) for j in range(args.mapSize)]
+
+    # compare attention map to gold regions
+    scores = []
+    for region in regions:
+        for cell, attention in cells:
+            scores.append(attention * intersectionRate(cell, region))
+    return sum(scores)
+
+
+##### Functions for distribution score
+##########################################################################################
+
+# Compute chi square statistic of gold distribution vs predicted distribution,
+# averaged over all question groups
+def chiSquare(goldDist, predictedDist):
+    sumScore, sumOverall = 0, 0
+
+    for group in goldDist:
+        score, overall = 0, 0
+
+        for ans in goldDist[group]:
+            e = goldDist[group][ans]
+            o = predictedDist[group].get(ans, 0)
+            score += ((float(o - e) ** 2) / e)
+            overall += goldDist[group][ans]
+
+        sumScore += score * overall
+        sumOverall += overall
+
+    avgScore = float(sumScore) / sumOverall
+
+    return avgScore
+
+
+##### Main score computation
+##########################################################################################
+
+# Loop over the questions and compute mterics
+for qid, question in tqdm(questions.items()):
+
+    # Compute scores over the balanced dataset (more robust against cheating by making educated guesses)
+    if question["isBalanced"]:
+        gold = question["answer"]
+        predicted = predictions[qid]
+
+        correct = (predicted == gold)
+        score = toScore(correct)
+
+        wordsNum = getWordsNum(question)
+        stepsNum = getStepsNum(question)
+
+        # Update accuracy
+        scores["accuracy"].append(score)
+        scores["accuracyPerLength"][wordsNum].append(score)
+        scores["accuracyPerSteps"][stepsNum].append(score)
+        scores["accuracyPerStructuralType"][question["types"]["structural"]].append(score)
+        scores["accuracyPerSemanticType"][question["types"]["semantic"]].append(score)
+        answerType = "open" if question["types"]["structural"] == "query" else "binary"
+        scores[answerType].append(score)
+
+        # Update validity score
+        valid = (
+            belongs(predicted, choices[qid]["valid"], question) if choices
+            else False)
+        scores["validity"].append(toScore(valid))
+
+        # Update plausibility score
+        plausible = (
+            belongs(predicted, choices[qid]["plausible"], question) if choices
+            else False)
+        scores["plausibility"].append(toScore(plausible))
+
+        # Optionally compute grounding (attention) score
+        if attentions is not None:
+            groundingScore = computeGroundingScore(question, scenes[question["imageId"]], attentions[qid])
+            if groundingScore is not None:
+                scores["grounding"].append(groundingScore)
+
+        # Update histograms for gold and predicted answers
+        globalGroup = question["groups"]["global"]
+        if globalGroup is not None:
+            dist["gold"][globalGroup][gold] += 1
+            dist["predicted"][globalGroup][predicted] += 1
+
+        if args.consistency:
+            # Compute consistency (for entailed questions)
+            updateConsistency(qid, question, questions)
+
+# Compute distribution score
+scores["distribution"] = chiSquare(dist["gold"], dist["predicted"]) / 100
+
+# Average scores over all questions (in the balanced dataset) and print scores
+
+metrics = [
+    "binary",
+    "open",
+    "accuracy",
+    "consistency",
+    "validity",
+    "plausibility",
+    "grounding",
+    "distribution"
+]
+
+detailedMetrics = [
+    ("accuracyPerStructuralType", "Accuracy / structural type"),
+    ("accuracyPerSemanticType", "Accuracy / semantic type"),
+    ("accuracyPerSteps", "Accuracy / steps number"),
+    ("accuracyPerLength", "Accuracy / words number")
+]
+
+subMetrics = {
+    "attr": "attribute",
+    "cat": "category",
+    "global": "scene",
+    "obj": "object",
+    "rel": "relation"
+}
+# average
+for k in metrics:
+    if isinstance(scores[k], list):
+        scores[k] = avg(scores[k]) * 100
+
+for k, _ in detailedMetrics:
+    for t in scores[k]:
+        scores[k][t] = avg(scores[k][t]) * 100, len(scores[k][t])
+
+# print
+print("")
+for m in metrics:
+    # skip grounding and consistency scores if not requested
+    if m == "grounding" and not args.grounding:
+        continue
+    if m == "consistency" and not args.consistency:
+        continue
+
+    # print score
+    print("{title}: {score:.2f}{suffix}".format(title=m.capitalize(), score=scores[m],
+                                                suffix=" (lower is better)" if m == "distribution" else "%"))
+
+for m, mPrintName in detailedMetrics:
+    print("")
+    # print metric title
+    print("{}:".format(mPrintName))
+
+    for t in sorted(list(scores[m].keys())):
+        # set sub-metric title
+        tName = t
+        if isinstance(scores[k], list):
+            tName = subMetrics.get(t, t).capitalize()
+
+        # print score
+        print("  {title}: {score:.2f}{suffix} ({amount} questions)".format(title=tName,
+                                                                           score=scores[m][t][0], suffix="%",
+                                                                           amount=scores[m][t][1]))
\ No newline at end of file
diff --git a/llava/eval/eval_pope.py b/llava/eval/eval_pope.py
new file mode 100644
index 0000000..b115b8f
--- /dev/null
+++ b/llava/eval/eval_pope.py
@@ -0,0 +1,81 @@
+import os
+import json
+import argparse
+
+def eval_pope(answers, label_file):
+    label_list = [json.loads(q)['label'] for q in open(label_file, 'r')]
+
+    for answer in answers:
+        text = answer['text']
+
+        # Only keep the first sentence
+        if text.find('.') != -1:
+            text = text.split('.')[0]
+
+        text = text.replace(',', '')
+        words = text.split(' ')
+        if 'No' in words or 'not' in words or 'no' in words:
+            answer['text'] = 'no'
+        else:
+            answer['text'] = 'yes'
+
+    for i in range(len(label_list)):
+        if label_list[i] == 'no':
+            label_list[i] = 0
+        else:
+            label_list[i] = 1
+
+    pred_list = []
+    for answer in answers:
+        if answer['text'] == 'no':
+            pred_list.append(0)
+        else:
+            pred_list.append(1)
+
+    pos = 1
+    neg = 0
+    yes_ratio = pred_list.count(1) / len(pred_list)
+
+    TP, TN, FP, FN = 0, 0, 0, 0
+    for pred, label in zip(pred_list, label_list):
+        if pred == pos and label == pos:
+            TP += 1
+        elif pred == pos and label == neg:
+            FP += 1
+        elif pred == neg and label == neg:
+            TN += 1
+        elif pred == neg and label == pos:
+            FN += 1
+
+    print('TP\tFP\tTN\tFN\t')
+    print('{}\t{}\t{}\t{}'.format(TP, FP, TN, FN))
+
+    precision = float(TP) / float(TP + FP)
+    recall = float(TP) / float(TP + FN)
+    f1 = 2*precision*recall / (precision + recall)
+    acc = (TP + TN) / (TP + TN + FP + FN)
+    print('Accuracy: {}'.format(acc))
+    print('Precision: {}'.format(precision))
+    print('Recall: {}'.format(recall))
+    print('F1 score: {}'.format(f1))
+    print('Yes ratio: {}'.format(yes_ratio))
+    print('%.3f, %.3f, %.3f, %.3f, %.3f' % (f1, acc, precision, recall, yes_ratio) )
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--annotation-dir", type=str)
+    parser.add_argument("--question-file", type=str)
+    parser.add_argument("--result-file", type=str)
+    args = parser.parse_args()
+
+    questions = [json.loads(line) for line in open(args.question_file)]
+    questions = {question['question_id']: question for question in questions}
+    answers = [json.loads(q) for q in open(args.result_file)]
+    for file in os.listdir(args.annotation_dir):
+        assert file.startswith('coco_pope_')
+        assert file.endswith('.json')
+        category = file[10:-5]
+        cur_answers = [x for x in answers if questions[x['question_id']]['category'] == category]
+        print('Category: {}, # samples: {}'.format(category, len(cur_answers)))
+        eval_pope(cur_answers, os.path.join(args.annotation_dir, file))
+        print("====================================")
diff --git a/llava/eval/eval_science_qa.py b/llava/eval/eval_science_qa.py
new file mode 100644
index 0000000..ccf206b
--- /dev/null
+++ b/llava/eval/eval_science_qa.py
@@ -0,0 +1,114 @@
+import argparse
+import json
+import os
+import re
+import random
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--base-dir', type=str)
+    parser.add_argument('--result-file', type=str)
+    parser.add_argument('--output-file', type=str)
+    parser.add_argument('--output-result', type=str)
+    parser.add_argument('--split', type=str, default='test')
+    parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"])
+    return parser.parse_args()
+
+
+def convert_caps(results):
+    fakecaps = []
+    for result in results:
+        image_id = result['question_id']
+        caption = result['text']
+        fakecaps.append({"image_id": int(image_id), "caption": caption})
+    return fakecaps
+
+
+def get_pred_idx(prediction, choices, options):
+    """
+    Get the index (e.g. 2) from the prediction (e.g. 'C')
+    """
+    if prediction in options[:len(choices)]:
+        return options.index(prediction)
+    else:
+        return -1
+        return random.choice(range(len(choices)))
+
+
+if __name__ == "__main__":
+    args = get_args()
+
+    base_dir = args.base_dir
+    split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split]
+    problems = json.load(open(os.path.join(base_dir, "problems.json")))
+    predictions = [json.loads(line) for line in open(args.result_file)]
+    predictions = {pred['question_id']: pred for pred in predictions}
+    split_problems = {idx: problems[idx] for idx in split_indices}
+
+    results = {'correct': [], 'incorrect': []}
+    sqa_results = {}
+    sqa_results['acc'] = None
+    sqa_results['correct'] = None
+    sqa_results['count'] = None
+    sqa_results['results'] = {}
+    sqa_results['outputs'] = {}
+
+    for prob_id, prob in split_problems.items():
+        if prob_id not in predictions:
+            pred = {'text': 'FAILED', 'prompt': 'Unknown'}
+            pred_text = 'FAILED'
+        else:
+            pred = predictions[prob_id]
+            pred_text = pred['text']
+
+        if pred_text in args.options:
+            answer = pred_text
+        elif len(pred_text) >= 3 and pred_text[0] in args.options and pred_text[1:3] == ". ":
+            answer = pred_text[0]
+        else:
+            pattern = re.compile(r'The answer is ([A-Z]).')
+            res = pattern.findall(pred_text)
+            if len(res) == 1:
+                answer = res[0]  # 'A', 'B', ...
+            else:
+                answer = "FAILED"
+
+        pred_idx = get_pred_idx(answer, prob['choices'], args.options)
+
+        analysis = {
+            'question_id': prob_id,
+            'parsed_ans': answer,
+            'ground_truth': args.options[prob['answer']],
+            'question': pred['prompt'],
+            'pred': pred_text,
+            'is_multimodal': '<image>' in pred['prompt'],
+        }
+
+        sqa_results['results'][prob_id] = get_pred_idx(answer, prob['choices'], args.options)
+        sqa_results['outputs'][prob_id] = pred_text
+
+        if pred_idx == prob['answer']:
+            results['correct'].append(analysis)
+        else:
+            results['incorrect'].append(analysis)
+
+    correct = len(results['correct'])
+    total = len(results['correct']) + len(results['incorrect'])
+
+    ###### IMG ######
+    multimodal_correct = len([x for x in results['correct'] if x['is_multimodal']])
+    multimodal_incorrect = len([x for x in results['incorrect'] if x['is_multimodal']])
+    multimodal_total = multimodal_correct + multimodal_incorrect
+    ###### IMG ######
+
+    print(f'Total: {total}, Correct: {correct}, Accuracy: {correct / total * 100:.2f}%, IMG-Accuracy: {multimodal_correct / multimodal_total * 100:.2f}%')
+
+    sqa_results['acc'] = correct / total * 100
+    sqa_results['correct'] = correct
+    sqa_results['count'] = total
+
+    with open(args.output_file, 'w') as f:
+        json.dump(results, f, indent=2)
+    with open(args.output_result, 'w') as f:
+        json.dump(sqa_results, f, indent=2)
diff --git a/llava/eval/eval_science_qa_gpt4.py b/llava/eval/eval_science_qa_gpt4.py
new file mode 100644
index 0000000..c2ff17c
--- /dev/null
+++ b/llava/eval/eval_science_qa_gpt4.py
@@ -0,0 +1,104 @@
+import argparse
+import json
+import os
+import re
+import random
+from collections import defaultdict
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--base-dir', type=str)
+    parser.add_argument('--gpt4-result', type=str)
+    parser.add_argument('--our-result', type=str)
+    parser.add_argument('--split', type=str, default='test')
+    parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"])
+    return parser.parse_args()
+
+
+def convert_caps(results):
+    fakecaps = []
+    for result in results:
+        image_id = result['question_id']
+        caption = result['text']
+        fakecaps.append({"image_id": int(image_id), "caption": caption})
+    return fakecaps
+
+
+def get_pred_idx(prediction, choices, options):
+    """
+    Get the index (e.g. 2) from the prediction (e.g. 'C')
+    """
+    if prediction in options[:len(choices)]:
+        return options.index(prediction)
+    else:
+        return random.choice(range(len(choices)))
+
+
+if __name__ == "__main__":
+    args = get_args()
+
+    base_dir = args.base_dir
+    split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split]
+    problems = json.load(open(os.path.join(base_dir, "problems.json")))
+    our_predictions = [json.loads(line) for line in open(args.our_result)]
+    our_predictions = {pred['question_id']: pred for pred in our_predictions}
+    split_problems = {idx: problems[idx] for idx in split_indices}
+
+    gpt4_predictions = json.load(open(args.gpt4_result))['outputs']
+
+    results = defaultdict(lambda: 0)
+
+    for prob_id, prob in split_problems.items():
+        if prob_id not in our_predictions:
+            continue
+        if prob_id not in gpt4_predictions:
+            continue
+        our_pred = our_predictions[prob_id]['text']
+        gpt4_pred = gpt4_predictions[prob_id]
+
+        pattern = re.compile(r'The answer is ([A-Z]).')
+        our_res = pattern.findall(our_pred)
+        if len(our_res) == 1:
+            our_answer = our_res[0]  # 'A', 'B', ...
+        else:
+            our_answer = "FAILED"
+        gpt4_res = pattern.findall(gpt4_pred)
+        if len(gpt4_res) == 1:
+            gpt4_answer = gpt4_res[0]  # 'A', 'B', ...
+        else:
+            gpt4_answer = "FAILED"
+
+        our_pred_idx = get_pred_idx(our_answer, prob['choices'], args.options)
+        gpt4_pred_idx = get_pred_idx(gpt4_answer, prob['choices'], args.options)
+
+        if gpt4_answer == 'FAILED':
+            results['gpt4_failed'] += 1
+            # continue
+            gpt4_pred_idx = our_pred_idx
+            # if our_pred_idx != prob['answer']:
+            #     print(our_predictions[prob_id]['prompt'])
+            #     print('-----------------')
+            #     print(f'LECTURE: {prob["lecture"]}')
+            #     print(f'SOLUTION: {prob["solution"]}')
+            #     print('=====================')
+        else:
+            # continue
+            pass
+        # gpt4_pred_idx = our_pred_idx
+
+        if gpt4_pred_idx == prob['answer']:
+            results['correct'] += 1
+        else:
+            results['incorrect'] += 1
+
+
+        if gpt4_pred_idx == prob['answer'] or our_pred_idx == prob['answer']:
+            results['correct_upperbound'] += 1
+
+    correct = results['correct']
+    total = results['correct'] + results['incorrect']
+    print(f'Total: {total}, Correct: {correct}, Accuracy: {correct / total * 100:.2f}%')
+    print(f'Total: {total}, Correct (upper): {results["correct_upperbound"]}, Accuracy: {results["correct_upperbound"] / total * 100:.2f}%')
+    print(f'Total: {total}, GPT-4 NO-ANS (RANDOM): {results["gpt4_failed"]}, Percentage: {results["gpt4_failed"] / total * 100:.2f}%')
+
diff --git a/llava/eval/eval_science_qa_gpt4_requery.py b/llava/eval/eval_science_qa_gpt4_requery.py
new file mode 100644
index 0000000..698546e
--- /dev/null
+++ b/llava/eval/eval_science_qa_gpt4_requery.py
@@ -0,0 +1,149 @@
+import argparse
+import json
+import os
+import re
+import random
+from collections import defaultdict
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--base-dir', type=str)
+    parser.add_argument('--gpt4-result', type=str)
+    parser.add_argument('--requery-result', type=str)
+    parser.add_argument('--our-result', type=str)
+    parser.add_argument('--output-result', type=str)
+    parser.add_argument('--split', type=str, default='test')
+    parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"])
+    return parser.parse_args()
+
+
+def convert_caps(results):
+    fakecaps = []
+    for result in results:
+        image_id = result['question_id']
+        caption = result['text']
+        fakecaps.append({"image_id": int(image_id), "caption": caption})
+    return fakecaps
+
+
+def get_pred_idx(prediction, choices, options):
+    """
+    Get the index (e.g. 2) from the prediction (e.g. 'C')
+    """
+    if prediction in options[:len(choices)]:
+        return options.index(prediction)
+    else:
+        return random.choice(range(len(choices)))
+
+
+if __name__ == "__main__":
+    args = get_args()
+
+    base_dir = args.base_dir
+    split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split]
+    problems = json.load(open(os.path.join(base_dir, "problems.json")))
+    our_predictions = [json.loads(line) for line in open(args.our_result)]
+    our_predictions = {pred['question_id']: pred for pred in our_predictions}
+    split_problems = {idx: problems[idx] for idx in split_indices}
+
+    requery_predictions = [json.loads(line) for line in open(args.requery_result)]
+    requery_predictions = {pred['question_id']: pred for pred in requery_predictions}
+
+    gpt4_predictions = json.load(open(args.gpt4_result))['outputs']
+
+    results = defaultdict(lambda: 0)
+
+    sqa_results = {}
+    sqa_results['acc'] = None
+    sqa_results['correct'] = None
+    sqa_results['count'] = None
+    sqa_results['results'] = {}
+    sqa_results['outputs'] = {}
+
+    for prob_id, prob in split_problems.items():
+        if prob_id not in our_predictions:
+            assert False
+        if prob_id not in gpt4_predictions:
+            assert False
+        our_pred = our_predictions[prob_id]['text']
+        gpt4_pred = gpt4_predictions[prob_id]
+        if prob_id not in requery_predictions:
+            results['missing_requery'] += 1
+            requery_pred = "MISSING"
+        else:
+            requery_pred = requery_predictions[prob_id]['text']
+
+        pattern = re.compile(r'The answer is ([A-Z]).')
+        our_res = pattern.findall(our_pred)
+        if len(our_res) == 1:
+            our_answer = our_res[0]  # 'A', 'B', ...
+        else:
+            our_answer = "FAILED"
+
+        requery_res = pattern.findall(requery_pred)
+        if len(requery_res) == 1:
+            requery_answer = requery_res[0]  # 'A', 'B', ...
+        else:
+            requery_answer = "FAILED"
+
+        gpt4_res = pattern.findall(gpt4_pred)
+        if len(gpt4_res) == 1:
+            gpt4_answer = gpt4_res[0]  # 'A', 'B', ...
+        else:
+            gpt4_answer = "FAILED"
+
+        our_pred_idx = get_pred_idx(our_answer, prob['choices'], args.options)
+        gpt4_pred_idx = get_pred_idx(gpt4_answer, prob['choices'], args.options)
+        requery_pred_idx = get_pred_idx(requery_answer, prob['choices'], args.options)
+
+        results['total'] += 1
+
+        if gpt4_answer == 'FAILED':
+            results['gpt4_failed'] += 1
+            if gpt4_pred_idx == prob['answer']:
+                results['gpt4_correct'] += 1
+            if our_pred_idx == prob['answer']:
+                results['gpt4_ourvisual_correct'] += 1
+        elif gpt4_pred_idx == prob['answer']:
+            results['gpt4_correct'] += 1
+            results['gpt4_ourvisual_correct'] += 1
+
+        if our_pred_idx == prob['answer']:
+            results['our_correct'] += 1
+
+        if requery_answer == 'FAILED':
+            sqa_results['results'][prob_id] = our_pred_idx
+            if our_pred_idx == prob['answer']:
+                results['requery_correct'] += 1
+        else:
+            sqa_results['results'][prob_id] = requery_pred_idx
+            if requery_pred_idx == prob['answer']:
+                results['requery_correct'] += 1
+            else:
+                print(f"""
+Question ({args.options[prob['answer']]}): {our_predictions[prob_id]['prompt']}
+Our ({our_answer}): {our_pred}
+GPT-4 ({gpt4_answer}): {gpt4_pred}
+Requery ({requery_answer}): {requery_pred}
+print("=====================================")
+""")
+
+        if gpt4_pred_idx == prob['answer'] or our_pred_idx == prob['answer']:
+            results['correct_upperbound'] += 1
+
+    total = results['total']
+    print(f'Total: {total}, Our-Correct: {results["our_correct"]}, Accuracy: {results["our_correct"] / total * 100:.2f}%')
+    print(f'Total: {total}, GPT-4-Correct: {results["gpt4_correct"]}, Accuracy: {results["gpt4_correct"] / total * 100:.2f}%')
+    print(f'Total: {total}, GPT-4 NO-ANS (RANDOM): {results["gpt4_failed"]}, Percentage: {results["gpt4_failed"] / total * 100:.2f}%')
+    print(f'Total: {total}, GPT-4-OursVisual-Correct: {results["gpt4_ourvisual_correct"]}, Accuracy: {results["gpt4_ourvisual_correct"] / total * 100:.2f}%')
+    print(f'Total: {total}, Requery-Correct: {results["requery_correct"]}, Accuracy: {results["requery_correct"] / total * 100:.2f}%')
+    print(f'Total: {total}, Correct upper: {results["correct_upperbound"]}, Accuracy: {results["correct_upperbound"] / total * 100:.2f}%')
+
+    sqa_results['acc'] = results["requery_correct"] / total * 100
+    sqa_results['correct'] = results["requery_correct"]
+    sqa_results['count'] = total
+
+    with open(args.output_result, 'w') as f:
+        json.dump(sqa_results, f, indent=2)
+
diff --git a/llava/eval/eval_textvqa.py b/llava/eval/eval_textvqa.py
new file mode 100644
index 0000000..468f4bb
--- /dev/null
+++ b/llava/eval/eval_textvqa.py
@@ -0,0 +1,65 @@
+import os
+import argparse
+import json
+import re
+
+from llava.eval.m4c_evaluator import TextVQAAccuracyEvaluator
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--annotation-file', type=str)
+    parser.add_argument('--result-file', type=str)
+    parser.add_argument('--result-dir', type=str)
+    return parser.parse_args()
+
+
+def prompt_processor(prompt):
+    if prompt.startswith('OCR tokens: '):
+        pattern = r"Question: (.*?) Short answer:"
+        match = re.search(pattern, prompt, re.DOTALL)
+        question = match.group(1)
+    elif 'Reference OCR token: ' in prompt and len(prompt.split('\n')) == 3:
+        if prompt.startswith('Reference OCR token:'):
+            question = prompt.split('\n')[1]
+        else:
+            question = prompt.split('\n')[0]
+    elif len(prompt.split('\n')) == 2:
+        question = prompt.split('\n')[0]
+    else:
+        assert False
+
+    return question.lower()
+
+
+def eval_single(annotation_file, result_file):
+    experiment_name = os.path.splitext(os.path.basename(result_file))[0]
+    print(experiment_name)
+    annotations = json.load(open(annotation_file))['data']
+    annotations = {(annotation['image_id'], annotation['question'].lower()): annotation for annotation in annotations}
+    results = [json.loads(line) for line in open(result_file)]
+
+    pred_list = []
+    for result in results:
+        annotation = annotations[(result['question_id'], prompt_processor(result['prompt']))]
+        pred_list.append({
+            "pred_answer": result['text'],
+            "gt_answers": annotation['answers'],
+        })
+
+    evaluator = TextVQAAccuracyEvaluator()
+    print('Samples: {}\nAccuracy: {:.2f}%\n'.format(len(pred_list), 100. * evaluator.eval_pred_list(pred_list)))
+
+
+if __name__ == "__main__":
+    args = get_args()
+
+    if args.result_file is not None:
+        eval_single(args.annotation_file, args.result_file)
+
+    if args.result_dir is not None:
+        for result_file in sorted(os.listdir(args.result_dir)):
+            if not result_file.endswith('.jsonl'):
+                print(f'Skipping {result_file}')
+                continue
+            eval_single(args.annotation_file, os.path.join(args.result_dir, result_file))
diff --git a/llava/eval/generate_webpage_data_from_table.py b/llava/eval/generate_webpage_data_from_table.py
new file mode 100644
index 0000000..9260225
--- /dev/null
+++ b/llava/eval/generate_webpage_data_from_table.py
@@ -0,0 +1,111 @@
+"""Generate json file for webpage."""
+import json
+import os
+import re
+
+# models = ['llama', 'alpaca', 'gpt35', 'bard']
+models = ['vicuna']
+
+
+def read_jsonl(path: str, key: str=None):
+    data = []
+    with open(os.path.expanduser(path)) as f:
+        for line in f:
+            if not line:
+                continue
+            data.append(json.loads(line))
+    if key is not None:
+        data.sort(key=lambda x: x[key])
+        data = {item[key]: item for item in data}
+    return data
+
+
+def trim_hanging_lines(s: str, n: int) -> str:
+    s = s.strip()
+    for _ in range(n):
+        s = s.split('\n', 1)[1].strip()
+    return s
+
+
+if __name__ == '__main__':
+    questions = read_jsonl('table/question.jsonl', key='question_id')
+
+    # alpaca_answers = read_jsonl('table/answer/answer_alpaca-13b.jsonl', key='question_id')
+    # bard_answers = read_jsonl('table/answer/answer_bard.jsonl', key='question_id')
+    # gpt35_answers = read_jsonl('table/answer/answer_gpt35.jsonl', key='question_id')
+    # llama_answers = read_jsonl('table/answer/answer_llama-13b.jsonl', key='question_id')
+    vicuna_answers = read_jsonl('table/answer/answer_vicuna-13b.jsonl', key='question_id')
+    ours_answers = read_jsonl('table/results/llama-13b-hf-alpaca.jsonl', key='question_id')
+
+    review_vicuna = read_jsonl('table/review/review_vicuna-13b_llama-13b-hf-alpaca.jsonl', key='question_id')
+    # review_alpaca = read_jsonl('table/review/review_alpaca-13b_vicuna-13b.jsonl', key='question_id')
+    # review_bard = read_jsonl('table/review/review_bard_vicuna-13b.jsonl', key='question_id')
+    # review_gpt35 = read_jsonl('table/review/review_gpt35_vicuna-13b.jsonl', key='question_id')
+    # review_llama = read_jsonl('table/review/review_llama-13b_vicuna-13b.jsonl', key='question_id')
+
+    records = []
+    for qid in questions.keys():
+        r = {
+            'id': qid,
+            'category': questions[qid]['category'],
+            'question': questions[qid]['text'],
+            'answers': {
+                # 'alpaca': alpaca_answers[qid]['text'],
+                # 'llama': llama_answers[qid]['text'],
+                # 'bard': bard_answers[qid]['text'],
+                # 'gpt35': gpt35_answers[qid]['text'],
+                'vicuna': vicuna_answers[qid]['text'],
+                'ours': ours_answers[qid]['text'],
+            },
+            'evaluations': {
+                # 'alpaca': review_alpaca[qid]['text'],
+                # 'llama': review_llama[qid]['text'],
+                # 'bard': review_bard[qid]['text'],
+                'vicuna': review_vicuna[qid]['content'],
+                # 'gpt35': review_gpt35[qid]['text'],
+            },
+            'scores': {
+                'vicuna': review_vicuna[qid]['tuple'],
+                # 'alpaca': review_alpaca[qid]['score'],
+                # 'llama': review_llama[qid]['score'],
+                # 'bard': review_bard[qid]['score'],
+                # 'gpt35': review_gpt35[qid]['score'],
+            },
+        }
+
+        # cleanup data
+        cleaned_evals = {}
+        for k, v in r['evaluations'].items():
+            v = v.strip()
+            lines = v.split('\n')
+            # trim the first line if it's a pair of numbers
+            if re.match(r'\d+[, ]+\d+', lines[0]):
+                lines = lines[1:]
+            v = '\n'.join(lines)
+            cleaned_evals[k] = v.replace('Assistant 1', "**Assistant 1**").replace('Assistant 2', '**Assistant 2**')
+
+        r['evaluations'] = cleaned_evals
+        records.append(r)
+
+    # Reorder the records, this is optional
+    for r in records:
+        if r['id'] <= 20:
+            r['id'] += 60
+        else:
+            r['id'] -= 20
+    for r in records:
+        if r['id'] <= 50:
+            r['id'] += 10
+        elif 50 < r['id'] <= 60:
+            r['id'] -= 50
+    for r in records:
+        if r['id'] == 7:
+            r['id'] = 1
+        elif r['id'] < 7:
+            r['id'] += 1 
+
+    records.sort(key=lambda x: x['id'])
+
+    # Write to file
+    with open('webpage/data.json', 'w') as f:
+        json.dump({'questions': records, 'models': models}, f, indent=2)
diff --git a/llava/eval/m4c_evaluator.py b/llava/eval/m4c_evaluator.py
new file mode 100644
index 0000000..e30e958
--- /dev/null
+++ b/llava/eval/m4c_evaluator.py
@@ -0,0 +1,334 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import re
+
+from tqdm import tqdm
+
+
+class EvalAIAnswerProcessor:
+    """
+    Processes an answer similar to Eval AI
+        copied from
+        https://github.com/facebookresearch/mmf/blob/c46b3b3391275b4181567db80943473a89ab98ab/pythia/tasks/processors.py#L897
+    """
+
+    CONTRACTIONS = {
+        "aint": "ain't",
+        "arent": "aren't",
+        "cant": "can't",
+        "couldve": "could've",
+        "couldnt": "couldn't",
+        "couldn'tve": "couldn't've",
+        "couldnt've": "couldn't've",
+        "didnt": "didn't",
+        "doesnt": "doesn't",
+        "dont": "don't",
+        "hadnt": "hadn't",
+        "hadnt've": "hadn't've",
+        "hadn'tve": "hadn't've",
+        "hasnt": "hasn't",
+        "havent": "haven't",
+        "hed": "he'd",
+        "hed've": "he'd've",
+        "he'dve": "he'd've",
+        "hes": "he's",
+        "howd": "how'd",
+        "howll": "how'll",
+        "hows": "how's",
+        "Id've": "I'd've",
+        "I'dve": "I'd've",
+        "Im": "I'm",
+        "Ive": "I've",
+        "isnt": "isn't",
+        "itd": "it'd",
+        "itd've": "it'd've",
+        "it'dve": "it'd've",
+        "itll": "it'll",
+        "let's": "let's",
+        "maam": "ma'am",
+        "mightnt": "mightn't",
+        "mightnt've": "mightn't've",
+        "mightn'tve": "mightn't've",
+        "mightve": "might've",
+        "mustnt": "mustn't",
+        "mustve": "must've",
+        "neednt": "needn't",
+        "notve": "not've",
+        "oclock": "o'clock",
+        "oughtnt": "oughtn't",
+        "ow's'at": "'ow's'at",
+        "'ows'at": "'ow's'at",
+        "'ow'sat": "'ow's'at",
+        "shant": "shan't",
+        "shed've": "she'd've",
+        "she'dve": "she'd've",
+        "she's": "she's",
+        "shouldve": "should've",
+        "shouldnt": "shouldn't",
+        "shouldnt've": "shouldn't've",
+        "shouldn'tve": "shouldn't've",
+        "somebody'd": "somebodyd",
+        "somebodyd've": "somebody'd've",
+        "somebody'dve": "somebody'd've",
+        "somebodyll": "somebody'll",
+        "somebodys": "somebody's",
+        "someoned": "someone'd",
+        "someoned've": "someone'd've",
+        "someone'dve": "someone'd've",
+        "someonell": "someone'll",
+        "someones": "someone's",
+        "somethingd": "something'd",
+        "somethingd've": "something'd've",
+        "something'dve": "something'd've",
+        "somethingll": "something'll",
+        "thats": "that's",
+        "thered": "there'd",
+        "thered've": "there'd've",
+        "there'dve": "there'd've",
+        "therere": "there're",
+        "theres": "there's",
+        "theyd": "they'd",
+        "theyd've": "they'd've",
+        "they'dve": "they'd've",
+        "theyll": "they'll",
+        "theyre": "they're",
+        "theyve": "they've",
+        "twas": "'twas",
+        "wasnt": "wasn't",
+        "wed've": "we'd've",
+        "we'dve": "we'd've",
+        "weve": "we've",
+        "werent": "weren't",
+        "whatll": "what'll",
+        "whatre": "what're",
+        "whats": "what's",
+        "whatve": "what've",
+        "whens": "when's",
+        "whered": "where'd",
+        "wheres": "where's",
+        "whereve": "where've",
+        "whod": "who'd",
+        "whod've": "who'd've",
+        "who'dve": "who'd've",
+        "wholl": "who'll",
+        "whos": "who's",
+        "whove": "who've",
+        "whyll": "why'll",
+        "whyre": "why're",
+        "whys": "why's",
+        "wont": "won't",
+        "wouldve": "would've",
+        "wouldnt": "wouldn't",
+        "wouldnt've": "wouldn't've",
+        "wouldn'tve": "wouldn't've",
+        "yall": "y'all",
+        "yall'll": "y'all'll",
+        "y'allll": "y'all'll",
+        "yall'd've": "y'all'd've",
+        "y'alld've": "y'all'd've",
+        "y'all'dve": "y'all'd've",
+        "youd": "you'd",
+        "youd've": "you'd've",
+        "you'dve": "you'd've",
+        "youll": "you'll",
+        "youre": "you're",
+        "youve": "you've",
+    }
+
+    NUMBER_MAP = {
+        "none": "0",
+        "zero": "0",
+        "one": "1",
+        "two": "2",
+        "three": "3",
+        "four": "4",
+        "five": "5",
+        "six": "6",
+        "seven": "7",
+        "eight": "8",
+        "nine": "9",
+        "ten": "10",
+    }
+    ARTICLES = ["a", "an", "the"]
+    PERIOD_STRIP = re.compile(r"(?!<=\d)(\.)(?!\d)")
+    COMMA_STRIP = re.compile(r"(?<=\d)(\,)+(?=\d)")
+    PUNCTUATIONS = [
+        ";",
+        r"/",
+        "[",
+        "]",
+        '"',
+        "{",
+        "}",
+        "(",
+        ")",
+        "=",
+        "+",
+        "\\",
+        "_",
+        "-",
+        ">",
+        "<",
+        "@",
+        "`",
+        ",",
+        "?",
+        "!",
+    ]
+
+    def __init__(self, *args, **kwargs):
+        pass
+
+    def word_tokenize(self, word):
+        word = word.lower()
+        word = word.replace(",", "").replace("?", "").replace("'s", " 's")
+        return word.strip()
+
+    def process_punctuation(self, in_text):
+        out_text = in_text
+        for p in self.PUNCTUATIONS:
+            if (p + " " in in_text or " " + p in in_text) or (
+                re.search(self.COMMA_STRIP, in_text) is not None
+            ):
+                out_text = out_text.replace(p, "")
+            else:
+                out_text = out_text.replace(p, " ")
+        out_text = self.PERIOD_STRIP.sub("", out_text, re.UNICODE)
+        return out_text
+
+    def process_digit_article(self, in_text):
+        out_text = []
+        temp_text = in_text.lower().split()
+        for word in temp_text:
+            word = self.NUMBER_MAP.setdefault(word, word)
+            if word not in self.ARTICLES:
+                out_text.append(word)
+            else:
+                pass
+        for word_id, word in enumerate(out_text):
+            if word in self.CONTRACTIONS:
+                out_text[word_id] = self.CONTRACTIONS[word]
+        out_text = " ".join(out_text)
+        return out_text
+
+    def __call__(self, item):
+        item = self.word_tokenize(item)
+        item = item.replace("\n", " ").replace("\t", " ").strip()
+        item = self.process_punctuation(item)
+        item = self.process_digit_article(item)
+        return item
+
+
+class TextVQAAccuracyEvaluator:
+    def __init__(self):
+        self.answer_processor = EvalAIAnswerProcessor()
+
+    def _compute_answer_scores(self, raw_answers):
+        """
+        compute the accuracy (soft score) of human answers
+        """
+        answers = [self.answer_processor(a) for a in raw_answers]
+        assert len(answers) == 10
+        gt_answers = list(enumerate(answers))
+        unique_answers = set(answers)
+        unique_answer_scores = {}
+
+        for unique_answer in unique_answers:
+            accs = []
+            for gt_answer in gt_answers:
+                other_answers = [item for item in gt_answers if item != gt_answer]
+                matching_answers = [
+                    item for item in other_answers if item[1] == unique_answer
+                ]
+                acc = min(1, float(len(matching_answers)) / 3)
+                accs.append(acc)
+            unique_answer_scores[unique_answer] = sum(accs) / len(accs)
+
+        return unique_answer_scores
+
+    def eval_pred_list(self, pred_list):
+        pred_scores = []
+        for entry in tqdm(pred_list):
+            pred_answer = self.answer_processor(entry["pred_answer"])
+            unique_answer_scores = self._compute_answer_scores(entry["gt_answers"])
+            score = unique_answer_scores.get(pred_answer, 0.0)
+            pred_scores.append(score)
+
+        accuracy = sum(pred_scores) / len(pred_scores)
+        return accuracy
+
+
+class STVQAAccuracyEvaluator:
+    def __init__(self):
+        self.answer_processor = EvalAIAnswerProcessor()
+
+    def eval_pred_list(self, pred_list):
+        pred_scores = []
+        for entry in pred_list:
+            pred_answer = self.answer_processor(entry["pred_answer"])
+            gts = [self.answer_processor(a) for a in entry["gt_answers"]]
+            score = 1.0 if pred_answer in gts else 0.0
+            pred_scores.append(score)
+
+        accuracy = sum(pred_scores) / len(pred_scores)
+        return accuracy
+
+
+class STVQAANLSEvaluator:
+    def __init__(self):
+        import editdistance  # install with `pip install editdistance`
+
+        self.get_edit_distance = editdistance.eval
+
+    def get_anls(self, s1, s2):
+        s1 = s1.lower().strip()
+        s2 = s2.lower().strip()
+        iou = 1 - self.get_edit_distance(s1, s2) / max(len(s1), len(s2))
+        anls = iou if iou >= 0.5 else 0.0
+        return anls
+
+    def eval_pred_list(self, pred_list):
+        pred_scores = []
+        for entry in pred_list:
+            anls = max(
+                self.get_anls(entry["pred_answer"], gt) for gt in entry["gt_answers"]
+            )
+            pred_scores.append(anls)
+
+        accuracy = sum(pred_scores) / len(pred_scores)
+        return accuracy
+
+
+class TextCapsBleu4Evaluator:
+    def __init__(self):
+        # The following script requires Java 1.8.0 and pycocotools installed.
+        # The pycocoevalcap can be installed with pip as
+        # pip install git+https://github.com/ronghanghu/coco-caption.git@python23
+        # Original pycocoevalcap code is at https://github.com/tylin/coco-caption
+        # but has no python3 support yet.
+        try:
+            from pycocoevalcap.bleu.bleu import Bleu
+            from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
+        except ModuleNotFoundError:
+            print(
+                "Please install pycocoevalcap module using "
+                "pip install git+https://github.com/ronghanghu/coco-caption.git@python23"  # noqa
+            )
+            raise
+
+        self.tokenizer = PTBTokenizer()
+        self.scorer = Bleu(4)
+
+    def eval_pred_list(self, pred_list):
+        # Create reference and hypotheses captions.
+        gts = {}
+        res = {}
+        for idx, entry in enumerate(pred_list):
+            gts[idx] = [{"caption": a} for a in entry["gt_answers"]]
+            res[idx] = [{"caption": entry["pred_answer"]}]
+
+        gts = self.tokenizer.tokenize(gts)
+        res = self.tokenizer.tokenize(res)
+        score, _ = self.scorer.compute_score(gts, res)
+
+        bleu4 = score[3]  # score is (Bleu-1, Bleu-2, Bleu-3, Bleu-4)
+        return bleu4
diff --git a/llava/eval/model_qa.py b/llava/eval/model_qa.py
new file mode 100644
index 0000000..6c8c113
--- /dev/null
+++ b/llava/eval/model_qa.py
@@ -0,0 +1,85 @@
+import argparse
+from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria
+import torch
+import os
+import json
+from tqdm import tqdm
+import shortuuid
+
+from llava.conversation import default_conversation
+from llava.utils import disable_torch_init
+
+
+# new stopping implementation
+class KeywordsStoppingCriteria(StoppingCriteria):
+    def __init__(self, keywords, tokenizer, input_ids):
+        self.keywords = keywords
+        self.tokenizer = tokenizer
+        self.start_len = None
+        self.input_ids = input_ids
+
+    def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        if self.start_len is None:
+            self.start_len = self.input_ids.shape[1]
+        else:
+            outputs = self.tokenizer.batch_decode(output_ids[:, self.start_len:], skip_special_tokens=True)[0]
+            for keyword in self.keywords:
+                if keyword in outputs:
+                    return True
+        return False
+
+
+@torch.inference_mode()
+def eval_model(model_name, questions_file, answers_file):
+    # Model
+    disable_torch_init()
+    model_name = os.path.expanduser(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
+    model = AutoModelForCausalLM.from_pretrained(model_name,
+        torch_dtype=torch.float16).cuda()
+
+
+    ques_file = open(os.path.expanduser(questions_file), "r")
+    ans_file = open(os.path.expanduser(answers_file), "w")
+    for i, line in enumerate(tqdm(ques_file)):
+        idx = json.loads(line)["question_id"]
+        qs = json.loads(line)["text"]
+        cat = json.loads(line)["category"]
+        conv = default_conversation.copy()
+        conv.append_message(conv.roles[0], qs)
+        prompt = conv.get_prompt()
+        inputs = tokenizer([prompt])
+        input_ids = torch.as_tensor(inputs.input_ids).cuda()
+        stopping_criteria = KeywordsStoppingCriteria([conv.sep], tokenizer, input_ids)
+        output_ids = model.generate(
+            input_ids,
+            do_sample=True,
+            use_cache=True,
+            temperature=0.7,
+            max_new_tokens=1024,
+            stopping_criteria=[stopping_criteria])
+        outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
+        try:
+            index = outputs.index(conv.sep, len(prompt))
+        except ValueError:
+            outputs += conv.sep
+            index = outputs.index(conv.sep, len(prompt))
+
+        outputs = outputs[len(prompt) + len(conv.roles[1]) + 2:index].strip()
+        ans_id = shortuuid.uuid()
+        ans_file.write(json.dumps({"question_id": idx,
+                                   "text": outputs,
+                                   "answer_id": ans_id,
+                                   "model_id": model_name,
+                                   "metadata": {}}) + "\n")
+        ans_file.flush()
+    ans_file.close()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-name", type=str, default="facebook/opt-350m")
+    parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
+    parser.add_argument("--answers-file", type=str, default="answer.jsonl")
+    args = parser.parse_args()
+
+    eval_model(args.model_name, args.question_file, args.answers_file)
diff --git a/llava/eval/model_vqa.py b/llava/eval/model_vqa.py
new file mode 100644
index 0000000..ff9a122
--- /dev/null
+++ b/llava/eval/model_vqa.py
@@ -0,0 +1,112 @@
+import argparse
+import torch
+import os
+import json
+from tqdm import tqdm
+import shortuuid
+
+from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import tokenizer_X_token, get_model_name_from_path, KeywordsStoppingCriteria
+
+from PIL import Image
+import math
+
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+
+def eval_model(args):
+    # Model
+    disable_torch_init()
+    model_path = os.path.expanduser(args.model_path)
+    model_name = get_model_name_from_path(model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
+
+    questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
+    questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
+    answers_file = os.path.expanduser(args.answers_file)
+    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
+    ans_file = open(answers_file, "w")
+    for line in tqdm(questions):
+        idx = line["question_id"]
+        image_file = line["image"]
+        qs = line["text"]
+        cur_prompt = qs
+        if model.config.mm_use_x_start_end:
+            qs = DEFAULT_X_START_TOKEN['IMAGE'] + DEFAULT_X_TOKEN['IMAGE'] + DEFAULT_X_END_TOKEN['IMAGE'] + '\n' + qs
+        else:
+            qs = DEFAULT_X_TOKEN['IMAGE'] + '\n' + qs
+
+        conv = conv_templates[args.conv_mode].copy()
+        conv.append_message(conv.roles[0], qs)
+        conv.append_message(conv.roles[1], None)
+        prompt = conv.get_prompt()
+
+        input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt').unsqueeze(0).cuda()
+
+        image = Image.open(os.path.join(args.image_folder, image_file)).convert('RGB')
+        image_tensor = processor['image'].preprocess(image, return_tensors='pt')['pixel_values'][0]
+
+        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+        keywords = [stop_str]
+        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+
+        with torch.inference_mode():
+            output_ids = model.generate(
+                input_ids,
+                images=[[image_tensor.half().cuda()], ['image']],
+                do_sample=True if args.temperature > 0 else False,
+                temperature=args.temperature,
+                top_p=args.top_p,
+                num_beams=args.num_beams,
+                # no_repeat_ngram_size=3,
+                max_new_tokens=1024,
+                use_cache=True)
+
+        input_token_len = input_ids.shape[1]
+        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+        if n_diff_input_output > 0:
+            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+        outputs = outputs.strip()
+        if outputs.endswith(stop_str):
+            outputs = outputs[:-len(stop_str)]
+        outputs = outputs.strip()
+
+        ans_id = shortuuid.uuid()
+        ans_file.write(json.dumps({"question_id": idx,
+                                   "prompt": cur_prompt,
+                                   "text": outputs,
+                                   "answer_id": ans_id,
+                                   "model_id": model_name,
+                                   "metadata": {}}) + "\n")
+        ans_file.flush()
+    ans_file.close()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--image-folder", type=str, default="")
+    parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
+    parser.add_argument("--answers-file", type=str, default="answer.jsonl")
+    parser.add_argument("--conv-mode", type=str, default="llava_v1")
+    parser.add_argument("--num-chunks", type=int, default=1)
+    parser.add_argument("--chunk-idx", type=int, default=0)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--top_p", type=float, default=None)
+    parser.add_argument("--num_beams", type=int, default=1)
+    args = parser.parse_args()
+
+    eval_model(args)
diff --git a/llava/eval/model_vqa_loader.py b/llava/eval/model_vqa_loader.py
new file mode 100644
index 0000000..e767097
--- /dev/null
+++ b/llava/eval/model_vqa_loader.py
@@ -0,0 +1,148 @@
+import argparse
+import torch
+import os
+import json
+from tqdm import tqdm
+import shortuuid
+
+from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import tokenizer_X_token, process_images, get_model_name_from_path
+from torch.utils.data import Dataset, DataLoader
+
+from PIL import Image
+import math
+
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+
+# Custom dataset class
+class CustomDataset(Dataset):
+    def __init__(self, questions, image_folder, tokenizer, image_processor, model_config):
+        self.questions = questions
+        self.image_folder = image_folder
+        self.tokenizer = tokenizer
+        self.image_processor = image_processor
+        self.model_config = model_config
+
+    def __getitem__(self, index):
+        line = self.questions[index]
+        image_file = line["image"]
+        qs = line["text"]
+        # if self.model_config.mm_use_im_start_end:
+        #     qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
+        # else:
+        #     qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
+
+        if self.model_config.mm_use_x_start_end:
+            qs = DEFAULT_X_START_TOKEN['IMAGE'] + DEFAULT_X_TOKEN['IMAGE'] + DEFAULT_X_END_TOKEN['IMAGE'] + '\n' + qs
+        else:
+            qs = DEFAULT_X_TOKEN['IMAGE'] + '\n' + qs
+        conv = conv_templates[args.conv_mode].copy()
+        conv.append_message(conv.roles[0], qs)
+        conv.append_message(conv.roles[1], None)
+        prompt = conv.get_prompt()
+
+        image = Image.open(os.path.join(self.image_folder, image_file)).convert('RGB')
+        image_tensor = process_images([image], self.image_processor, self.model_config)[0]
+
+        input_ids = tokenizer_X_token(prompt, self.tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt')
+
+        return input_ids, image_tensor
+
+    def __len__(self):
+        return len(self.questions)
+
+
+# DataLoader
+def create_data_loader(questions, image_folder, tokenizer, image_processor, model_config, batch_size=1, num_workers=4):
+    assert batch_size == 1, "batch_size must be 1"
+    dataset = CustomDataset(questions, image_folder, tokenizer, image_processor, model_config)
+    data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
+    return data_loader
+
+
+def eval_model(args):
+    # Model
+    disable_torch_init()
+    model_path = os.path.expanduser(args.model_path)
+    model_name = get_model_name_from_path(model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
+
+    questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
+    questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
+    answers_file = os.path.expanduser(args.answers_file)
+    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
+    ans_file = open(answers_file, "w")
+
+    if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode:
+        args.conv_mode = args.conv_mode + '_mmtag'
+        print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.')
+
+    data_loader = create_data_loader(questions, args.image_folder, tokenizer, processor['image'], model.config)
+
+    for (input_ids, image_tensor), line in tqdm(zip(data_loader, questions), total=len(questions)):
+        idx = line["question_id"]
+        cur_prompt = line["text"]
+
+        stop_str = conv_templates[args.conv_mode].sep if conv_templates[args.conv_mode].sep_style != SeparatorStyle.TWO else conv_templates[args.conv_mode].sep2
+        input_ids = input_ids.to(device='cuda', non_blocking=True)
+
+        with torch.inference_mode():
+            output_ids = model.generate(
+                input_ids,
+                images=[[image_tensor[0].to(dtype=torch.float16, device='cuda', non_blocking=True)], ['image']],
+                do_sample=True if args.temperature > 0 else False,
+                temperature=args.temperature,
+                top_p=args.top_p,
+                num_beams=args.num_beams,
+                max_new_tokens=128,
+                use_cache=True)
+
+        input_token_len = input_ids.shape[1]
+        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+        if n_diff_input_output > 0:
+            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+        outputs = outputs.strip()
+        if outputs.endswith(stop_str):
+            outputs = outputs[:-len(stop_str)]
+        outputs = outputs.strip()
+
+        ans_id = shortuuid.uuid()
+        ans_file.write(json.dumps({"question_id": idx,
+                                   "prompt": cur_prompt,
+                                   "text": outputs,
+                                   "answer_id": ans_id,
+                                   "model_id": model_name,
+                                   "metadata": {}}) + "\n")
+        # ans_file.flush()
+    ans_file.close()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--image-folder", type=str, default="")
+    parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
+    parser.add_argument("--answers-file", type=str, default="answer.jsonl")
+    parser.add_argument("--conv-mode", type=str, default="llava_v1")
+    parser.add_argument("--num-chunks", type=int, default=1)
+    parser.add_argument("--chunk-idx", type=int, default=0)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--top_p", type=float, default=None)
+    parser.add_argument("--num_beams", type=int, default=1)
+    args = parser.parse_args()
+
+    eval_model(args)
diff --git a/llava/eval/model_vqa_mmbench.py b/llava/eval/model_vqa_mmbench.py
new file mode 100644
index 0000000..4e7e753
--- /dev/null
+++ b/llava/eval/model_vqa_mmbench.py
@@ -0,0 +1,175 @@
+import argparse
+import torch
+import os
+import json
+import pandas as pd
+from tqdm import tqdm
+import shortuuid
+
+from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import tokenizer_X_token, process_images, load_image_from_base64, get_model_name_from_path
+
+from PIL import Image
+import math
+
+
+all_options = ['A', 'B', 'C', 'D']
+
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+
+def is_none(value):
+    if value is None:
+        return True
+    if type(value) is float and math.isnan(value):
+        return True
+    if type(value) is str and value.lower() == 'nan':
+        return True
+    if type(value) is str and value.lower() == 'none':
+        return True
+    return False
+
+def get_options(row, options):
+    parsed_options = []
+    for option in options:
+        option_value = row[option]
+        if is_none(option_value):
+            break
+        parsed_options.append(option_value)
+    return parsed_options
+
+
+def eval_model(args):
+    # Model
+    disable_torch_init()
+    model_path = os.path.expanduser(args.model_path)
+    model_name = get_model_name_from_path(model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
+
+    questions = pd.read_table(os.path.expanduser(args.question_file))
+    questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
+    answers_file = os.path.expanduser(args.answers_file)
+    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
+    ans_file = open(answers_file, "w")
+
+    if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode:
+        args.conv_mode = args.conv_mode + '_mmtag'
+        print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.')
+
+    for index, row in tqdm(questions.iterrows(), total=len(questions)):
+        options = get_options(row, all_options)
+        cur_option_char = all_options[:len(options)]
+
+        if args.all_rounds:
+            num_rounds = len(options)
+        else:
+            num_rounds = 1
+
+        for round_idx in range(num_rounds):
+            idx = row['index']
+            question = row['question']
+            hint = row['hint']
+            image = load_image_from_base64(row['image'])
+            if not is_none(hint):
+                question = hint + '\n' + question
+            for option_char, option in zip(all_options[:len(options)], options):
+                question = question + '\n' + option_char + '. ' + option
+            qs = cur_prompt = question
+            # if model.config.mm_use_im_start_end:
+            #     qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
+            # else:
+            #     qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
+            if model.config.mm_use_x_start_end:
+                qs = DEFAULT_X_START_TOKEN['IMAGE'] + DEFAULT_X_TOKEN['IMAGE'] + DEFAULT_X_END_TOKEN[
+                    'IMAGE'] + '\n' + qs
+            else:
+                qs = DEFAULT_X_TOKEN['IMAGE'] + '\n' + qs
+
+            if args.single_pred_prompt:
+                if args.lang == 'cn':
+                    qs = qs + '\n' + "请直接回答选项字母。"
+                else:
+                    qs = qs + '\n' + "Answer with the option's letter from the given choices directly."
+
+            conv = conv_templates[args.conv_mode].copy()
+            conv.append_message(conv.roles[0], qs)
+            conv.append_message(conv.roles[1], None)
+            prompt = conv.get_prompt()
+
+            input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt').unsqueeze(0).cuda()
+
+            image_tensor = process_images([image], processor['image'], model.config)[0]
+            # image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
+
+            stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+
+            with torch.inference_mode():
+                output_ids = model.generate(
+                    input_ids,
+                    images=[[image_tensor.half().cuda()], ['image']],
+                    do_sample=True if args.temperature > 0 else False,
+                    temperature=args.temperature,
+                    top_p=args.top_p,
+                    num_beams=args.num_beams,
+                    # no_repeat_ngram_size=3,
+                    max_new_tokens=1024,
+                    use_cache=True)
+
+            input_token_len = input_ids.shape[1]
+            n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+            if n_diff_input_output > 0:
+                print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+            outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+            outputs = outputs.strip()
+            if outputs.endswith(stop_str):
+                outputs = outputs[:-len(stop_str)]
+            outputs = outputs.strip()
+
+            ans_id = shortuuid.uuid()
+            ans_file.write(json.dumps({"question_id": idx,
+                                    "round_id": round_idx,
+                                    "prompt": cur_prompt,
+                                    "text": outputs,
+                                    "options": options,
+                                    "option_char": cur_option_char,
+                                    "answer_id": ans_id,
+                                    "model_id": model_name,
+                                    "metadata": {}}) + "\n")
+            ans_file.flush()
+
+            # rotate options
+            options = options[1:] + options[:1]
+            cur_option_char = cur_option_char[1:] + cur_option_char[:1]
+    ans_file.close()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--image-folder", type=str, default="")
+    parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
+    parser.add_argument("--answers-file", type=str, default="answer.jsonl")
+    parser.add_argument("--conv-mode", type=str, default="llava_v1")
+    parser.add_argument("--num-chunks", type=int, default=1)
+    parser.add_argument("--chunk-idx", type=int, default=0)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--top_p", type=float, default=None)
+    parser.add_argument("--num_beams", type=int, default=1)
+    parser.add_argument("--all-rounds", action="store_true")
+    parser.add_argument("--single-pred-prompt", action="store_true")
+    parser.add_argument("--lang", type=str, default="en")
+    args = parser.parse_args()
+
+    eval_model(args)
diff --git a/llava/eval/model_vqa_science.py b/llava/eval/model_vqa_science.py
new file mode 100644
index 0000000..3815ffe
--- /dev/null
+++ b/llava/eval/model_vqa_science.py
@@ -0,0 +1,150 @@
+import argparse
+import torch
+import os
+import json
+from tqdm import tqdm
+import shortuuid
+
+from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import tokenizer_X_token, get_model_name_from_path, KeywordsStoppingCriteria
+
+from PIL import Image
+import math
+
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+
+def eval_model(args):
+    # Model
+    disable_torch_init()
+    model_path = os.path.expanduser(args.model_path)
+    model_name = get_model_name_from_path(model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
+    print(model)
+    print(processor)
+    print(model_path, model_name)
+    questions = json.load(open(os.path.expanduser(args.question_file), "r"))
+    questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
+    answers_file = os.path.expanduser(args.answers_file)
+    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
+    ans_file = open(answers_file, "w")
+    for i, line in enumerate(tqdm(questions)):
+        idx = line["id"]
+        question = line['conversations'][0]
+        qs = question['value'].replace('<image>', '').strip()
+        cur_prompt = qs
+
+        if 'image' in line:
+            image_file = line["image"]
+            image = Image.open(os.path.join(args.image_folder, image_file))
+            image_tensor = processor['image'].preprocess(image, return_tensors='pt')['pixel_values'][0]
+            # images = image_tensor.unsqueeze(0).half().cuda() ##########
+            images = image_tensor.half().cuda()
+            if getattr(model.config, 'mm_use_x_start_end', False):
+                qs = DEFAULT_X_START_TOKEN['IMAGE'] + DEFAULT_X_TOKEN['IMAGE'] + DEFAULT_X_END_TOKEN['IMAGE'] + '\n' + qs
+            else:
+                qs = DEFAULT_X_TOKEN['IMAGE'] + '\n' + qs
+            cur_prompt = '<image>' + '\n' + cur_prompt
+        else:
+            images = None
+
+        if args.single_pred_prompt:
+            qs = qs + '\n' + "Answer with the option's letter from the given choices directly."
+            cur_prompt = cur_prompt + '\n' + "Answer with the option's letter from the given choices directly."
+
+        conv = conv_templates[args.conv_mode].copy()
+        conv.append_message(conv.roles[0], qs)
+        conv.append_message(conv.roles[1], None)
+        prompt = conv.get_prompt()
+
+        input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt').unsqueeze(0).cuda()
+
+        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+        keywords = [stop_str]
+        stopping_criteria = [KeywordsStoppingCriteria(keywords, tokenizer, input_ids)] if conv.version == "v0" else None
+
+        with torch.inference_mode():
+            output_ids = model.generate(
+                input_ids,
+                images=[[images], ['image']],
+                do_sample=True if args.temperature > 0 else False,
+                temperature=args.temperature,
+                max_new_tokens=1024,
+                use_cache=True,
+                stopping_criteria=stopping_criteria,
+            )
+
+        input_token_len = input_ids.shape[1]
+        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+        if n_diff_input_output > 0:
+            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+        outputs = outputs.strip()
+        if outputs.endswith(stop_str):
+            outputs = outputs[:-len(stop_str)]
+        outputs = outputs.strip()
+
+        # prompt for answer
+        if args.answer_prompter:
+            outputs_reasoning = outputs
+            input_ids = tokenizer_X_token(prompt + outputs_reasoning + ' ###\nANSWER:', tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt').unsqueeze(0).cuda()
+
+            with torch.inference_mode():
+                output_ids = model.generate(
+                    input_ids,
+                    images=[[images], ['image']],
+                    do_sample=True if args.temperature > 0 else False,
+                    temperature=args.temperature,
+                    max_new_tokens=64,
+                    use_cache=True,
+                    stopping_criteria=[stopping_criteria])
+
+            input_token_len = input_ids.shape[1]
+            n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+            if n_diff_input_output > 0:
+                print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+            outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+            outputs = outputs.strip()
+            if outputs.endswith(stop_str):
+                outputs = outputs[:-len(stop_str)]
+            outputs = outputs.strip()
+            outputs = outputs_reasoning + '\n The answer is ' + outputs
+
+        ans_id = shortuuid.uuid()
+        ans_file.write(json.dumps({"question_id": idx,
+                                   "prompt": cur_prompt,
+                                   "text": outputs,
+                                   "answer_id": ans_id,
+                                   "model_id": model_name,
+                                   "metadata": {}}) + "\n")
+        ans_file.flush()
+    ans_file.close()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--image-folder", type=str, default="")
+    parser.add_argument("--question-file", type=str, default="tables/question.json")
+    parser.add_argument("--answers-file", type=str, default="answer.jsonl")
+    parser.add_argument("--conv-mode", type=str, default="llava_v0")
+    parser.add_argument("--num-chunks", type=int, default=1)
+    parser.add_argument("--chunk-idx", type=int, default=0)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--answer-prompter", action="store_true")
+    parser.add_argument("--single-pred-prompt", action="store_true")
+    args = parser.parse_args()
+
+    eval_model(args)
diff --git a/llava/eval/qa_baseline_gpt35.py b/llava/eval/qa_baseline_gpt35.py
new file mode 100644
index 0000000..babab6e
--- /dev/null
+++ b/llava/eval/qa_baseline_gpt35.py
@@ -0,0 +1,74 @@
+"""Generate answers with GPT-3.5"""
+# Note: you need to be using OpenAI Python v0.27.0 for the code below to work
+import argparse
+import json
+import os
+import time
+import concurrent.futures
+
+import openai
+import tqdm
+import shortuuid
+
+MODEL = 'gpt-3.5-turbo'
+MODEL_ID = 'gpt-3.5-turbo:20230327'
+
+def get_answer(question_id: int, question: str, max_tokens: int):
+    ans = {
+        'answer_id': shortuuid.uuid(),
+        'question_id': question_id,
+        'model_id': MODEL_ID,
+    }
+    for _ in range(3):
+        try:
+            response = openai.ChatCompletion.create(
+                model=MODEL,
+                messages=[{
+                    'role': 'system',
+                    'content': 'You are a helpful assistant.'
+                }, {
+                    'role': 'user',
+                    'content': question,
+                }],
+                max_tokens=max_tokens,
+            )
+            ans['text'] = response['choices'][0]['message']['content']
+            return ans
+        except Exception as e:
+            print('[ERROR]', e)
+            ans['text'] = '#ERROR#'
+            time.sleep(1)
+    return ans
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='ChatGPT answer generation.')
+    parser.add_argument('-q', '--question')
+    parser.add_argument('-o', '--output')
+    parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output')
+    args = parser.parse_args()
+
+    questions_dict = {}
+    with open(os.path.expanduser(args.question)) as f:
+        for line in f:
+            if not line:
+                continue
+            q = json.loads(line)
+            questions_dict[q['question_id']] = q['text']
+
+    answers = []
+
+    with concurrent.futures.ThreadPoolExecutor(max_workers=32) as executor:
+        futures = []
+        for qid, question in questions_dict.items():
+            future = executor.submit(get_answer, qid, question, args.max_tokens)
+            futures.append(future)
+
+        for future in tqdm.tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
+            answers.append(future.result())
+
+    answers.sort(key=lambda x: x['question_id'])
+
+    with open(os.path.expanduser(args.output), 'w') as f:
+        table = [json.dumps(ans) for ans in answers]
+        f.write('\n'.join(table))
diff --git a/llava/eval/run_llava.py b/llava/eval/run_llava.py
new file mode 100644
index 0000000..b54f0d6
--- /dev/null
+++ b/llava/eval/run_llava.py
@@ -0,0 +1,97 @@
+import argparse
+import torch
+
+from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import tokenizer_X_token, get_model_name_from_path, KeywordsStoppingCriteria
+
+from PIL import Image
+
+import requests
+from PIL import Image
+from io import BytesIO
+
+
+def load_image(image_file):
+    if image_file.startswith('http') or image_file.startswith('https'):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    else:
+        image = Image.open(image_file).convert('RGB')
+    return image
+
+
+def eval_model(args):
+    # Model
+    disable_torch_init()
+
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)
+
+    qs = args.query
+    if model.config.mm_use_im_start_end:
+        qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
+    else:
+        qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
+
+    if 'llama-2' in model_name.lower():
+        conv_mode = "llava_llama_2"
+    elif "v1" in model_name.lower():
+        conv_mode = "llava_v1"
+    elif "mpt" in model_name.lower():
+        conv_mode = "mpt"
+    else:
+        conv_mode = "llava_v0"
+
+    if args.conv_mode is not None and conv_mode != args.conv_mode:
+        print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
+    else:
+        args.conv_mode = conv_mode
+
+    conv = conv_templates[args.conv_mode].copy()
+    conv.append_message(conv.roles[0], qs)
+    conv.append_message(conv.roles[1], None)
+    prompt = conv.get_prompt()
+
+    image = load_image(args.image_file)
+    image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda()
+
+    input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
+
+    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+    keywords = [stop_str]
+    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+
+    with torch.inference_mode():
+        output_ids = model.generate(
+            input_ids,
+            images=image_tensor,
+            do_sample=True,
+            temperature=0.2,
+            max_new_tokens=1024,
+            use_cache=True,
+            stopping_criteria=[stopping_criteria])
+
+    input_token_len = input_ids.shape[1]
+    n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+    if n_diff_input_output > 0:
+        print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+    outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+    outputs = outputs.strip()
+    if outputs.endswith(stop_str):
+        outputs = outputs[:-len(stop_str)]
+    outputs = outputs.strip()
+    print(outputs)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--image-file", type=str, required=True)
+    parser.add_argument("--query", type=str, required=True)
+    parser.add_argument("--conv-mode", type=str, default=None)
+    args = parser.parse_args()
+
+    eval_model(args)
diff --git a/llava/eval/summarize_gpt_review.py b/llava/eval/summarize_gpt_review.py
new file mode 100644
index 0000000..0f796a3
--- /dev/null
+++ b/llava/eval/summarize_gpt_review.py
@@ -0,0 +1,60 @@
+import json
+import os
+from collections import defaultdict
+
+import numpy as np
+
+import argparse
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.')
+    parser.add_argument('-d', '--dir', default=None)
+    parser.add_argument('-v', '--version', default=None)
+    parser.add_argument('-s', '--select', nargs='*', default=None)
+    parser.add_argument('-f', '--files', nargs='*', default=[])
+    parser.add_argument('-i', '--ignore', nargs='*', default=[])
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    if args.ignore is not None:
+        args.ignore = [int(x) for x in args.ignore]
+
+    if len(args.files) > 0:
+        review_files = args.files
+    else:
+        review_files = [x for x in os.listdir(args.dir) if x.endswith('.jsonl') and (x.startswith('gpt4_text') or x.startswith('reviews_') or x.startswith('review_') or 'review' in args.dir)]
+
+    for review_file in sorted(review_files):
+        config = os.path.basename(review_file).replace('gpt4_text_', '').replace('.jsonl', '')
+        if args.select is not None and any(x not in config for x in args.select):
+            continue
+        if '0613' in config:
+            version = '0613'
+        else:
+            version = '0314'
+        if args.version is not None and args.version != version:
+            continue
+        scores = defaultdict(list)
+        print(config)
+        with open(os.path.join(args.dir, review_file) if args.dir is not None else review_file) as f:
+            for review_str in f:
+                review = json.loads(review_str)
+                if review['question_id'] in args.ignore:
+                    continue
+                if 'category' in review:
+                    scores[review['category']].append(review['tuple'])
+                    scores['all'].append(review['tuple'])
+                else:
+                    if 'tuple' in review:
+                        scores['all'].append(review['tuple'])
+                    else:
+                        scores['all'].append(review['score'])
+        for k, v in sorted(scores.items()):
+            stats = np.asarray(v).mean(0).tolist()
+            stats = [round(x, 3) for x in stats]
+            # print(k, stats, round(stats[1]/stats[0]*100, 1))
+            print(k, round(stats[1]/stats[0]*100, 1), round(stats[0] * 10, 1), round(stats[1] * 10, 1))
+        print('=================================')
diff --git a/llava/eval/table/caps_boxes_coco2014_val_80.jsonl b/llava/eval/table/caps_boxes_coco2014_val_80.jsonl
new file mode 100644
index 0000000..a0130f3
--- /dev/null
+++ b/llava/eval/table/caps_boxes_coco2014_val_80.jsonl
@@ -0,0 +1,80 @@
+{"id": "000000296284", "image": "000000296284.jpg", "captions": ["A donut shop is full of different flavors of donuts.", "Fruit flavored donuts lined up in a glass fronted cabinet", "A rack with some doughnuts in a glass case.", "A display case in a bakery filled with donuts.", "An assortment of doughnuts are arranged in a display case."], "instances": [{"category": "donut", "bbox": [0.37, 0.584, 0.504, 0.709]}, {"category": "donut", "bbox": [0.369, 0.22, 0.492, 0.317]}, {"category": "donut", "bbox": [0.471, 0.587, 0.639, 0.706]}, {"category": "donut", "bbox": [0.544, 0.213, 0.679, 0.316]}, {"category": "donut", "bbox": [0.035, 0.22, 0.196, 0.328]}, {"category": "donut", "bbox": [0.054, 0.608, 0.221, 0.711]}, {"category": "donut", "bbox": [0.283, 0.586, 0.429, 0.708]}, {"category": "donut", "bbox": [0.466, 0.226, 0.585, 0.32]}, {"category": "donut", "bbox": [0.28, 0.232, 0.393, 0.322]}, {"category": "donut", "bbox": [0.0, 0.609, 0.097, 0.722]}]}
+{"id": "000000151358", "image": "000000151358.jpg", "captions": ["A newspaper that has sunglasses on top of it sitting in front of books.", "an apple sunglasses books and a teddy bear", "A folded newspaper and sunglasses are on a table with an apple, books, and teddy bear behind.", "An apple sitting on a table next to sunglasses and a news paper.", "There are sunglasses laying on the folded newspaper."], "instances": [{"category": "tie", "bbox": [0.258, 0.074, 0.527, 0.589]}, {"category": "apple", "bbox": [0.621, 0.482, 0.853, 0.645]}, {"category": "book", "bbox": [0.154, 0.107, 0.275, 0.59]}, {"category": "book", "bbox": [0.535, 0.09, 0.735, 0.583]}, {"category": "book", "bbox": [0.051, 0.112, 0.159, 0.6]}, {"category": "teddy bear", "bbox": [0.753, 0.084, 1.0, 0.517]}, {"category": "book", "bbox": [0.681, 0.097, 0.796, 0.483]}, {"category": "book", "bbox": [0.443, 0.099, 0.574, 0.588]}, {"category": "book", "bbox": [0.267, 0.337, 0.386, 0.579]}]}
+{"id": "000000052312", "image": "000000052312.jpg", "captions": ["The old man literally has a toothbrush mustache.", "An old man with a tooth brush head under his nose, mimicking Hitler", "A man wearing a toothbrush for a moustache.", "A man with the head of a toothbrush under his nose like a mustache", "An elderly man wearing the head of a toothbrush as a moustache."], "instances": [{"category": "toothbrush", "bbox": [0.345, 0.59, 0.594, 0.679]}, {"category": "person", "bbox": [0.0, 0.03, 1.0, 0.99]}]}
+{"id": "000000473210", "image": "000000473210.jpg", "captions": ["two people taking apart their wii controllers to replace batteries", "People taking apart video game remote controls on a table", "People handling a couple of remotes taking them apart.", "two sets of hands a wooden table and two controllers", "Two people who are taking apart a video game controller."], "instances": [{"category": "person", "bbox": [0.002, 0.334, 0.453, 0.986]}, {"category": "remote", "bbox": [0.407, 0.207, 0.727, 0.604]}, {"category": "remote", "bbox": [0.088, 0.344, 0.313, 0.547]}, {"category": "laptop", "bbox": [0.001, 0.049, 0.1, 0.197]}, {"category": "person", "bbox": [0.484, 0.254, 0.998, 0.985]}, {"category": "dining table", "bbox": [0.0, 0.003, 1.0, 0.956]}]}
+{"id": "000000097131", "image": "000000097131.jpg", "captions": ["A car parked by a parking meter in front of a building.", "A car is sitting parked at a curb in front of a parking meter.", "A black car on the street next to a parking meter.", "A gray car parked in front of two parking meters.", "A black car parked on the side of the road."], "instances": [{"category": "car", "bbox": [0.227, 0.362, 0.946, 0.761]}, {"category": "car", "bbox": [0.793, 0.322, 0.88, 0.4]}, {"category": "car", "bbox": [0.0, 0.447, 0.028, 0.726]}, {"category": "parking meter", "bbox": [0.156, 0.35, 0.186, 0.453]}, {"category": "truck", "bbox": [0.907, 0.331, 1.0, 0.408]}, {"category": "parking meter", "bbox": [0.188, 0.349, 0.218, 0.448]}]}
+{"id": "000000543364", "image": "000000543364.jpg", "captions": ["There is a table in the middle of the room.", "A room with a couch, table, lamp and a chaise.", "A living room with couch, chaise, track lighting, and a large window.", "A room with large windows, a couch and a table.", "A living room with lots of furniture and a large window."], "instances": [{"category": "dining table", "bbox": [0.388, 0.644, 0.636, 0.879]}, {"category": "couch", "bbox": [0.194, 0.531, 0.552, 0.777]}, {"category": "couch", "bbox": [0.568, 0.488, 0.907, 0.783]}, {"category": "remote", "bbox": [0.524, 0.651, 0.556, 0.675]}, {"category": "chair", "bbox": [0.661, 0.478, 0.802, 0.604]}]}
+{"id": "000000217181", "image": "000000217181.jpg", "captions": ["They are standing next to some stylish motorcycles.", "Three men are standing around looking at sports motorcycles.", "A small group of men are standing around a motorcycle.", "Two men surrounding a blue motorcycle and others", "A few blue motorcycles are parked in a lot."], "instances": [{"category": "car", "bbox": [0.011, 0.177, 0.2, 0.336]}, {"category": "motorcycle", "bbox": [0.032, 0.139, 0.907, 0.982]}, {"category": "motorcycle", "bbox": [0.0, 0.239, 0.148, 0.613]}, {"category": "motorcycle", "bbox": [0.0, 0.301, 0.106, 0.45]}, {"category": "person", "bbox": [0.775, 0.043, 0.93, 0.463]}, {"category": "person", "bbox": [0.717, 0.116, 0.81, 0.509]}, {"category": "person", "bbox": [0.296, 0.008, 0.472, 0.325]}, {"category": "person", "bbox": [0.115, 0.19, 0.164, 0.269]}, {"category": "truck", "bbox": [0.63, 0.227, 0.731, 0.335]}]}
+{"id": "000000140289", "image": "000000140289.jpg", "captions": ["Two born bears walking though a forest surrounded by trees.", "Two full grown brown bears in a habitat.", "Two bears are roaming around in the woods.", "Two bears around logs in front of a large rock.", "Two big bears wandering through the woods together"], "instances": [{"category": "bear", "bbox": [0.131, 0.269, 0.375, 0.65]}, {"category": "bear", "bbox": [0.568, 0.193, 0.809, 0.827]}]}
+{"id": "000000460149", "image": "000000460149.jpg", "captions": ["A clock hosted on a pole on a pavement next to a building", "Street clock on quiet street with trees and bicycles.", "A tall clock stands on an empty sidewalk.", "A pole that has a clock on the top of it.", "a clock on a short tower and potted plants along the sidewalk"], "instances": [{"category": "potted plant", "bbox": [0.14, 0.71, 0.338, 0.856]}, {"category": "bicycle", "bbox": [0.65, 0.671, 0.766, 0.733]}, {"category": "car", "bbox": [0.38, 0.608, 0.488, 0.656]}, {"category": "clock", "bbox": [0.468, 0.048, 0.699, 0.216]}, {"category": "bicycle", "bbox": [0.669, 0.662, 0.719, 0.67]}, {"category": "car", "bbox": [0.786, 0.625, 0.86, 0.668]}, {"category": "potted plant", "bbox": [0.756, 0.637, 0.819, 0.682]}, {"category": "person", "bbox": [0.942, 0.615, 0.954, 0.641]}, {"category": "bicycle", "bbox": [0.648, 0.68, 0.714, 0.747]}, {"category": "car", "bbox": [0.837, 0.619, 0.88, 0.659]}, {"category": "potted plant", "bbox": [0.017, 0.197, 0.443, 0.686]}]}
+{"id": "000000225738", "image": "000000225738.jpg", "captions": ["A group of giraffes standing up in their natural habitat.", "A group of giraffe standing in a grass field.", "A group of four giraffes near the same tree.", "there are four giraffes standing among some dry brush", "A herd of giraffe standing on top of a grass field."], "instances": [{"category": "giraffe", "bbox": [0.648, 0.231, 0.855, 0.915]}, {"category": "giraffe", "bbox": [0.33, 0.136, 0.521, 0.93]}, {"category": "giraffe", "bbox": [0.406, 0.261, 0.515, 1.0]}, {"category": "giraffe", "bbox": [0.347, 0.194, 0.583, 0.922]}]}
+{"id": "000000109532", "image": "000000109532.jpg", "captions": ["An adorable husky dog sleeping in a dog bed next to a fan.", "A dark room with a dog sleeping on a dog bed.", "A dog is sleeping in a dark room.", "a large dog laying in a dog bed in a living room", "A dog sleeping on a dog bed in a room."], "instances": [{"category": "dog", "bbox": [0.426, 0.661, 0.582, 0.925]}, {"category": "potted plant", "bbox": [0.603, 0.261, 0.781, 0.613]}, {"category": "chair", "bbox": [0.67, 0.515, 0.899, 0.801]}, {"category": "potted plant", "bbox": [0.671, 0.439, 0.763, 0.612]}, {"category": "chair", "bbox": [0.852, 0.653, 0.948, 0.818]}]}
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+{"id": "000000367571", "image": "000000367571.jpg", "captions": ["A couple of different doughnuts in a box", "There are four donuts in a box, and some are cake donuts and a doughnut with nuts and coconut on top.", "A box of glazed doughnuts on a table.", "Three donuts with toppings on them sitting inside a box.", "A box that is filled with different kinds of doughnuts."], "instances": [{"category": "donut", "bbox": [0.412, 0.335, 0.711, 0.681]}, {"category": "donut", "bbox": [0.093, 0.493, 0.486, 0.922]}, {"category": "donut", "bbox": [0.713, 0.423, 0.957, 0.874]}, {"category": "donut", "bbox": [0.13, 0.331, 0.397, 0.55]}]}
+{"id": "000000580197", "image": "000000580197.jpg", "captions": ["Two men in bow ties standing next to steel rafter.", "Several men in suits talking together in a room.", "An older man in a tuxedo standing next to a younger man in a tuxedo wearing glasses.", "Two men wearing tuxedos glance at each other.", "Older man in tuxedo sitting next to another younger man in tuxedo."], "instances": [{"category": "tie", "bbox": [0.914, 0.46, 0.984, 0.512]}, {"category": "person", "bbox": [0.297, 0.638, 0.71, 0.989]}, {"category": "person", "bbox": [0.77, 0.177, 1.0, 0.971]}, {"category": "tie", "bbox": [0.281, 0.481, 0.368, 0.519]}, {"category": "person", "bbox": [0.103, 0.204, 0.497, 1.0]}]}
+{"id": "000000506095", "image": "000000506095.jpg", "captions": ["A cat is staring at a laptop computer.", "a cat on a desk with a laptop and a mouse", "A cat that is sitting at a desk next to a laptop.", "A kitten sitting on a laptop computer sitting on top of a wooden desk.", "A kitten sits facing an open black laptop."], "instances": [{"category": "cat", "bbox": [0.658, 0.207, 1.0, 0.754]}, {"category": "laptop", "bbox": [0.108, 0.135, 0.766, 0.69]}, {"category": "book", "bbox": [0.836, 0.239, 0.954, 0.273]}, {"category": "book", "bbox": [0.0, 0.556, 0.128, 0.685]}, {"category": "book", "bbox": [0.039, 0.574, 0.257, 0.691]}, {"category": "book", "bbox": [0.825, 0.214, 0.962, 0.254]}, {"category": "book", "bbox": [0.892, 0.275, 0.958, 0.308]}, {"category": "book", "bbox": [0.922, 0.318, 0.986, 0.353]}, {"category": "book", "bbox": [0.87, 0.267, 0.951, 0.291]}, {"category": "book", "bbox": [0.949, 0.102, 0.976, 0.114]}, {"category": "book", "bbox": [0.936, 0.161, 0.958, 0.168]}]}
+{"id": "000000024996", "image": "000000024996.jpg", "captions": ["A bathroom with a glass door and a sink.", "A blue lined bathroom with an open glass door.", "A nice bathroom with a sink, toilet, and tiled shower.", "A bathroom that is clean and shiny in the day.", "a bathroom with a sink and a mirror and a window"], "instances": [{"category": "toilet", "bbox": [0.842, 0.934, 0.95, 1.0]}, {"category": "sink", "bbox": [0.506, 0.724, 0.683, 0.834]}]}
+{"id": "000000457882", "image": "000000457882.jpg", "captions": ["a girl in a bikini and a brown and white dog and a few other people", "A woman with a swimsuit on sitting with a dog.", "A woman is sitting with a dog on her lap.", "A dog sitting next to a woman in her swimsuit.", "WOMAN SITTING WITH HER DOG, AND OTHER WOMEN ARE AROUND"], "instances": [{"category": "dog", "bbox": [0.202, 0.409, 0.54, 0.81]}, {"category": "dog", "bbox": [0.61, 0.428, 0.729, 0.723]}, {"category": "boat", "bbox": [0.003, 0.705, 0.939, 0.974]}, {"category": "person", "bbox": [0.236, 0.001, 0.558, 0.784]}, {"category": "person", "bbox": [0.681, 0.001, 0.957, 0.798]}, {"category": "person", "bbox": [0.849, 0.478, 1.0, 0.946]}, {"category": "person", "bbox": [0.345, 0.187, 0.634, 0.828]}, {"category": "person", "bbox": [0.033, 0.345, 0.109, 0.434]}]}
+{"id": "000000081552", "image": "000000081552.jpg", "captions": ["A cat sitting and curled up on a red couch", "A cat laying on a red couch sleeping.", "a tan and black cat curled up asleep on a red velvet seat", "A cat is curled up on a red sofa.", "Cat curled up, sleeping on a red plush couch."], "instances": [{"category": "cat", "bbox": [0.412, 0.237, 0.634, 0.482]}, {"category": "couch", "bbox": [0.003, 0.005, 1.0, 0.99]}]}
+{"id": "000000273450", "image": "000000273450.jpg", "captions": ["A person flipping of a parking meter on the side of a road.", "A man holds up his middle finger to a parking meter.", "Person giving the middle finger to a parking meter.", "a black silver white blue red an orange parking meter and a hand flipping it off", "A person is flipping off a parking meter."], "instances": [{"category": "person", "bbox": [0.0, 0.475, 0.565, 0.987]}, {"category": "car", "bbox": [0.0, 0.0, 0.531, 0.734]}, {"category": "parking meter", "bbox": [0.0, 0.0, 1.0, 0.987]}]}
+{"id": "000000203879", "image": "000000203879.jpg", "captions": ["There is a small cellphone displayed between a set of ear buds and two paper weights.", "a cell phone lays next to some diamonds", "a close up of a cell phone on a table near earbuds", "A cell phone sits on a table next to some jewels.", "A cell phone, ear buds, and two jewels laying near each other."], "instances": [{"category": "cell phone", "bbox": [0.322, 0.233, 0.62, 0.79]}]}
+{"id": "000000346875", "image": "000000346875.jpg", "captions": ["two zebras in a field near one another", "A couple of zebra walking across a green field.", "Two zebra are walking near a gravel road.", "two zebras in a green field of grass and some trees", "A zebra follows another zebra through a park."], "instances": [{"category": "zebra", "bbox": [0.591, 0.263, 0.82, 0.466]}, {"category": "zebra", "bbox": [0.293, 0.243, 0.561, 0.45]}]}
+{"id": "000000525439", "image": "000000525439.jpg", "captions": ["a man stands in front of a flipped skate boarder", "A man standing next to a skateboard that is laying on the ground wheels pointed up.", "Skateboard laying upside down on cement with someone standing next to it.", "A boy in camo shorts stands before an overturned skateboard.", "a person with an upside down skate board"], "instances": [{"category": "person", "bbox": [0.307, 0.001, 0.63, 0.739]}, {"category": "skateboard", "bbox": [0.0, 0.592, 0.626, 0.969]}]}
+{"id": "000000304749", "image": "000000304749.jpg", "captions": ["The woman is taking a picture in the bathroom mirror.", "A picture of a woman in a mirror.", "A woman's midsection reflected in a round mirror.", "A circular mirror reflecting a woman's stomach in turquoise shirt.", "A selfie taken of a person from the neck down."], "instances": [{"category": "person", "bbox": [0.092, 0.001, 0.646, 0.496]}]}
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+{"id": "000000066144", "image": "000000066144.jpg", "captions": ["A woman standing in front of window next to a bug and a stop sign.", "A car parked on the street next to a tree and stop sign.", "A lone Volkswagen is parked by a stop sign.", "A window view of a small car near a street stop sign.", "An old VW Bug standing at a stop sign."], "instances": [{"category": "stop sign", "bbox": [0.501, 0.328, 0.569, 0.428]}, {"category": "car", "bbox": [0.242, 0.488, 0.56, 0.726]}, {"category": "car", "bbox": [0.279, 0.325, 0.33, 0.363]}, {"category": "car", "bbox": [0.153, 0.333, 0.29, 0.405]}, {"category": "car", "bbox": [0.11, 0.339, 0.177, 0.373]}, {"category": "car", "bbox": [0.0, 0.654, 0.082, 0.826]}, {"category": "car", "bbox": [0.0, 0.322, 0.064, 0.364]}, {"category": "car", "bbox": [0.451, 0.333, 0.51, 0.392]}]}
+{"id": "000000455772", "image": "000000455772.jpg", "captions": ["A person in a field jumping to catch a Frisbee.", "A guy jumping to catch a frisbee in mid-air.", "A person that is trying to get a frisbee.", "Nice reach, but the Frisbee flies on, victorious.", "A man playing frisbee in a grassy yard."], "instances": [{"category": "car", "bbox": [0.148, 0.339, 0.201, 0.476]}, {"category": "car", "bbox": [0.376, 0.396, 0.424, 0.476]}, {"category": "person", "bbox": [0.547, 0.122, 0.698, 0.904]}, {"category": "frisbee", "bbox": [0.479, 0.154, 0.555, 0.231]}, {"category": "car", "bbox": [0.001, 0.299, 0.085, 0.394]}]}
+{"id": "000000511117", "image": "000000511117.jpg", "captions": ["A couple of kids standing on top of a grass covered field.", "A little boy wearing a baseball uniform stands by a little girl.", "A young boy in a baseball uniform and a young girl are standing in front of a chain link fence.", "A little boy and girl standing on a baseball field. The boy has a uniform on.", "A young baseball player is standing next to a young girl."], "instances": [{"category": "person", "bbox": [0.514, 0.178, 0.776, 0.774]}, {"category": "baseball glove", "bbox": [0.468, 0.462, 0.593, 0.609]}, {"category": "person", "bbox": [0.174, 0.051, 0.598, 0.839]}, {"category": "bench", "bbox": [0.558, 0.125, 1.0, 0.315]}]}
+{"id": "000000207151", "image": "000000207151.jpg", "captions": ["A vegetarian pizza is half eaten on a pizza holder.", "A couple of pieces of pizza with vegetable slices on them.", "A wooden pan serving tray with a pizza on it.", "A pizza on a cutting board is half gone.", "A Pizza is nearly finished with only three pieces left."], "instances": [{"category": "bottle", "bbox": [0.001, 0.001, 0.121, 0.231]}, {"category": "cup", "bbox": [0.0, 0.002, 0.121, 0.238]}, {"category": "pizza", "bbox": [0.17, 0.472, 0.526, 0.82]}, {"category": "pizza", "bbox": [0.398, 0.106, 0.962, 0.679]}, {"category": "dining table", "bbox": [0.0, 0.001, 1.0, 0.988]}]}
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+{"id": "000000378545", "image": "000000378545.jpg", "captions": ["A pole that has a clock on top of it.", "A clock mounted on an outdoor post with Roman numerals.", "a clock on a pole saying it is 12:45", "An ornamental standing clock is at the foreground of a row of houses.", "A black and gold clock on a pole in front of a building."], "instances": [{"category": "clock", "bbox": [0.216, 0.249, 0.749, 0.658]}]}
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+{"id": "000000161011", "image": "000000161011.jpg", "captions": ["Three skiers posing for a picture on the slope.", "Three skiers pause for a photo at the top of a mountain.", "Three people standing on a mountain taking a picture as they ski.", "A woman and two men on skis on a snowy hillside surrounded by trees", "Three skiers have stopped to pose for a picture."], "instances": [{"category": "person", "bbox": [0.36, 0.321, 0.509, 0.82]}, {"category": "person", "bbox": [0.179, 0.281, 0.349, 0.795]}, {"category": "person", "bbox": [0.611, 0.292, 0.751, 0.809]}, {"category": "skis", "bbox": [0.595, 0.743, 0.732, 0.961]}, {"category": "skis", "bbox": [0.341, 0.724, 0.621, 0.907]}, {"category": "skis", "bbox": [0.212, 0.705, 0.398, 0.905]}]}
+{"id": "000000284296", "image": "000000284296.jpg", "captions": ["Three giraffe's leaning over to get a sip of water.", "an image of a herd of giraffes in the water", "three giraffes banding down to drink water with trees in the background", "Three giraffe drinking from a pond with brush in back.", "Giraffes leaning down to drink at a watering hole"], "instances": [{"category": "giraffe", "bbox": [0.624, 0.387, 0.822, 0.635]}, {"category": "giraffe", "bbox": [0.4, 0.326, 0.561, 0.58]}, {"category": "giraffe", "bbox": [0.152, 0.291, 0.343, 0.551]}]}
+{"id": "000000056013", "image": "000000056013.jpg", "captions": ["a number of luggage bags on a cart in a lobby", "Wheeled cart with luggage at lobby of commercial business.", "Trolley used for transporting personal luggage to guests rooms.", "A luggage cart topped with lots of luggage.", "a cart filled with suitcases and bags"], "instances": [{"category": "backpack", "bbox": [0.276, 0.52, 0.456, 0.678]}, {"category": "suitcase", "bbox": [0.41, 0.58, 0.597, 0.827]}, {"category": "suitcase", "bbox": [0.173, 0.645, 0.363, 0.836]}, {"category": "person", "bbox": [0.959, 0.297, 1.0, 0.478]}, {"category": "suitcase", "bbox": [0.526, 0.519, 0.712, 0.706]}, {"category": "person", "bbox": [0.762, 0.253, 0.871, 0.46]}, {"category": "backpack", "bbox": [0.517, 0.514, 0.694, 0.698]}, {"category": "handbag", "bbox": [0.316, 0.181, 0.431, 0.426]}, {"category": "suitcase", "bbox": [0.747, 0.453, 0.858, 0.557]}]}
+{"id": "000000293505", "image": "000000293505.jpg", "captions": ["A person on a motor bike next to a cow.", "A woman riding a motorcycle down a dirt road.", "there is a woman riding a scooter down a dirt road", "A woman on a moped, two men and animals walking down the road.", "A woman on a motorcycle is next to a man walking a dog along with other people going down a dirt road."], "instances": [{"category": "cow", "bbox": [0.602, 0.472, 0.721, 0.816]}, {"category": "motorcycle", "bbox": [0.402, 0.512, 0.516, 0.788]}, {"category": "person", "bbox": [0.408, 0.4, 0.514, 0.639]}, {"category": "person", "bbox": [0.754, 0.301, 1.0, 1.0]}, {"category": "person", "bbox": [0.705, 0.415, 0.789, 0.714]}, {"category": "cow", "bbox": [0.347, 0.44, 0.373, 0.509]}, {"category": "cow", "bbox": [0.361, 0.436, 0.381, 0.501]}]}
+{"id": "000000305873", "image": "000000305873.jpg", "captions": ["A little girl holding a red black dotted umbrella.", "A little girl with rain boots and a rain jacket on and an open umbrella to match her jacket.", "a little girl holding onto a lady bug pattern umbrella", "The child wears a labybug rain coat with a matching umbrella.", "A little girl wearing a ladybug raincoat and green rubber boots holding a ladybug umbrella"], "instances": [{"category": "umbrella", "bbox": [0.246, 0.002, 0.992, 0.415]}, {"category": "person", "bbox": [0.35, 0.132, 0.699, 0.791]}, {"category": "car", "bbox": [0.614, 0.0, 1.0, 0.465]}]}
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+{"id": "000000431026", "image": "000000431026.jpg", "captions": ["a street sign on a city street near some tall bushes", "street signs on a metal pole lining a sidewalk lined with shrubbery.", "a large hedge of bushes on a corner near a street sign.", "Two street signs on sidewalk next to bushes and trees.", "Street signs along a well manicured street with large houses."], "instances": []}
+{"id": "000000524575", "image": "000000524575.jpg", "captions": ["Three giraffe and a wildebeest in a field.", "A moose and several giraffes are grazing in the field.", "Zebras in the wild with a wildebeest behind them", "Two giraffe and a ox standing in a field eating grass.", "Giraffes and other safari animals graze in a sunlit field."], "instances": [{"category": "cow", "bbox": [0.46, 0.716, 0.643, 0.999]}, {"category": "giraffe", "bbox": [0.285, 0.5, 0.401, 0.826]}, {"category": "giraffe", "bbox": [0.083, 0.554, 0.179, 0.821]}, {"category": "giraffe", "bbox": [0.887, 0.481, 0.968, 0.715]}]}
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+{"id": "000000018476", "image": "000000018476.jpg", "captions": ["A tie that is sitting on top of a shirt.", "This photograph appears to be looking truly wonderful.", "a uniform complete with shoes laying on a bed", "Suit laid out with a red tie, white shirt and black shoes.", "a white shirt a red tie and some black shoes"], "instances": [{"category": "tie", "bbox": [0.457, 0.09, 0.853, 0.984]}, {"category": "bed", "bbox": [0.005, 0.005, 1.0, 0.379]}]}
+{"id": "000000480652", "image": "000000480652.jpg", "captions": ["These suitcases are sitting next to a chair.", "An assortment of luggage bags stacked by a kitchen chair.", "A stack of luggage by a chair and table.", "a table and chair with several pieces of luggage nearby", "A pile of luggage sitting on the floor."], "instances": [{"category": "chair", "bbox": [0.483, 0.192, 1.0, 0.769]}, {"category": "backpack", "bbox": [0.433, 0.429, 0.742, 0.856]}, {"category": "suitcase", "bbox": [0.059, 0.414, 0.453, 0.841]}, {"category": "handbag", "bbox": [0.19, 0.184, 0.779, 0.475]}, {"category": "suitcase", "bbox": [0.175, 0.204, 0.583, 0.462]}]}
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+{"id": "000000399452", "image": "000000399452.jpg", "captions": ["a sandwhich sitting on a plate next to a glass of tea, bowl of soup", "a sandwich on a white plate a drink on a brown table", "A sandwich and chips sit on a white plate.", "a large plate of food with a glass of soda by it", "A sandwich sitting on top of a white plate next to a cup of coffee."], "instances": [{"category": "sandwich", "bbox": [0.175, 0.326, 0.605, 0.71]}, {"category": "cup", "bbox": [0.504, 0.024, 0.687, 0.419]}, {"category": "knife", "bbox": [0.742, 0.283, 0.857, 0.376]}, {"category": "spoon", "bbox": [0.618, 0.46, 0.797, 0.809]}, {"category": "fork", "bbox": [0.684, 0.254, 0.805, 0.395]}, {"category": "bowl", "bbox": [0.782, 0.366, 1.0, 0.62]}, {"category": "chair", "bbox": [0.202, 0.0, 0.671, 0.148]}, {"category": "dining table", "bbox": [0.002, 0.126, 0.996, 0.987]}]}
+{"id": "000000515716", "image": "000000515716.jpg", "captions": ["A couple of women standing on either side of a man wearing glasses.", "Two women and a man are holding glasses up at a wine tasting.", "Three young adults holding wine glasses while standing at a bar.", "A group of people sit holding glasses and smiling at a table with several bottles.", "A group of people at a celebration having a taste of wine."], "instances": [{"category": "bottle", "bbox": [0.529, 0.604, 0.637, 0.908]}, {"category": "bottle", "bbox": [0.379, 0.398, 0.481, 0.892]}, {"category": "bottle", "bbox": [0.942, 0.464, 0.988, 0.653]}, {"category": "person", "bbox": [0.0, 0.126, 0.136, 0.811]}, {"category": "person", "bbox": [0.05, 0.093, 0.211, 0.471]}, {"category": "person", "bbox": [0.401, 0.031, 0.678, 0.683]}, {"category": "person", "bbox": [0.617, 0.191, 0.94, 0.858]}, {"category": "person", "bbox": [0.723, 0.098, 0.947, 0.564]}, {"category": "wine glass", "bbox": [0.634, 0.434, 0.697, 0.628]}, {"category": "wine glass", "bbox": [0.285, 0.346, 0.372, 0.558]}, {"category": "wine glass", "bbox": [0.522, 0.422, 0.583, 0.544]}, {"category": "handbag", "bbox": [0.704, 0.601, 1.0, 0.916]}, {"category": "person", "bbox": [0.944, 0.319, 0.999, 0.604]}, {"category": "bottle", "bbox": [0.921, 0.46, 0.953, 0.636]}, {"category": "person", "bbox": [0.116, 0.171, 0.41, 0.829]}]}
+{"id": "000000116173", "image": "000000116173.jpg", "captions": ["The boy is on his surfboard in the water riding it.", "a young boy riding a boogie board in the water", "A boy riding surf board in the ocean.", "A young boy is riding a surfboard on a small wave.", "A young boy is surfing in the ocean."], "instances": [{"category": "person", "bbox": [0.485, 0.238, 0.702, 0.821]}, {"category": "person", "bbox": [0.866, 0.223, 0.921, 0.29]}, {"category": "person", "bbox": [0.752, 0.146, 0.775, 0.188]}, {"category": "surfboard", "bbox": [0.239, 0.758, 0.782, 0.846]}, {"category": "surfboard", "bbox": [0.853, 0.277, 0.981, 0.29]}, {"category": "surfboard", "bbox": [0.727, 0.169, 0.801, 0.198]}, {"category": "person", "bbox": [0.637, 0.194, 0.677, 0.261]}]}
+{"id": "000000186013", "image": "000000186013.jpg", "captions": ["A beach scene includes many different kites flying in a cloudy sky.", "Kites being flown at the beach at twilight.", "A beach with flags in the ground and kites overhead in the sky.", "A beach with rows of flags in the sand and kites flying overhead.", "A beach filled with kites and wind sails next to the ocean."], "instances": [{"category": "kite", "bbox": [0.174, 0.4, 0.351, 0.483]}, {"category": "kite", "bbox": [0.144, 0.13, 0.273, 0.17]}, {"category": "kite", "bbox": [0.236, 0.269, 0.268, 0.294]}, {"category": "kite", "bbox": [0.464, 0.204, 0.598, 0.271]}, {"category": "kite", "bbox": [0.61, 0.304, 0.659, 0.342]}, {"category": "kite", "bbox": [0.545, 0.435, 0.565, 0.452]}, {"category": "kite", "bbox": [0.027, 0.558, 0.151, 0.59]}, {"category": "kite", "bbox": [0.93, 0.429, 0.973, 0.536]}, {"category": "kite", "bbox": [0.684, 0.36, 0.697, 0.374]}, {"category": "surfboard", "bbox": [0.393, 0.627, 0.446, 0.934]}, {"category": "person", "bbox": [0.959, 0.685, 0.984, 0.713]}, {"category": "person", "bbox": [0.919, 0.681, 0.94, 0.725]}, {"category": "person", "bbox": [0.8, 0.597, 0.805, 0.61]}, {"category": "person", "bbox": [0.079, 0.928, 0.116, 0.975]}, {"category": "kite", "bbox": [0.743, 0.307, 0.755, 0.319]}, {"category": "kite", "bbox": [0.78, 0.322, 0.795, 0.335]}, {"category": "kite", "bbox": [0.536, 0.526, 0.597, 0.617]}, {"category": "person", "bbox": [0.941, 0.694, 0.961, 0.726]}, {"category": "kite", "bbox": [0.575, 0.446, 0.594, 0.471]}]}
+{"id": "000000015029", "image": "000000015029.jpg", "captions": ["A man holding a white frisbee standing on top of a field.", "A man is playing frisbee next to a tent.", "Guy at the park holding a frisbee with people in the back under a tent", "A man is holding a Frisbee standing in the grass.", "Young adult male holding a frisbee at an event."], "instances": [{"category": "frisbee", "bbox": [0.138, 0.359, 0.215, 0.587]}, {"category": "person", "bbox": [0.16, 0.002, 0.726, 0.995]}, {"category": "person", "bbox": [0.81, 0.73, 0.852, 0.825]}, {"category": "person", "bbox": [0.786, 0.749, 0.833, 0.814]}, {"category": "person", "bbox": [0.847, 0.743, 0.89, 0.804]}, {"category": "person", "bbox": [0.614, 0.749, 0.706, 0.936]}]}
+{"id": "000000500565", "image": "000000500565.jpg", "captions": ["A woman holding a child wrapped in a towel brushing her teeth.", "A woman is holding a baby who is wrapped in a towel and holding a toothbrush", "A woman holding a little boy who is brushing his teeth.", "A baby with a toothbrush in his mouth while being held by a woman", "a close up of an adult holding a child brushing their teeth"], "instances": [{"category": "toothbrush", "bbox": [0.586, 0.66, 0.754, 0.821]}, {"category": "person", "bbox": [0.002, 0.007, 0.637, 0.991]}, {"category": "person", "bbox": [0.357, 0.196, 0.998, 0.984]}]}
+{"id": "000000297323", "image": "000000297323.jpg", "captions": ["Two buses are parked against a curb in front of a building.", "Two automobiles parked on the side of a building.", "two tourist buses parked on street in front of old industrial building", "Two unique city buses stopped at a stop sign.", "Buses parked outside by a building and stop sign."], "instances": [{"category": "bus", "bbox": [0.7, 0.711, 0.92, 0.881]}, {"category": "person", "bbox": [0.936, 0.771, 0.972, 0.833]}, {"category": "stop sign", "bbox": [0.237, 0.666, 0.285, 0.728]}, {"category": "bus", "bbox": [0.334, 0.71, 0.678, 0.935]}, {"category": "truck", "bbox": [0.335, 0.72, 0.683, 0.934]}, {"category": "person", "bbox": [0.34, 0.791, 0.367, 0.834]}]}
+{"id": "000000441147", "image": "000000441147.jpg", "captions": ["Two antique suitcases sit stacked one on top of the other.", "Two suitcases are stacked on each other and one is black while the other is brown and yellow.", "a close up of two luggage suit cases stacked on each other", "A stack of antique luggage is displayed with price tags.", "two suitcases made of leather and stacked on top of each other"], "instances": [{"category": "suitcase", "bbox": [0.167, 0.025, 0.989, 0.445]}, {"category": "suitcase", "bbox": [0.002, 0.31, 0.994, 0.996]}]}
+{"id": "000000353536", "image": "000000353536.jpg", "captions": ["A table topped with plates and glasses with eating utensils..", "a fork is laying on a small white plate", "dirty dishes on a table, and a bottle of something.", "a table top with some dishes on top of it", "A table full of dirty dishes is pictured in this image."], "instances": [{"category": "dining table", "bbox": [0.0, 0.007, 0.998, 0.988]}, {"category": "bottle", "bbox": [0.554, 0.002, 0.768, 0.411]}, {"category": "cup", "bbox": [0.372, 0.011, 0.544, 0.427]}, {"category": "fork", "bbox": [0.442, 0.464, 0.818, 0.572]}, {"category": "fork", "bbox": [0.089, 0.233, 0.272, 0.456]}, {"category": "spoon", "bbox": [0.144, 0.218, 0.326, 0.413]}, {"category": "cup", "bbox": [0.688, 0.056, 0.812, 0.361]}]}
+{"id": "000000416256", "image": "000000416256.jpg", "captions": ["A cat laying on the floor next to a keyboard.", "an orange and white cat is laying next to a keyboard and some wires", "A cat is laying next to a computer keyboard.", "a cat laying on a floor next to a keyboard", "A CAT LAYING ON THE FLOOR AMIDST A COMPUTER,SPEAKERS,CORDS"], "instances": [{"category": "cat", "bbox": [0.235, 0.23, 0.737, 0.639]}, {"category": "keyboard", "bbox": [0.243, 0.562, 0.631, 0.836]}, {"category": "keyboard", "bbox": [0.058, 0.33, 0.277, 0.608]}]}
+{"id": "000000214367", "image": "000000214367.jpg", "captions": ["Wood shading on the side of a window with brick siding.", "A tree filled with lots of red fruit near a building.", "By the window outside is a apple tree, where the apples are ready to be picked.", "Some very nice looking red fruity by a window,", "A shuttered window has a fruit tree outside it."], "instances": [{"category": "apple", "bbox": [0.214, 0.112, 0.408, 0.266]}, {"category": "apple", "bbox": [0.472, 0.166, 0.618, 0.293]}, {"category": "apple", "bbox": [0.055, 0.592, 0.172, 0.686]}, {"category": "apple", "bbox": [0.126, 0.661, 0.236, 0.739]}, {"category": "apple", "bbox": [0.52, 0.09, 0.609, 0.143]}, {"category": "apple", "bbox": [0.226, 0.354, 0.285, 0.409]}, {"category": "apple", "bbox": [0.0, 0.698, 0.096, 0.771]}, {"category": "apple", "bbox": [0.001, 0.646, 0.042, 0.713]}, {"category": "apple", "bbox": [0.258, 0.719, 0.329, 0.778]}]}
+{"id": "000000210299", "image": "000000210299.jpg", "captions": ["A little boy riding his bike and wearing a helmet", "A little boy raveling down a road on a bike, with a yellow helmet on.", "The boy wears a helmet while riding his bicycle.", "a small child wearing a helmet and riding a bike", "A little boy wearing a helmet and riding a bike."], "instances": [{"category": "person", "bbox": [0.198, 0.259, 0.399, 0.679]}, {"category": "bicycle", "bbox": [0.213, 0.383, 0.408, 0.835]}]}
+{"id": "000000088218", "image": "000000088218.jpg", "captions": ["Signs proclaim the famous Haight Ashbury intersection and district.", "a pole with street lights, signs and wires attached to it", "A traffic light at the intersection of Haight and Ashbury", "A traffic sign is shown with traffic signs above it.", "The street signs and traffic signal are below wires attached to the pole."], "instances": [{"category": "traffic light", "bbox": [0.443, 0.435, 0.658, 0.721]}]}
+{"id": "000000020650", "image": "000000020650.jpg", "captions": ["Burger with broccoli, pickle, and fork on orange plate", "On a plate is kept a burger and a bowl of broccoli and a fork.", "There is half a sandwich on an orange plate with a pickle and a bowl of broccoli", "A A bowl and a sandwich on an orange plate on a table.", "A plate has a sandwich, broccoli, and a pickle."], "instances": [{"category": "sandwich", "bbox": [0.436, 0.155, 0.805, 0.859]}, {"category": "sandwich", "bbox": [0.311, 0.006, 0.748, 0.293]}, {"category": "fork", "bbox": [0.0, 0.665, 0.578, 0.876]}, {"category": "bowl", "bbox": [0.002, 0.263, 0.487, 0.744]}, {"category": "bowl", "bbox": [0.708, 0.003, 0.828, 0.03]}, {"category": "broccoli", "bbox": [0.185, 0.288, 0.366, 0.546]}, {"category": "broccoli", "bbox": [0.017, 0.344, 0.384, 0.654]}, {"category": "broccoli", "bbox": [0.31, 0.191, 0.466, 0.463]}, {"category": "broccoli", "bbox": [0.104, 0.107, 0.285, 0.342]}, {"category": "broccoli", "bbox": [0.092, 0.276, 0.242, 0.442]}, {"category": "dining table", "bbox": [0.002, 0.0, 0.999, 0.987]}]}
+{"id": "000000514915", "image": "000000514915.jpg", "captions": ["A large black dog laying on a kitchen floor.", "A dog is laying down on the floor in the home.", "Black dog laying down on the kitchen floor next to it's bowls and toy", "A black dog with a red collar laying on a tiled floor.", "A black dog that is laying on the floor."], "instances": [{"category": "dog", "bbox": [0.087, 0.276, 0.812, 0.792]}, {"category": "bowl", "bbox": [0.437, 0.09, 0.533, 0.213]}, {"category": "bowl", "bbox": [0.537, 0.035, 0.665, 0.141]}]}
+{"id": "000000205183", "image": "000000205183.jpg", "captions": ["A duck walking along a paved road next to a patch of grass.", "A close up of a duck walking on a path.", "a duck walks along a cement patch while looking down", "A white duck out of water, walking on the ground.", "A goose standing in the road, looking at the ground."], "instances": [{"category": "bird", "bbox": [0.291, 0.235, 0.859, 0.889]}]}
+{"id": "000000534270", "image": "000000534270.jpg", "captions": ["Man and woman with umbrella hats sitting on top of a bridge.", "A couple equipped with umbrella hats taking a break from walking their dog on a bridge on a rainy day.", "Two people in ridiculous looking umbrella hats.", "two people with umbrella hats near one another", "A couple of people wearing umbrella hats next to the ocean."], "instances": [{"category": "dog", "bbox": [0.456, 0.832, 0.6, 0.983]}, {"category": "person", "bbox": [0.433, 0.464, 0.636, 0.975]}, {"category": "person", "bbox": [0.263, 0.321, 0.459, 0.978]}, {"category": "boat", "bbox": [0.912, 0.4, 0.978, 0.433]}, {"category": "boat", "bbox": [0.211, 0.236, 0.478, 0.304]}, {"category": "boat", "bbox": [0.144, 0.328, 0.189, 0.361]}, {"category": "umbrella", "bbox": [0.443, 0.402, 0.607, 0.473]}, {"category": "umbrella", "bbox": [0.325, 0.311, 0.483, 0.432]}, {"category": "umbrella", "bbox": [0.207, 0.738, 0.284, 0.778]}, {"category": "umbrella", "bbox": [0.489, 0.713, 0.649, 0.83]}]}
+{"id": "000000408439", "image": "000000408439.jpg", "captions": ["Cliffs rise on the edge of a placid lake.", "A scenic view of a river with a train on the edge of it in the distance.", "A large lake surrounded by beautiful tree covered mountains.", "a landscape scene with water, mountains and trees", "A train on a waterfront track surrounded by mountains."], "instances": [{"category": "train", "bbox": [0.008, 0.591, 0.562, 0.644]}]}
+{"id": "000000474253", "image": "000000474253.jpg", "captions": ["A man riding on the back of a horse through a river.", "A person is riding a horse through water.", "Horse and rider crossing waterway during competitive event.", "A woman riding a horse splashes through a large puddle.", "A young man riding a horse through some water."], "instances": [{"category": "horse", "bbox": [0.385, 0.235, 0.651, 0.814]}, {"category": "person", "bbox": [0.396, 0.06, 0.576, 0.675]}, {"category": "person", "bbox": [0.29, 0.148, 0.355, 0.333]}, {"category": "person", "bbox": [0.129, 0.163, 0.212, 0.349]}, {"category": "person", "bbox": [0.005, 0.014, 0.038, 0.165]}, {"category": "person", "bbox": [0.144, 0.011, 0.193, 0.155]}, {"category": "person", "bbox": [0.089, 0.007, 0.133, 0.162]}]}
+{"id": "000000098029", "image": "000000098029.jpg", "captions": ["a table with many plates on it with a bread basket", "A table set for four has many foods and fruits on it.", "Several objects displayed on a kitchen table including bread, oranges and plating.", "Several dishes and food items sit on a table.", "An assortment of foods sitting on a round brown table."], "instances": [{"category": "refrigerator", "bbox": [0.013, 0.004, 0.37, 0.317]}, {"category": "bottle", "bbox": [0.467, 0.517, 0.555, 0.638]}, {"category": "bottle", "bbox": [0.602, 0.536, 0.658, 0.609]}, {"category": "chair", "bbox": [0.747, 0.367, 1.0, 0.592]}, {"category": "chair", "bbox": [0.044, 0.368, 0.358, 0.544]}, {"category": "cup", "bbox": [0.296, 0.465, 0.359, 0.54]}, {"category": "cup", "bbox": [0.709, 0.67, 0.782, 0.736]}, {"category": "cup", "bbox": [0.213, 0.684, 0.294, 0.753]}, {"category": "knife", "bbox": [0.787, 0.699, 0.922, 0.797]}, {"category": "knife", "bbox": [0.161, 0.539, 0.265, 0.584]}, {"category": "spoon", "bbox": [0.813, 0.674, 0.922, 0.759]}, {"category": "spoon", "bbox": [0.156, 0.555, 0.233, 0.587]}, {"category": "spoon", "bbox": [0.596, 0.467, 0.613, 0.509]}, {"category": "bowl", "bbox": [0.241, 0.753, 0.505, 0.935]}, {"category": "banana", "bbox": [0.632, 0.138, 0.718, 0.161]}, {"category": "apple", "bbox": [0.701, 0.152, 0.758, 0.191]}, {"category": "orange", "bbox": [0.607, 0.66, 0.692, 0.716]}, {"category": "orange", "bbox": [0.565, 0.636, 0.611, 0.667]}, {"category": "orange", "bbox": [0.526, 0.624, 0.572, 0.652]}, {"category": "orange", "bbox": [0.61, 0.628, 0.656, 0.657]}, {"category": "orange", "bbox": [0.599, 0.649, 0.643, 0.677]}, {"category": "dining table", "bbox": [0.013, 0.439, 0.964, 0.986]}, {"category": "cup", "bbox": [0.612, 0.489, 0.669, 0.548]}, {"category": "knife", "bbox": [0.605, 0.457, 0.638, 0.53]}, {"category": "apple", "bbox": [0.502, 0.137, 0.537, 0.159]}, {"category": "orange", "bbox": [0.54, 0.135, 0.563, 0.151]}, {"category": "orange", "bbox": [0.527, 0.129, 0.554, 0.142]}, {"category": "orange", "bbox": [0.611, 0.155, 0.641, 0.171]}, {"category": "chair", "bbox": [0.0, 0.843, 0.29, 0.989]}, {"category": "cup", "bbox": [0.353, 0.469, 0.411, 0.511]}, {"category": "cup", "bbox": [0.609, 0.716, 0.682, 0.786]}, {"category": "orange", "bbox": [0.638, 0.158, 0.679, 0.177]}, {"category": "cake", "bbox": [0.38, 0.821, 0.481, 0.895]}, {"category": "chair", "bbox": [0.79, 0.747, 1.0, 1.0]}, {"category": "bottle", "bbox": [0.719, 0.55, 0.769, 0.616]}, {"category": "bottle", "bbox": [0.795, 0.546, 0.873, 0.613]}, {"category": "knife", "bbox": [0.17, 0.799, 0.264, 0.88]}, {"category": "cup", "bbox": [0.317, 0.695, 0.391, 0.752]}]}
+{"id": "000000294073", "image": "000000294073.jpg", "captions": ["A woman and a man standing between two brown horses.", "A COUPLE WEARING YELLOW DRESS STANDING NEAR TWO HORSES.", "An older couple stands between two horses.", "A man and a woman standing with two horses", "A man and a woman stand in between two horses."], "instances": [{"category": "horse", "bbox": [0.0, 0.052, 0.49, 0.989]}, {"category": "horse", "bbox": [0.632, 0.23, 1.0, 0.989]}, {"category": "person", "bbox": [0.425, 0.326, 0.696, 0.987]}, {"category": "person", "bbox": [0.627, 0.203, 0.828, 0.986]}, {"category": "book", "bbox": [0.525, 0.597, 0.644, 0.833]}]}
+{"id": "000000203629", "image": "000000203629.jpg", "captions": ["A man on a cell phone in a public area holding his thumb up.", "A group of people gathered inside of a room.", "A man on his cellphone posing for a  picture.", "A man giving a thumbs up while on a cell phone.", "The man is giving a thumbs up while on his phone."], "instances": [{"category": "cell phone", "bbox": [0.43, 0.459, 0.449, 0.503]}, {"category": "cup", "bbox": [0.756, 0.838, 0.865, 0.98]}, {"category": "person", "bbox": [0.232, 0.317, 0.603, 0.98]}, {"category": "person", "bbox": [0.602, 0.405, 1.0, 0.999]}, {"category": "person", "bbox": [0.003, 0.339, 0.313, 0.987]}, {"category": "person", "bbox": [0.164, 0.379, 0.258, 0.733]}, {"category": "person", "bbox": [0.564, 0.36, 0.673, 0.645]}, {"category": "person", "bbox": [0.241, 0.379, 0.336, 0.512]}, {"category": "person", "bbox": [0.682, 0.372, 0.736, 0.502]}, {"category": "person", "bbox": [0.654, 0.428, 0.734, 0.536]}, {"category": "person", "bbox": [0.718, 0.368, 0.787, 0.508]}, {"category": "person", "bbox": [0.148, 0.362, 0.205, 0.529]}, {"category": "person", "bbox": [0.001, 0.431, 0.044, 0.564]}, {"category": "cup", "bbox": [0.901, 0.808, 0.995, 0.982]}]}
+{"id": "000000119876", "image": "000000119876.jpg", "captions": ["A man dressed loudly is using his cell phone.", "A man talking on the phone while he walks down the street.", "A man with pink hair talking on a cell phone.", "A man in a purple shirt and tie and purple hair.", "a man colored his hair in purple walking on the road"], "instances": [{"category": "bicycle", "bbox": [0.525, 0.222, 0.924, 0.608]}, {"category": "bicycle", "bbox": [0.895, 0.249, 1.0, 0.642]}, {"category": "person", "bbox": [0.0, 0.0, 0.738, 1.0]}, {"category": "tie", "bbox": [0.319, 0.255, 0.423, 0.638]}, {"category": "cell phone", "bbox": [0.411, 0.13, 0.426, 0.161]}, {"category": "handbag", "bbox": [0.369, 0.205, 0.575, 0.839]}]}
+{"id": "000000164255", "image": "000000164255.jpg", "captions": ["An umbrella that is standing in the sand.", "An umbrella is stuck in the sand on the beach.", "a colorful striped umbrella on the beach near the ocean", "A colorful umbrella is set up at the beach.", "The colorful umbrella is sitting by the beach,"], "instances": [{"category": "umbrella", "bbox": [0.0, 0.101, 0.567, 0.575]}]}
+{"id": "000000192817", "image": "000000192817.jpg", "captions": ["A view from a window high up in the sky.", "A bunch of mountains seen from a plane window.", "The window from a plane overlooking the ground.", "The view of a mountain area from an airplane window.", "An aerial view of mountains and lakes from an airplane window."], "instances": []}
+{"id": "000000258285", "image": "000000258285.jpg", "captions": ["Two large passenger jets flying over a beach filled with birds.", "A plane is flying over a bird filed lake", "Two airplanes are in the sky over blue water.", "An airplane landing over an airplane on the ground.", "A photo of two plans with water and birds surrounding it , one plane in the air one one the ground."], "instances": [{"category": "bird", "bbox": [0.507, 0.941, 0.536, 0.973]}, {"category": "bird", "bbox": [0.304, 0.933, 0.315, 0.95]}, {"category": "bird", "bbox": [0.129, 0.885, 0.143, 0.912]}, {"category": "bird", "bbox": [0.158, 0.851, 0.165, 0.87]}, {"category": "bird", "bbox": [0.404, 0.839, 0.429, 0.864]}, {"category": "bird", "bbox": [0.498, 0.833, 0.513, 0.861]}, {"category": "airplane", "bbox": [0.276, 0.085, 0.825, 0.316]}, {"category": "airplane", "bbox": [0.478, 0.252, 0.983, 0.495]}, {"category": "bird", "bbox": [0.552, 0.828, 0.564, 0.844]}, {"category": "bird", "bbox": [0.789, 0.812, 0.798, 0.836]}, {"category": "bird", "bbox": [0.927, 0.82, 0.936, 0.838]}, {"category": "bird", "bbox": [0.65, 0.828, 0.664, 0.849]}, {"category": "bird", "bbox": [0.752, 0.81, 0.763, 0.83]}, {"category": "bird", "bbox": [0.841, 0.817, 0.852, 0.828]}, {"category": "bird", "bbox": [0.292, 0.849, 0.311, 0.868]}, {"category": "bird", "bbox": [0.005, 0.727, 0.981, 0.998]}]}
+{"id": "000000506483", "image": "000000506483.jpg", "captions": ["An art installation is placed by a street.", "People sit near a display of large artworks including an oversize bench and painted feline heads.", "Looking down on a giant rocking bench and large animal heads.", "An over sized wooden bench next to two massive animal art sculptures.", "artistic sculptures and images on a city street"], "instances": [{"category": "car", "bbox": [0.656, 0.939, 0.933, 1.0]}, {"category": "person", "bbox": [0.08, 0.664, 0.147, 0.805]}, {"category": "person", "bbox": [0.154, 0.646, 0.217, 0.821]}, {"category": "bench", "bbox": [0.316, 0.124, 0.951, 0.635]}, {"category": "backpack", "bbox": [0.062, 0.701, 0.097, 0.769]}, {"category": "person", "bbox": [0.0, 0.132, 0.031, 0.197]}]}
+{"id": "000000502168", "image": "000000502168.jpg", "captions": ["a fleet of naval ships in the ocean", "A group of men on aircraft carrier with other boats in the distance.", "A large ship floating in the ocean next to other ships.", "Several men on a boat looking over the side.", "The men wear hardhats as they work on the aircraft carrier."], "instances": [{"category": "boat", "bbox": [0.634, 0.292, 1.0, 0.982]}, {"category": "person", "bbox": [0.675, 0.507, 0.736, 0.731]}, {"category": "person", "bbox": [0.684, 0.737, 0.817, 1.0]}, {"category": "person", "bbox": [0.803, 0.691, 0.883, 0.932]}, {"category": "person", "bbox": [0.741, 0.56, 0.798, 0.767]}, {"category": "person", "bbox": [0.924, 0.269, 0.951, 0.367]}, {"category": "boat", "bbox": [0.079, 0.171, 0.172, 0.231]}, {"category": "boat", "bbox": [0.863, 0.131, 0.961, 0.239]}, {"category": "boat", "bbox": [0.435, 0.288, 0.46, 0.313]}, {"category": "boat", "bbox": [0.591, 0.186, 0.605, 0.222]}, {"category": "person", "bbox": [0.451, 0.289, 0.455, 0.296]}, {"category": "person", "bbox": [0.446, 0.29, 0.451, 0.296]}, {"category": "person", "bbox": [0.872, 0.627, 0.957, 0.966]}, {"category": "person", "bbox": [0.44, 0.288, 0.446, 0.3]}]}
+{"id": "000000319432", "image": "000000319432.jpg", "captions": ["Man holding two shirts with luggage and window", "A man holding clothes on a hanger with a suitcase in front of him.", "A man show a red and a white clothing hangers.", "A man holding his garment bags in both hands", "A man holding up some clothes in some hanger bags."], "instances": [{"category": "person", "bbox": [0.0, 0.092, 0.776, 0.852]}, {"category": "suitcase", "bbox": [0.153, 0.798, 0.587, 1.0]}]}
+{"id": "000000131019", "image": "000000131019.jpg", "captions": ["Two zebras and two monkeys walking on the grass.", "Two giraffes and another animal are on green grass.", "A baboon and two zebras grazing on the savannah.", "A baboon and its baby eat by two zebras in the grass", "Monkey standing behind two zebras as they graze."], "instances": [{"category": "zebra", "bbox": [0.367, 0.258, 0.834, 0.646]}, {"category": "zebra", "bbox": [0.161, 0.13, 0.396, 0.375]}, {"category": "bird", "bbox": [0.309, 0.138, 0.34, 0.163]}]}
diff --git a/llava/eval/table/model.jsonl b/llava/eval/table/model.jsonl
new file mode 100644
index 0000000..61b6de1
--- /dev/null
+++ b/llava/eval/table/model.jsonl
@@ -0,0 +1,5 @@
+{"model_id": "vicuna-13b:20230322-clean-lang", "model_name": "vicuna-13b", "model_version": "20230322-clean-lang", "model_metadata": "vicuna-13b-20230322-clean-lang"}
+{"model_id": "alpaca-13b:v1", "model_name": "alpaca-13b", "model_version": "v1", "model_metadata": "alpaca-13b"}
+{"model_id": "llama-13b:v1", "model_name": "llama-13b", "model_version": "v1", "model_metadata": "hf-llama-13b"}
+{"model_id": "bard:20230327", "model_name": "bard", "model_version": "20230327", "model_metadata": "Google Bard 20230327"}
+{"model_id": "gpt-3.5-turbo:20230327", "model_name": "gpt-3.5-turbo", "model_version": "20230327", "model_metadata": "OpenAI ChatGPT gpt-3.5-turbo Chat Completion"}
diff --git a/llava/eval/table/prompt.jsonl b/llava/eval/table/prompt.jsonl
new file mode 100644
index 0000000..a46845a
--- /dev/null
+++ b/llava/eval/table/prompt.jsonl
@@ -0,0 +1,4 @@
+{"prompt_id": 1, "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer.", "prompt_template": "[Question]\n{question}\n\n[Assistant 1]\n{answer_1}\n\n[End of Assistant 1]\n\n[Assistant 2]\n{answer_2}\n\n[End of Assistant 2]\n\n[System]\n{prompt}\n\n", "defaults": {"prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above.\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}, "description": "Prompt for general questions"}
+{"prompt_id": 2, "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer.", "prompt_template": "[Question]\n{question}\n\n[Assistant 1]\n{answer_1}\n\n[End of Assistant 1]\n\n[Assistant 2]\n{answer_2}\n\n[End of Assistant 2]\n\n[System]\n{prompt}\n\n", "defaults": {"prompt": "Your task is to evaluate the coding abilities of the above two assistants. They have been asked to implement a program to solve a given problem. Please review their code submissions, paying close attention to their problem-solving approach, code structure, readability, and the inclusion of helpful comments.\n\nPlease ensure that the assistants' submissions:\n\n1. Correctly implement the given problem statement.\n2. Contain accurate and efficient code.\n3. Include clear and concise comments that explain the code's logic and functionality.\n4. Adhere to proper coding standards and best practices.\n\nOnce you have carefully reviewed both submissions, provide detailed feedback on their strengths and weaknesses, along with any suggestions for improvement. You should first output a single line containing two scores on the scale of 1-10 (1: no code/no sense; 10: perfect) for Assistant 1 and 2, respectively. Then give extra comments starting from the next line."}, "description": "Prompt for coding questions"}
+{"prompt_id": 3, "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer.", "prompt_template": "[Question]\n{question}\n\n[Assistant 1]\n{answer_1}\n\n[End of Assistant 1]\n\n[Assistant 2]\n{answer_2}\n\n[End of Assistant 2]\n\n[System]\n{prompt}\n\n", "defaults": {"prompt": "We would like to request your feedback on the mathematical proficiency of two AI assistants regarding the given user question.\nFirstly, please solve the problem independently, without referring to the answers provided by Assistant 1 and Assistant 2.\nAfterward, please examine the problem-solving process of Assistant 1 and Assistant 2 step-by-step to ensure their correctness, identifying any incorrect steps if present. Your evaluation should take into account not only the answer but also the problem-solving steps.\nFinally, please output a Python tuple containing two numerical scores for Assistant 1 and Assistant 2, ranging from 1 to 10, respectively. If applicable, explain the reasons for any variations in their scores and determine which assistant performed better."}, "description": "Prompt for math questions"}
+{"prompt_id": 4, "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer.", "prompt_template": "[Visual Context]\n{context}\n[Question]\n{question}\n\n[Assistant 1]\n{answer_1}\n\n[End of Assistant 1]\n\n[Assistant 2]\n{answer_2}\n\n[End of Assistant 2]\n\n[System]\n{prompt}\n\n", "defaults": {"prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with five descriptive sentences describing the same image and the bounding box coordinates of each object in the scene. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}, "description": "Prompt for visual questions"}
diff --git a/llava/eval/table/question.jsonl b/llava/eval/table/question.jsonl
new file mode 100644
index 0000000..c946b8f
--- /dev/null
+++ b/llava/eval/table/question.jsonl
@@ -0,0 +1,80 @@
+{"question_id": 1, "text": "How can I improve my time management skills?", "category": "generic"}
+{"question_id": 2, "text": "What are the most effective ways to deal with stress?", "category": "generic"}
+{"question_id": 3, "text": "What are the main differences between Python and JavaScript programming languages?", "category": "generic"}
+{"question_id": 4, "text": "How can I increase my productivity while working from home?", "category": "generic"}
+{"question_id": 5, "text": "Can you explain the basics of quantum computing?", "category": "generic"}
+{"question_id": 6, "text": "What are the differences between plant-based and animal-based protein sources?", "category": "generic"}
+{"question_id": 7, "text": "How can I develop my critical thinking skills?", "category": "generic"}
+{"question_id": 8, "text": "What are the major challenges faced by the education sector today?", "category": "generic"}
+{"question_id": 9, "text": "What are the primary factors that influence consumer behavior?", "category": "generic"}
+{"question_id": 10, "text": "What are the most effective strategies for conflict resolution in the workplace?", "category": "generic"}
+{"question_id": 11, "text": "What are some potential implications of using a single-use plastic bottle versus a reusable bottle on both the environment and human health?", "category": "knowledge"}
+{"question_id": 12, "text": "What factors would you consider when designing an inclusive and accessible public transportation system?", "category": "knowledge"}
+{"question_id": 13, "text": "How can governments utilize fiscal and monetary policies to combat economic recessions?", "category": "knowledge"}
+{"question_id": 14, "text": "How do language and cultural barriers affect the way people communicate and form relationships in multicultural societies?", "category": "knowledge"}
+{"question_id": 15, "text": "Describe a scenario where artificial intelligence could be used to improve the quality and efficiency of healthcare delivery.", "category": "knowledge"}
+{"question_id": 16, "text": "Explain the process of gene editing using CRISPR-Cas9 technology, and discuss its potential applications and ethical implications.", "category": "knowledge"}
+{"question_id": 17, "text": "How do vaccinations work to protect individuals and communities from infectious diseases, and what is herd immunity?", "category": "knowledge"}
+{"question_id": 18, "text": "How do social media platforms influence the way people consume and share news, and what are the potential implications for the spread of misinformation?", "category": "knowledge"}
+{"question_id": 19, "text": "How do cultural, social, and economic factors influence people's food choices, and how can this knowledge be used to promote healthier diets?", "category": "knowledge"}
+{"question_id": 20, "text": "Explain the process of natural selection and how it contributes to the evolution and adaptation of species.", "category": "knowledge"}
+{"question_id": 21, "text": "How would you introduce yourself as a medieval knight at a royal banquet?", "category": "roleplay"}
+{"question_id": 22, "text": "As a pirate captain, what would you say to your crew to motivate them to search for hidden treasure?", "category": "roleplay"}
+{"question_id": 23, "text": "If you were a Shakespearean character, how would you declare your love for someone in a soliloquy?", "category": "roleplay"}
+{"question_id": 24, "text": "As a superhero, how would you explain your origin story to a curious child?", "category": "roleplay"}
+{"question_id": 25, "text": "Imagine you are a time traveler from the year 3000. What technological advancements would you tell people about?", "category": "roleplay"}
+{"question_id": 26, "text": "As a sports commentator, describe the winning play in the final seconds of a championship game.", "category": "roleplay"}
+{"question_id": 27, "text": "Pretend to be a world-famous chef. How would you describe your signature dish to a panel of judges?", "category": "roleplay"}
+{"question_id": 28, "text": "You are a mountain climber reaching the summit of Mount Everest. Describe your emotions and the view from the top.", "category": "roleplay"}
+{"question_id": 29, "text": "As a space colonist on Mars, describe your daily life and the challenges you face living on another planet.", "category": "roleplay"}
+{"question_id": 30, "text": "Pretend to be a character in a post-apocalyptic world. Describe how you survive and the allies you encounter.", "category": "roleplay"}
+{"question_id": 31, "text": "How can you determine if a restaurant is popular among locals or mainly attracts tourists, and why might this information be useful?", "category": "common-sense"}
+{"question_id": 32, "text": "What are some subtle clues that suggest someone is pretending to understand a topic or conversation when they are actually confused or uninformed?", "category": "common-sense"}
+{"question_id": 33, "text": "Why might someone choose to use a paper map or ask for directions instead of relying on a GPS device or smartphone app?", "category": "common-sense"}
+{"question_id": 34, "text": "How can you determine if a person is genuinely interested in a conversation or simply being polite?", "category": "common-sense"}
+{"question_id": 35, "text": "Why might someone prefer to shop at a small, locally-owned business instead of a large chain store, even if the prices are higher?", "category": "common-sense"}
+{"question_id": 36, "text": "How can you assess the credibility of a source of information, such as a news article or blog post, without relying solely on the reputation of the author or publisher?", "category": "common-sense"}
+{"question_id": 37, "text": "Why do some people enjoy the sensation of being scared, such as by watching horror movies or going on roller coasters, while others avoid these experiences?", "category": "common-sense"}
+{"question_id": 38, "text": "How can observing the behavior of other people in a social situation provide clues about cultural norms and expectations?", "category": "common-sense"}
+{"question_id": 39, "text": "Do we have a moral obligation to explore space, or should we focus on solving Earth's problems first?", "category": "common-sense"}
+{"question_id": 40, "text": "In a world where automation is becoming increasingly prevalent, is it more important to prioritize job creation or technological progress?", "category": "common-sense"}
+{"question_id": 41, "text": "How many times does the average human blink in a lifetime? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
+{"question_id": 42, "text": "How many atoms are in a grain of salt? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
+{"question_id": 43, "text": "How many lightning strikes occur on Earth each day? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
+{"question_id": 44, "text": "How many balloons would it take to lift a house like in the movie \"Up\"? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
+{"question_id": 45, "text": "How many text messages are sent globally in a minute? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
+{"question_id": 46, "text": "How many words are spoken daily on Earth? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
+{"question_id": 47, "text": "How many snowflakes fall during a typical winter? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
+{"question_id": 48, "text": "How many pages are in all the books ever written? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
+{"question_id": 49, "text": "How many times has the Earth orbited the Sun since the beginning of life? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
+{"question_id": 50, "text": "How many songs have been recorded throughout history? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
+{"question_id": 51, "text": "What if the Internet had been invented during the Renaissance period?", "category": "counterfactual"}
+{"question_id": 52, "text": "What if the Aztecs had successfully repelled the Spanish conquistadors?", "category": "counterfactual"}
+{"question_id": 53, "text": "What if the Black Death had not occurred in the 14th century?", "category": "counterfactual"}
+{"question_id": 54, "text": "What if Isaac Newton had focused on biology instead of physics?", "category": "counterfactual"}
+{"question_id": 55, "text": "What if the Beatles had never formed as a band?", "category": "counterfactual"}
+{"question_id": 56, "text": "What if Alan Turing had not cracked the Enigma code during World War II?", "category": "counterfactual"}
+{"question_id": 57, "text": "What if the Suez Canal had never been constructed?", "category": "counterfactual"}
+{"question_id": 58, "text": "What if the Maya civilization had never mysteriously collapsed?", "category": "counterfactual"}
+{"question_id": 59, "text": "What if Christopher Columbus had not discovered the Americas?", "category": "counterfactual"}
+{"question_id": 60, "text": "What if Vincent van Gogh had been a successful artist during his lifetime?", "category": "counterfactual"}
+{"question_id": 61, "text": "Develop a C++ program that reads a text file line by line and counts the number of occurrences of a specific word in the file.", "category": "coding"}
+{"question_id": 62, "text": "Implement a Python function to find the longest common subsequence of two input strings using dynamic programming.", "category": "coding"}
+{"question_id": 63, "text": "Implement a regular expression in Python to validate an email address.", "category": "coding"}
+{"question_id": 64, "text": "Write a program to find the nth Fibonacci number using dynamic programming.", "category": "coding"}
+{"question_id": 65, "text": "Implement a binary search algorithm to find a specific element in a sorted array.", "category": "coding"}
+{"question_id": 66, "text": "Implement a queue data structure using two stacks in Python.", "category": "coding"}
+{"question_id": 67, "text": "Implement a program to find the common elements in two arrays without using any extra data structures.", "category": "coding"}
+{"question_id": 68, "text": "Given that f(x) = 5x^3 - 2x + 3, find the value of f(2).", "category": "math"}
+{"question_id": 69, "text": "Solve for x in the equation 3x + 10 = 5(x - 2).", "category": "math"}
+{"question_id": 70, "text": "If the endpoints of a line segment are (2, -2) and (10, 4), what is the length of the segment?", "category": "math"}
+{"question_id": 71, "text": "Can you help me write a formal email to a potential business partner proposing a joint venture?", "category": "writing"}
+{"question_id": 72, "text": "Can you help me write a resignation letter to my current employer, while leaving on good terms and expressing gratitude for the opportunities provided?", "category": "writing"}
+{"question_id": 73, "text": "Use an appropriate format to structure a formal letter of recommendation for a student applying to a prestigious graduate program in computer science.", "category": "writing"}
+{"question_id": 74, "text": "Write a compelling product launch announcement email to inform our customers of our new software solution.", "category": "writing"}
+{"question_id": 75, "text": "Draft an apology email to a customer who experienced a delay in their order, and provide reassurance that the issue has been resolved.", "category": "writing"}
+{"question_id": 76, "text": "Write a script for a YouTube video exploring the history and cultural significance of jazz.", "category": "writing"}
+{"question_id": 77, "text": "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions.", "category": "writing"}
+{"question_id": 78, "text": "Write a captivating movie review for a recently released science fiction film, discussing its plot, characters, and special effects.", "category": "writing"}
+{"question_id": 79, "text": "Structure a podcast script for an episode discussing the influence of streaming platforms on the music industry.", "category": "writing"}
+{"question_id": 80, "text": "Write a symphony concert review, discussing the orchestra's performance and overall audience experience.", "category": "writing"}
diff --git a/llava/eval/table/reviewer.jsonl b/llava/eval/table/reviewer.jsonl
new file mode 100644
index 0000000..e96127c
--- /dev/null
+++ b/llava/eval/table/reviewer.jsonl
@@ -0,0 +1,4 @@
+{"reviewer_id": "gpt-4-0328-default", "prompt_id": 1, "metadata": {"temperature": 0.2, "max_tokens": 1024}, "description": "GPT-4 for general questions"}
+{"reviewer_id": "gpt-4-0328-coding", "prompt_id": 2, "metadata": {"temperature": 0.2, "max_tokens": 1024}, "description": "GPT-4 for coding questions"}
+{"reviewer_id": "gpt-4-0328-math", "prompt_id": 3, "metadata": {"temperature": 0.2, "max_tokens": 1024}, "description": "GPT-4 for math questions"}
+{"reviewer_id": "gpt-4-0417-visual", "prompt_id": 4, "metadata": {"temperature": 0.2, "max_tokens": 1024}, "description": "GPT-4 for math questions"}
diff --git a/llava/eval/table/rule.json b/llava/eval/table/rule.json
new file mode 100644
index 0000000..26c7f4e
--- /dev/null
+++ b/llava/eval/table/rule.json
@@ -0,0 +1,11 @@
+{
+    "coding": {"role": "Assistant", "prompt": "Your task is to evaluate the coding abilities of the above two assistants. They have been asked to implement a program to solve a given problem. Please review their code submissions, paying close attention to their problem-solving approach, code structure, readability, and the inclusion of helpful comments.\n\nPlease ensure that the assistants' submissions:\n\n1. Correctly implement the given problem statement.\n2. Contain accurate and efficient code.\n3. Include clear and concise comments that explain the code's logic and functionality.\n4. Adhere to proper coding standards and best practices.\n\nOnce you have carefully reviewed both submissions, provide detailed feedback on their strengths and weaknesses, along with any suggestions for improvement. You should first output a single line containing two scores on the scale of 1-10 (1: no code/no sense; 10: perfect) for Assistant 1 and 2, respectively. Then give extra comments starting from the next line."},
+    "math":  {"role": "Assistant", "prompt": "We would like to request your feedback on the mathematical proficiency of two AI assistants regarding the given user question.\nFirstly, please solve the problem independently, without referring to the answers provided by Assistant 1 and Assistant 2.\nAfterward, please examine the problem-solving process of Assistant 1 and Assistant 2 step-by-step to ensure their correctness, identifying any incorrect steps if present. Your evaluation should take into account not only the answer but also the problem-solving steps.\nFinally, please output a Python tuple containing two numerical scores for Assistant 1 and Assistant 2, ranging from 1 to 10, respectively. If applicable, explain the reasons for any variations in their scores and determine which assistant performed better."},
+    "default":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above.\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."},
+    "conv":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with five descriptive sentences describing the same image and the bounding box coordinates of each object in the scene. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."},
+    "detail":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with five descriptive sentences describing the same image and the bounding box coordinates of each object in the scene. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."},
+    "complex":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with five descriptive sentences describing the same image and the bounding box coordinates of each object in the scene. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."},
+    "llava_bench_conv":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."},
+    "llava_bench_detail":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."},
+    "llava_bench_complex":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}
+}
\ No newline at end of file
diff --git a/llava/eval/video/eval_benchmark_1_correctness.py b/llava/eval/video/eval_benchmark_1_correctness.py
new file mode 100644
index 0000000..324be0b
--- /dev/null
+++ b/llava/eval/video/eval_benchmark_1_correctness.py
@@ -0,0 +1,191 @@
+import openai
+import os
+import argparse
+import json
+import ast
+from multiprocessing.pool import Pool
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="question-answer-generation-using-gpt-3")
+    parser.add_argument("--pred_path", required=True, help="The path to file containing prediction.")
+    parser.add_argument("--output_dir", required=True, help="The path to save annotation json files.")
+    parser.add_argument("--output_json", required=True, help="The path to save annotation final combined json file.")
+    parser.add_argument("--api_key", required=True, help="OpenAI API key.")
+    parser.add_argument("--api_base", default="", type=str, help="OpenAI API base.")
+    parser.add_argument("--num_tasks", required=True, type=int, help="Number of splits.")
+    args = parser.parse_args()
+    return args
+
+
+def annotate(prediction_set, caption_files, output_dir, args):
+    """
+    Evaluates question and answer pairs using GPT-3
+    Returns a score for correctness.
+    """
+    # Set the OpenAI API key.
+    openai.api_key = args.api_key
+    if args.api_base is not None:
+        openai.api_base = args.api_base
+    for file in caption_files:
+        key = file[:-5] # Strip file extension
+        qa_set = prediction_set[key]
+        question = qa_set['q']
+        answer = qa_set['a']
+        pred = qa_set['pred']
+        try:
+            # Compute the correctness score
+            completion = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {
+                        "role": "system",
+                        "content": 
+                            "You are an intelligent chatbot designed for evaluating the factual accuracy of generative outputs for video-based question-answer pairs. "
+                            "Your task is to compare the predicted answer with the correct answer and determine if they are factually consistent. Here's how you can accomplish the task:"
+                            "------"
+                            "##INSTRUCTIONS: "
+                            "- Focus on the factual consistency between the predicted answer and the correct answer. The predicted answer should not contain any misinterpretations or misinformation.\n"
+                            "- The predicted answer must be factually accurate and align with the video content.\n"
+                            "- Consider synonyms or paraphrases as valid matches.\n"
+                            "- Evaluate the factual accuracy of the prediction compared to the answer."
+                    },
+                    {
+                        "role": "user",
+                        "content":
+                            "Please evaluate the following video-based question-answer pair:\n\n"
+                            f"Question: {question}\n"
+                            f"Correct Answer: {answer}\n"
+                            f"Predicted Answer: {pred}\n\n"
+                            "Provide your evaluation only as a factual accuracy score where the factual accuracy score is an integer value between 0 and 5, with 5 indicating the highest level of factual consistency. "
+                            "Please generate the response in the form of a Python dictionary string with keys 'score', where its value is the factual accuracy score in INTEGER, not STRING."
+                            "DO NOT PROVIDE ANY OTHER OUTPUT TEXT OR EXPLANATION. Only provide the Python dictionary string. "
+                            "For example, your response should look like this: {''score': 4.8}."
+                    }
+                ]
+            )
+            # Convert response to a Python dictionary.
+            response_message = completion["choices"][0]["message"]["content"]
+            response_dict = ast.literal_eval(response_message)
+            result_qa_pair = [response_dict, qa_set]
+
+            # Save the question-answer pairs to a json file.
+            with open(f"{output_dir}/{key}.json", "w") as f:
+                json.dump(result_qa_pair, f)
+
+        except Exception as e:
+            print(f"Error processing file '{key}': {e}")
+
+
+def main():
+    """
+    Main function to control the flow of the program.
+    """
+    # Parse arguments.
+    args = parse_args()
+
+    file = open(args.pred_path)
+    pred_contents = json.load(file)
+
+    # Dictionary to store the count of occurrences for each video_id
+    video_id_counts = {}
+    new_pred_contents = []
+
+    # Iterate through each sample in pred_contents
+    for sample in pred_contents:
+        video_id = sample['video_name']
+        if video_id in video_id_counts:
+            video_id_counts[video_id] += 1
+        else:
+            video_id_counts[video_id] = 0
+
+        # Create a new sample with the modified key
+        new_sample = sample
+        new_sample['video_name'] = f"{video_id}_{video_id_counts[video_id]}"
+        new_pred_contents.append(new_sample)
+
+    # Generating list of id's and corresponding files
+    id_list = [x['video_name'] for x in new_pred_contents]
+    caption_files = [f"{id}.json" for id in id_list]
+
+    output_dir = args.output_dir
+    # Generate output directory if not exists.
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # Preparing dictionary of question-answer sets
+    prediction_set = {}
+    for sample in new_pred_contents:
+        id = sample['video_name']
+        question = sample['Q']
+        answer = sample['A']
+        pred = sample['pred']
+        qa_set = {"q": question, "a": answer, "pred": pred}
+        prediction_set[id] = qa_set
+
+    # Set the OpenAI API key.
+    openai.api_key = args.api_key
+    num_tasks = args.num_tasks
+
+    # While loop to ensure that all captions are processed.
+    while True:
+        try:
+            # Files that have not been processed yet.
+            completed_files = os.listdir(output_dir)
+            print(f"completed_files: {len(completed_files)}")
+
+            # Files that have not been processed yet.
+            incomplete_files = [f for f in caption_files if f not in completed_files]
+            print(f"incomplete_files: {len(incomplete_files)}")
+
+            # Break the loop when there are no incomplete files
+            if len(incomplete_files) == 0:
+                break
+            if len(incomplete_files) <= num_tasks:
+                num_tasks = 1
+
+            # Split tasks into parts.
+            part_len = len(incomplete_files) // num_tasks
+            all_parts = [incomplete_files[i:i + part_len] for i in range(0, len(incomplete_files), part_len)]
+            task_args = [(prediction_set, part, args.output_dir, args) for part in all_parts]
+
+            # Use a pool of workers to process the files in parallel.
+            with Pool() as pool:
+                pool.starmap(annotate, task_args)
+
+        except Exception as e:
+            print(f"Error: {e}")
+
+    # Combine all the processed files into one
+    combined_contents = {}
+    json_path = args.output_json
+
+    # Iterate through json files
+    for file_name in os.listdir(output_dir):
+        if file_name.endswith(".json"):
+            file_path = os.path.join(output_dir, file_name)
+            with open(file_path, "r") as json_file:
+                content = json.load(json_file)
+                combined_contents[file_name[:-5]] = content
+
+    # Write combined content to a json file
+    with open(json_path, "w") as json_file:
+        json.dump(combined_contents, json_file)
+    print("All evaluation completed!")
+
+    # Calculate average score
+    score_sum = 0
+    count = 0
+    for key, result in combined_contents.items():
+        count += 1
+        score_match = result[0]['score']
+        score = int(score_match)
+        score_sum += score
+    average_score = score_sum / count
+
+    print("Average score for correctness:", average_score)
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/llava/eval/video/eval_benchmark_2_detailed_orientation.py b/llava/eval/video/eval_benchmark_2_detailed_orientation.py
new file mode 100644
index 0000000..7a852ff
--- /dev/null
+++ b/llava/eval/video/eval_benchmark_2_detailed_orientation.py
@@ -0,0 +1,191 @@
+import openai
+import os
+import argparse
+import json
+import ast
+from multiprocessing.pool import Pool
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="question-answer-generation-using-gpt-3")
+    parser.add_argument("--pred_path", required=True, help="The path to file containing prediction.")
+    parser.add_argument("--output_dir", required=True, help="The path to save annotation json files.")
+    parser.add_argument("--output_json", required=True, help="The path to save annotation final combined json file.")
+    parser.add_argument("--api_key", required=True, help="OpenAI API key.")
+    parser.add_argument("--api_base", default="", type=str, help="OpenAI API base.")
+    parser.add_argument("--num_tasks", required=True, type=int, help="Number of splits.")
+    args = parser.parse_args()
+    return args
+
+
+def annotate(prediction_set, caption_files, output_dir, args):
+    """
+    Evaluates question and answer pairs using GPT-3 and
+    returns a score for detailed orientation.
+    """
+    # Set the OpenAI API key.
+    openai.api_key = args.api_key
+    if args.api_base is not None:
+        openai.api_base = args.api_base
+    for file in caption_files:
+        key = file[:-5] # Strip file extension
+        qa_set = prediction_set[key]
+        question = qa_set['q']
+        answer = qa_set['a']
+        pred = qa_set['pred']
+        try:
+            # Compute the detailed-orientation score
+            completion = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {
+                        "role": "system",
+                        "content":
+                            "You are an intelligent chatbot designed for evaluating the detail orientation of generative outputs for video-based question-answer pairs. "
+                            "Your task is to compare the predicted answer with the correct answer and determine its level of detail, considering both completeness and specificity. Here's how you can accomplish the task:"
+                            "------"
+                            "##INSTRUCTIONS: "
+                            "- Check if the predicted answer covers all major points from the video. The response should not leave out any key aspects.\n"
+                            "- Evaluate whether the predicted answer includes specific details rather than just generic points. It should provide comprehensive information that is tied to specific elements of the video.\n"
+                            "- Consider synonyms or paraphrases as valid matches.\n"
+                            "- Provide a single evaluation score that reflects the level of detail orientation of the prediction, considering both completeness and specificity."
+                    },
+                    {
+                        "role": "user",
+                        "content":
+                            "Please evaluate the following video-based question-answer pair:\n\n"
+                            f"Question: {question}\n"
+                            f"Correct Answer: {answer}\n"
+                            f"Predicted Answer: {pred}\n\n"
+                            "Provide your evaluation only as a detail orientation score where the detail orientation score is an integer value between 0 and 5, with 5 indicating the highest level of detail orientation. "
+                            "Please generate the response in the form of a Python dictionary string with keys 'score', where its value is the detail orientation score in INTEGER, not STRING."
+                            "DO NOT PROVIDE ANY OTHER OUTPUT TEXT OR EXPLANATION. Only provide the Python dictionary string. "
+                            "For example, your response should look like this: {''score': 4.8}."
+                    }
+                ]
+            )
+            # Convert response to a Python dictionary.
+            response_message = completion["choices"][0]["message"]["content"]
+            response_dict = ast.literal_eval(response_message)
+            result_qa_pair = [response_dict, qa_set]
+
+            # Save the question-answer pairs to a json file.
+            with open(f"{output_dir}/{key}.json", "w") as f:
+                json.dump(result_qa_pair, f)
+
+        except Exception as e:
+            print(f"Error processing file '{key}': {e}")
+
+
+def main():
+    """
+    Main function to control the flow of the program.
+    """
+    # Parse arguments.
+    args = parse_args()
+
+    file = open(args.pred_path)
+    pred_contents = json.load(file)
+
+    # Dictionary to store the count of occurrences for each video_id
+    video_id_counts = {}
+    new_pred_contents = []
+
+    # Iterate through each sample in pred_contents
+    for sample in pred_contents:
+        video_id = sample['video_name']
+        if video_id in video_id_counts:
+            video_id_counts[video_id] += 1
+        else:
+            video_id_counts[video_id] = 0
+
+        # Create a new sample with the modified key
+        new_sample = sample
+        new_sample['video_name'] = f"{video_id}_{video_id_counts[video_id]}"
+        new_pred_contents.append(new_sample)
+
+    # Generating list of id's and corresponding files
+    id_list = [x['video_name'] for x in new_pred_contents]
+    caption_files = [f"{id}.json" for id in id_list]
+
+    output_dir = args.output_dir
+    # Generate output directory if not exists.
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # Preparing dictionary of question-answer sets
+    prediction_set = {}
+    for sample in new_pred_contents:
+        id = sample['video_name']
+        question = sample['Q']
+        answer = sample['A']
+        pred = sample['pred']
+        qa_set = {"q": question, "a": answer, "pred": pred}
+        prediction_set[id] = qa_set
+
+    # Set the OpenAI API key.
+    openai.api_key = args.api_key
+    num_tasks = args.num_tasks
+
+    # While loop to ensure that all captions are processed.
+    while True:
+        try:
+            # Files that have not been processed yet.
+            completed_files = os.listdir(output_dir)
+            print(f"completed_files: {len(completed_files)}")
+
+            # Files that have not been processed yet.
+            incomplete_files = [f for f in caption_files if f not in completed_files]
+            print(f"incomplete_files: {len(incomplete_files)}")
+
+            # Break the loop when there are no incomplete files
+            if len(incomplete_files) == 0:
+                break
+            if len(incomplete_files) <= num_tasks:
+                num_tasks = 1
+
+            # Split tasks into parts.
+            part_len = len(incomplete_files) // num_tasks
+            all_parts = [incomplete_files[i:i + part_len] for i in range(0, len(incomplete_files), part_len)]
+            task_args = [(prediction_set, part, args.output_dir, args) for part in all_parts]
+
+            # Use a pool of workers to process the files in parallel.
+            with Pool() as pool:
+                pool.starmap(annotate, task_args)
+
+        except Exception as e:
+            print(f"Error: {e}")
+
+    # Combine all the processed files into one
+    combined_contents = {}
+    json_path = args.output_json
+
+    # Iterate through json files
+    for file_name in os.listdir(output_dir):
+        if file_name.endswith(".json"):
+            file_path = os.path.join(output_dir, file_name)
+            with open(file_path, "r") as json_file:
+                content = json.load(json_file)
+                combined_contents[file_name[:-5]] = content
+
+    # Write combined content to a json file
+    with open(json_path, "w") as json_file:
+        json.dump(combined_contents, json_file)
+    print("All evaluation completed!")
+
+    # Calculate average score
+    score_sum = 0
+    count = 0
+    for key, result in combined_contents.items():
+        count += 1
+        score_match = result[0]['score']
+        score = int(score_match)
+        score_sum += score
+    average_score = score_sum / count
+
+    print("Average score for detailed orientation:", average_score)
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/llava/eval/video/eval_benchmark_3_context.py b/llava/eval/video/eval_benchmark_3_context.py
new file mode 100644
index 0000000..bb8d260
--- /dev/null
+++ b/llava/eval/video/eval_benchmark_3_context.py
@@ -0,0 +1,191 @@
+import openai
+import os
+import argparse
+import json
+import ast
+from multiprocessing.pool import Pool
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="question-answer-generation-using-gpt-3")
+    parser.add_argument("--pred_path", required=True, help="The path to file containing prediction.")
+    parser.add_argument("--output_dir", required=True, help="The path to save annotation json files.")
+    parser.add_argument("--output_json", required=True, help="The path to save annotation final combined json file.")
+    parser.add_argument("--api_key", required=True, help="OpenAI API key.")
+    parser.add_argument("--api_base", default="", type=str, help="OpenAI API base.")
+    parser.add_argument("--num_tasks", required=True, type=int, help="Number of splits.")
+    args = parser.parse_args()
+    return args
+
+
+def annotate(prediction_set, caption_files, output_dir, args):
+    """
+    Evaluates question and answer pairs using GPT-3 and
+    returns a score for contextual understanding.
+    """
+    # Set the OpenAI API key.
+    openai.api_key = args.api_key
+    if args.api_base is not None:
+        openai.api_base = args.api_base
+    for file in caption_files:
+        key = file[:-5] # Strip file extension
+        qa_set = prediction_set[key]
+        question = qa_set['q']
+        answer = qa_set['a']
+        pred = qa_set['pred']
+        try:
+            # Compute the contextual understanding score
+            completion = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {
+                        "role": "system",
+                        "content":
+                            "You are an intelligent chatbot designed for evaluating the contextual understanding of generative outputs for video-based question-answer pairs. "
+                            "Your task is to compare the predicted answer with the correct answer and determine if the generated response aligns with the overall context of the video content. Here's how you can accomplish the task:"
+                            "------"
+                            "##INSTRUCTIONS: "
+                            "- Evaluate whether the predicted answer aligns with the overall context of the video content. It should not provide information that is out of context or misaligned.\n"
+                            "- The predicted answer must capture the main themes and sentiments of the video.\n"
+                            "- Consider synonyms or paraphrases as valid matches.\n"
+                            "- Provide your evaluation of the contextual understanding of the prediction compared to the answer."
+                    },
+                    {
+                        "role": "user",
+                        "content":
+                            "Please evaluate the following video-based question-answer pair:\n\n"
+                            f"Question: {question}\n"
+                            f"Correct Answer: {answer}\n"
+                            f"Predicted Answer: {pred}\n\n"
+                            "Provide your evaluation only as a contextual understanding score where the contextual understanding score is an integer value between 0 and 5, with 5 indicating the highest level of contextual understanding. "
+                            "Please generate the response in the form of a Python dictionary string with keys 'score', where its value is contextual understanding score in INTEGER, not STRING."
+                            "DO NOT PROVIDE ANY OTHER OUTPUT TEXT OR EXPLANATION. Only provide the Python dictionary string. "
+                            "For example, your response should look like this: {''score': 4.8}."
+                    }
+                ]
+            )
+            # Convert response to a Python dictionary.
+            response_message = completion["choices"][0]["message"]["content"]
+            response_dict = ast.literal_eval(response_message)
+            result_qa_pair = [response_dict, qa_set]
+
+            # Save the question-answer pairs to a json file.
+            with open(f"{output_dir}/{key}.json", "w") as f:
+                json.dump(result_qa_pair, f)
+
+        except Exception as e:
+            print(f"Error processing file '{key}': {e}")
+
+
+def main():
+    """
+    Main function to control the flow of the program.
+    """
+    # Parse arguments.
+    args = parse_args()
+
+    file = open(args.pred_path)
+    pred_contents = json.load(file)
+
+    # Dictionary to store the count of occurrences for each video_id
+    video_id_counts = {}
+    new_pred_contents = []
+
+    # Iterate through each sample in pred_contents
+    for sample in pred_contents:
+        video_id = sample['video_name']
+        if video_id in video_id_counts:
+            video_id_counts[video_id] += 1
+        else:
+            video_id_counts[video_id] = 0
+
+        # Create a new sample with the modified key
+        new_sample = sample
+        new_sample['video_name'] = f"{video_id}_{video_id_counts[video_id]}"
+        new_pred_contents.append(new_sample)
+
+    # Generating list of id's and corresponding files
+    id_list = [x['video_name'] for x in new_pred_contents]
+    caption_files = [f"{id}.json" for id in id_list]
+
+    output_dir = args.output_dir
+    # Generate output directory if not exists.
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # Preparing dictionary of question-answer sets
+    prediction_set = {}
+    for sample in new_pred_contents:
+        id = sample['video_name']
+        question = sample['Q']
+        answer = sample['A']
+        pred = sample['pred']
+        qa_set = {"q": question, "a": answer, "pred": pred}
+        prediction_set[id] = qa_set
+
+    # Set the OpenAI API key.
+    openai.api_key = args.api_key
+    num_tasks = args.num_tasks
+
+    # While loop to ensure that all captions are processed.
+    while True:
+        try:
+            # Files that have not been processed yet.
+            completed_files = os.listdir(output_dir)
+            print(f"completed_files: {len(completed_files)}")
+
+            # Files that have not been processed yet.
+            incomplete_files = [f for f in caption_files if f not in completed_files]
+            print(f"incomplete_files: {len(incomplete_files)}")
+
+            # Break the loop when there are no incomplete files
+            if len(incomplete_files) == 0:
+                break
+            if len(incomplete_files) <= num_tasks:
+                num_tasks = 1
+
+            # Split tasks into parts.
+            part_len = len(incomplete_files) // num_tasks
+            all_parts = [incomplete_files[i:i + part_len] for i in range(0, len(incomplete_files), part_len)]
+            task_args = [(prediction_set, part, args.output_dir, args) for part in all_parts]
+
+            # Use a pool of workers to process the files in parallel.
+            with Pool() as pool:
+                pool.starmap(annotate, task_args)
+
+        except Exception as e:
+            print(f"Error: {e}")
+
+    # Combine all the processed files into one
+    combined_contents = {}
+    json_path = args.output_json
+
+    # Iterate through json files
+    for file_name in os.listdir(output_dir):
+        if file_name.endswith(".json"):
+            file_path = os.path.join(output_dir, file_name)
+            with open(file_path, "r") as json_file:
+                content = json.load(json_file)
+                combined_contents[file_name[:-5]] = content
+
+    # Write combined content to a json file
+    with open(json_path, "w") as json_file:
+        json.dump(combined_contents, json_file)
+    print("All evaluation completed!")
+
+    # Calculate average score
+    score_sum = 0
+    count = 0
+    for key, result in combined_contents.items():
+        count += 1
+        score_match = result[0]['score']
+        score = int(score_match)
+        score_sum += score
+    average_score = score_sum / count
+
+    print("Average score for contextual understanding:", average_score)
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/llava/eval/video/eval_benchmark_4_temporal.py b/llava/eval/video/eval_benchmark_4_temporal.py
new file mode 100644
index 0000000..46d3112
--- /dev/null
+++ b/llava/eval/video/eval_benchmark_4_temporal.py
@@ -0,0 +1,190 @@
+import openai
+import os
+import argparse
+import json
+import ast
+from multiprocessing.pool import Pool
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="question-answer-generation-using-gpt-3")
+    parser.add_argument("--pred_path", required=True, help="The path to file containing prediction.")
+    parser.add_argument("--output_dir", required=True, help="The path to save annotation json files.")
+    parser.add_argument("--output_json", required=True, help="The path to save annotation final combined json file.")
+    parser.add_argument("--api_key", required=True, help="OpenAI API key.")
+    parser.add_argument("--api_base", default="", type=str, help="OpenAI API base.")
+    parser.add_argument("--num_tasks", required=True, type=int, help="Number of splits.")
+    args = parser.parse_args()
+    return args
+
+
+def annotate(prediction_set, caption_files, output_dir, args):
+    """
+    Evaluates question and answer pairs using GPT-3 and
+    returns a score for temporal understanding.
+    """
+    # Set the OpenAI API key.
+    openai.api_key = args.api_key
+    if args.api_base is not None:
+        openai.api_base = args.api_base
+    for file in caption_files:
+        key = file[:-5] # Strip file extension
+        qa_set = prediction_set[key]
+        question = qa_set['q']
+        answer = qa_set['a']
+        pred = qa_set['pred']
+        try:
+            # Compute the temporal understanding score
+            completion = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {
+                        "role": "system",
+                        "content":
+                            "You are an intelligent chatbot designed for evaluating the temporal understanding of generative outputs for video-based question-answer pairs. "
+                            "Your task is to compare the predicted answer with the correct answer and determine if they correctly reflect the temporal sequence of events in the video content. Here's how you can accomplish the task:"
+                            "------"
+                            "##INSTRUCTIONS: "
+                            "- Focus on the temporal consistency between the predicted answer and the correct answer. The predicted answer should correctly reflect the sequence of events or details as they are presented in the video content.\n"
+                            "- Consider synonyms or paraphrases as valid matches, but only if the temporal order is maintained.\n"
+                            "- Evaluate the temporal accuracy of the prediction compared to the answer."
+                    },
+                    {
+                        "role": "user",
+                        "content":
+                            "Please evaluate the following video-based question-answer pair:\n\n"
+                            f"Question: {question}\n"
+                            f"Correct Answer: {answer}\n"
+                            f"Predicted Answer: {pred}\n\n"
+                            "Provide your evaluation only as a temporal accuracy score where the temporal accuracy score is an integer value between 0 and 5, with 5 indicating the highest level of temporal consistency. "
+                            "Please generate the response in the form of a Python dictionary string with keys 'score', where its value is the temporal accuracy score in INTEGER, not STRING."
+                            "DO NOT PROVIDE ANY OTHER OUTPUT TEXT OR EXPLANATION. Only provide the Python dictionary string. "
+                            "For example, your response should look like this: {''score': 4.8}."
+                    }
+                ]
+            )
+            # Convert response to a Python dictionary.
+            response_message = completion["choices"][0]["message"]["content"]
+            response_dict = ast.literal_eval(response_message)
+            result_qa_pair = [response_dict, qa_set]
+
+            # Save the question-answer pairs to a json file.
+            with open(f"{output_dir}/{key}.json", "w") as f:
+                json.dump(result_qa_pair, f)
+
+        except Exception as e:
+            print(f"Error processing file '{key}': {e}")
+
+
+def main():
+    """
+    Main function to control the flow of the program.
+    """
+    # Parse arguments.
+    args = parse_args()
+
+    file = open(args.pred_path)
+    pred_contents = json.load(file)
+
+    # Dictionary to store the count of occurrences for each video_id
+    video_id_counts = {}
+    new_pred_contents = []
+
+    # Iterate through each sample in pred_contents
+    for sample in pred_contents:
+        video_id = sample['video_name']
+        if video_id in video_id_counts:
+            video_id_counts[video_id] += 1
+        else:
+            video_id_counts[video_id] = 0
+
+        # Create a new sample with the modified key
+        new_sample = sample
+        new_sample['video_name'] = f"{video_id}_{video_id_counts[video_id]}"
+        new_pred_contents.append(new_sample)
+
+    # Generating list of id's and corresponding files
+    id_list = [x['video_name'] for x in new_pred_contents]
+    caption_files = [f"{id}.json" for id in id_list]
+
+    output_dir = args.output_dir
+    # Generate output directory if not exists.
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # Preparing dictionary of question-answer sets
+    prediction_set = {}
+    for sample in new_pred_contents:
+        id = sample['video_name']
+        question = sample['Q']
+        answer = sample['A']
+        pred = sample['pred']
+        qa_set = {"q": question, "a": answer, "pred": pred}
+        prediction_set[id] = qa_set
+
+    # Set the OpenAI API key.
+    openai.api_key = args.api_key
+    num_tasks = args.num_tasks
+
+    # While loop to ensure that all captions are processed.
+    while True:
+        try:
+            # Files that have not been processed yet.
+            completed_files = os.listdir(output_dir)
+            print(f"completed_files: {len(completed_files)}")
+
+            # Files that have not been processed yet.
+            incomplete_files = [f for f in caption_files if f not in completed_files]
+            print(f"incomplete_files: {len(incomplete_files)}")
+
+            # Break the loop when there are no incomplete files
+            if len(incomplete_files) == 0:
+                break
+            if len(incomplete_files) <= num_tasks:
+                num_tasks = 1
+
+            # Split tasks into parts.
+            part_len = len(incomplete_files) // num_tasks
+            all_parts = [incomplete_files[i:i + part_len] for i in range(0, len(incomplete_files), part_len)]
+            task_args = [(prediction_set, part, args.output_dir, args) for part in all_parts]
+
+            # Use a pool of workers to process the files in parallel.
+            with Pool() as pool:
+                pool.starmap(annotate, task_args)
+
+        except Exception as e: 
+            print(f"Error: {e}")
+
+    # Combine all the processed files into one
+    combined_contents = {}
+    json_path = args.output_json
+
+    # Iterate through json files
+    for file_name in os.listdir(output_dir):
+        if file_name.endswith(".json"):
+            file_path = os.path.join(output_dir, file_name)
+            with open(file_path, "r") as json_file:
+                content = json.load(json_file)
+                combined_contents[file_name[:-5]] = content
+
+    # Write combined content to a json file
+    with open(json_path, "w") as json_file:
+        json.dump(combined_contents, json_file)
+    print("All evaluation completed!")
+
+    # Calculate average score
+    score_sum = 0
+    count = 0
+    for key, result in combined_contents.items():
+        count += 1
+        score_match = result[0]['score']
+        score = int(score_match)
+        score_sum += score
+    average_score = score_sum / count
+
+    print("Average score temporal understanding:", average_score)
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/llava/eval/video/eval_benchmark_5_consistency.py b/llava/eval/video/eval_benchmark_5_consistency.py
new file mode 100644
index 0000000..86fdc55
--- /dev/null
+++ b/llava/eval/video/eval_benchmark_5_consistency.py
@@ -0,0 +1,198 @@
+import openai
+import os
+import argparse
+import json
+import ast
+from multiprocessing.pool import Pool
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="question-answer-generation-using-gpt-3")
+    parser.add_argument("--pred_path", required=True, help="The path to file containing prediction.")
+    parser.add_argument("--output_dir", required=True, help="The path to save annotation json files.")
+    parser.add_argument("--output_json", required=True, help="The path to save annotation final combined json file.")
+    parser.add_argument("--api_key", required=True, help="OpenAI API key.")
+    parser.add_argument("--api_base", default="", type=str, help="OpenAI API base.")
+    parser.add_argument("--num_tasks", required=True, type=int, help="Number of splits.")
+    args = parser.parse_args()
+    return args
+
+
+def annotate(prediction_set, caption_files, output_dir, args):
+    """
+    Evaluates question and answer pairs using GPT-3 and
+    returns a score for consistency.
+    """
+    # Set the OpenAI API key.
+    openai.api_key = args.api_key
+    if args.api_base is not None:
+        openai.api_base = args.api_base
+    for file in caption_files:
+        key = file[:-5] # Strip file extension
+        qa_set = prediction_set[key]
+        question1 = qa_set['q1']
+        question2 = qa_set['q2']
+        answer = qa_set['a']
+        pred1 = qa_set['pred1']
+        pred2 = qa_set['pred2']
+        try:
+            # Compute the consistency score
+            completion = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {
+                        "role": "system",
+                        "content":
+                            "You are an intelligent chatbot designed for evaluating the consistency of generative outputs for similar video-based question-answer pairs. "
+                            "You will be given two very similar questions, a common answer common to both the questions and predicted answers for the two questions ."
+                            "Your task is to compare the predicted answers for two very similar question, with a common correct answer and determine if they are consistent. Here's how you can accomplish the task:"
+                            "------"
+                            "##INSTRUCTIONS: "
+                            "- Focus on the consistency between the two predicted answers and the correct answer. Both predicted answers should correspond to the correct answer and to each other, and should not contain any contradictions or significant differences in the conveyed information.\n"
+                            "- Both predicted answers must be consistent with each other and the correct answer, in terms of the information they provide about the video content.\n"
+                            "- Consider synonyms or paraphrases as valid matches, but only if they maintain the consistency in the conveyed information.\n"
+                            "- Evaluate the consistency of the two predicted answers compared to the correct answer."
+                    },
+                    {
+                        "role": "user",
+                        "content":
+                            "Please evaluate the following video-based question-answer pair:\n\n"
+                            f"Question 1: {question1}\n"
+                            f"Question 2: {question2}\n"
+                            f"Correct Answer: {answer}\n"
+                            f"Predicted Answer to Question 1: {pred1}\n"
+                            f"Predicted Answer to Question 2: {pred2}\n\n"
+                            "Provide your evaluation only as a consistency score where the consistency score is an integer value between 0 and 5, with 5 indicating the highest level of consistency. "
+                            "Please generate the response in the form of a Python dictionary string with keys 'score', where its value is the consistency score in INTEGER, not STRING."
+                            "DO NOT PROVIDE ANY OTHER OUTPUT TEXT OR EXPLANATION. Only provide the Python dictionary string. "
+                            "For example, your response should look like this: {''score': 4.8}."
+                    }
+                ]
+            )
+            # Convert response to a Python dictionary.
+            response_message = completion["choices"][0]["message"]["content"]
+            response_dict = ast.literal_eval(response_message)
+            result_qa_pair = [response_dict, qa_set]
+
+            # Save the question-answer pairs to a json file.
+            with open(f"{output_dir}/{key}.json", "w") as f:
+                json.dump(result_qa_pair, f)
+
+        except Exception as e:
+            print(f"Error processing file '{key}': {e}")
+
+
+def main():
+    """
+    Main function to control the flow of the program.
+    """
+    # Parse arguments.
+    args = parse_args()
+
+    file = open(args.pred_path)
+    pred_contents = json.load(file)
+
+    # Dictionary to store the count of occurrences for each video_id
+    video_id_counts = {}
+    new_pred_contents = []
+
+    # Iterate through each sample in pred_contents
+    for sample in pred_contents:
+        video_id = sample['video_name']
+        if video_id in video_id_counts:
+            video_id_counts[video_id] += 1
+        else:
+            video_id_counts[video_id] = 0
+
+        # Create a new sample with the modified key
+        new_sample = sample
+        new_sample['video_name'] = f"{video_id}_{video_id_counts[video_id]}"
+        new_pred_contents.append(new_sample)
+
+    # Generating list of id's and corresponding files
+    id_list = [x['video_name'] for x in new_pred_contents]
+    caption_files = [f"{id}.json" for id in id_list]
+
+    output_dir = args.output_dir
+    # Generate output directory if not exists.
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # Preparing dictionary of question-answer sets
+    prediction_set = {}
+    for sample in new_pred_contents:
+        id = sample['video_name']
+        question1 = sample['Q1']
+        question2 = sample['Q1']
+        answer = sample['A']
+        pred1 = sample['pred1']
+        pred2 = sample['pred2']
+        qa_set = {"q1": question1, "q2": question2, "a": answer, "pred1": pred1, "pred2": pred2}
+        prediction_set[id] = qa_set
+
+    # Set the OpenAI API key.
+    openai.api_key = args.api_key
+    num_tasks = args.num_tasks
+
+    # While loop to ensure that all captions are processed.
+    while True:
+        try:
+            # Files that have not been processed yet.
+            completed_files = os.listdir(output_dir)
+            print(f"completed_files: {len(completed_files)}")
+
+            # Files that have not been processed yet.
+            incomplete_files = [f for f in caption_files if f not in completed_files]
+            print(f"incomplete_files: {len(incomplete_files)}")
+
+            # Break the loop when there are no incomplete files
+            if len(incomplete_files) == 0:
+                break
+            if len(incomplete_files) <= num_tasks:
+                num_tasks = 1
+
+            # Split tasks into parts.
+            part_len = len(incomplete_files) // num_tasks
+            all_parts = [incomplete_files[i:i + part_len] for i in range(0, len(incomplete_files), part_len)]
+            task_args = [(prediction_set, part, args.output_dir, args) for part in all_parts]
+
+            # Use a pool of workers to process the files in parallel.
+            with Pool() as pool:
+                pool.starmap(annotate, task_args)
+
+        except Exception as e: 
+            print(f"Error: {e}")
+
+    # Combine all the processed files into one
+    combined_contents = {}
+    json_path = args.output_json
+
+    # Iterate through json files
+    for file_name in os.listdir(output_dir):
+        if file_name.endswith(".json"):
+            file_path = os.path.join(output_dir, file_name)
+            with open(file_path, "r") as json_file:
+                content = json.load(json_file)
+                combined_contents[file_name[:-5]] = content
+
+    # Write combined content to a json file
+    with open(json_path, "w") as json_file:
+        json.dump(combined_contents, json_file)
+    print("All evaluation completed!")
+
+    # Calculate average score
+    score_sum = 0
+    count = 0
+    for key, result in combined_contents.items():
+        count += 1
+        score_match = result[0]['score']
+        score = int(score_match)
+        score_sum += score
+    average_score = score_sum / count
+
+    print("Average score for consistency:", average_score)
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/llava/eval/video/eval_video_qa.py b/llava/eval/video/eval_video_qa.py
new file mode 100644
index 0000000..61956df
--- /dev/null
+++ b/llava/eval/video/eval_video_qa.py
@@ -0,0 +1,206 @@
+import openai
+import os
+import argparse
+import json
+import ast
+from multiprocessing.pool import Pool
+from tqdm import tqdm
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="question-answer-generation-using-gpt-3")
+    parser.add_argument("--pred_path", default=r'', help="The path to file containing prediction.")
+    parser.add_argument("--output_dir", default=r'', help="The path to save annotation json files.")
+    parser.add_argument("--output_json", default=r'', help="The path to save annotation final combined json file.")
+    parser.add_argument("--api_key", default="", help="OpenAI API key.")
+    parser.add_argument("--api_base", default="", type=str, help="OpenAI API base.")
+    parser.add_argument("--num_tasks", default=1, type=int, help="Number of splits.")
+    args = parser.parse_args()
+    return args
+
+
+def annotate(prediction_set, caption_files, output_dir, args):
+    """
+    Evaluates question and answer pairs using GPT-3
+    Returns a score for correctness.
+    """
+    # Set the OpenAI API key.
+    openai.api_key = args.api_key
+    if args.api_base is not None:
+        openai.api_base = args.api_base
+    for file in caption_files:
+        key = file[:-5] # Strip file extension
+        qa_set = prediction_set[key]
+        question = qa_set['q']
+        answer = qa_set['a']
+        pred = qa_set['pred']
+        try:
+            # Compute the correctness score
+            completion = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {
+                        "role": "system",
+                        "content":
+                            "You are an intelligent chatbot designed for evaluating the correctness of generative outputs for question-answer pairs. "
+                            "Your task is to compare the predicted answer with the correct answer and determine if they match meaningfully. Here's how you can accomplish the task:"
+                            "------"
+                            "##INSTRUCTIONS: "
+                            "- Focus on the meaningful match between the predicted answer and the correct answer.\n"
+                            "- Consider synonyms or paraphrases as valid matches.\n"
+                            "- Evaluate the correctness of the prediction compared to the answer."
+                    },
+                    {
+                        "role": "user",
+                        "content":
+                            "Please evaluate the following video-based question-answer pair:\n\n"
+                            f"Question: {question}\n"
+                            f"Correct Answer: {answer}\n"
+                            f"Predicted Answer: {pred}\n\n"
+                            "Provide your evaluation only as a yes/no and score where the score is an integer value between 0 and 5, with 5 indicating the highest meaningful match. "
+                            "Please generate the response in the form of a Python dictionary string with keys 'pred' and 'score', where value of 'pred' is  a string of 'yes' or 'no' and value of 'score' is in INTEGER, not STRING."
+                            "DO NOT PROVIDE ANY OTHER OUTPUT TEXT OR EXPLANATION. Only provide the Python dictionary string. "
+                            "For example, your response should look like this: {'pred': 'yes', 'score': 4.8}."
+                    }
+                ]
+            )
+            # Convert response to a Python dictionary.
+            response_message = completion["choices"][0]["message"]["content"]
+            response_dict = ast.literal_eval(response_message)
+            result_qa_pair = [response_dict, qa_set]
+
+            # Save the question-answer pairs to a json file.
+            with open(f"{output_dir}/{key}.json", "w") as f:
+                json.dump(result_qa_pair, f)
+
+        except Exception as e:
+            print(f"Error processing file '{key}': {e}")
+
+
+def main():
+    """
+    Main function to control the flow of the program.
+    """
+    # Parse arguments.
+    args = parse_args()
+
+    file = open(args.pred_path)
+    new_pred_contents = [eval(i.strip()) for i in file.readlines()]
+
+    '''
+    # Dictionary to store the count of occurrences for each video_id
+    video_id_counts = {}
+    new_pred_contents = []
+
+    # Iterate through each sample in pred_contents
+    for sample in pred_contents:
+        video_id = sample['video_name']
+        if video_id in video_id_counts:
+            video_id_counts[video_id] += 1
+        else:
+            video_id_counts[video_id] = 0
+
+        # Create a new sample with the modified key
+        new_sample = sample
+        new_sample['video_name'] = f"{video_id}_{video_id_counts[video_id]}"
+        new_pred_contents.append(new_sample)
+    '''
+    # Generating list of id's and corresponding files
+    id_list = [x['id'] for x in new_pred_contents]
+    caption_files = [f"{id}.json" for id in id_list]
+
+    output_dir = args.output_dir
+    # Generate output directory if not exists.
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # Preparing dictionary of question-answer sets
+    prediction_set = {}
+    for sample in new_pred_contents:
+        id = sample['id']
+        question = sample['question']
+        answer = sample['answer']
+        pred = sample['pred']
+        qa_set = {"q": question, "a": answer, "pred": pred}
+        prediction_set[id] = qa_set
+
+    num_tasks = args.num_tasks
+
+    # While loop to ensure that all captions are processed.
+    while True:
+        try:
+            # Files that have not been processed yet.
+            completed_files = os.listdir(output_dir)
+            print(f"completed_files: {len(completed_files)}")
+
+            # Files that have not been processed yet.
+            incomplete_files = [f for f in caption_files if f not in completed_files]
+            print(f"incomplete_files: {len(incomplete_files)}")
+
+            # Break the loop when there are no incomplete files
+            if len(incomplete_files) == 0:
+                break
+            if len(incomplete_files) <= num_tasks:
+                num_tasks = 1
+
+            # Split tasks into parts.
+            part_len = len(incomplete_files) // num_tasks
+            all_parts = [incomplete_files[i:i + part_len] for i in range(0, len(incomplete_files), part_len)]
+            task_args = [(prediction_set, part, args.output_dir, args) for part in all_parts]
+
+            # Use a pool of workers to process the files in parallel.
+            with Pool() as pool:
+                pool.starmap(annotate, task_args)
+
+        except Exception as e:
+            print(f"Error: {e}")
+
+    # Combine all the processed files into one
+    combined_contents = {}
+    json_path = args.output_json
+
+    # Iterate through json files
+    for file_name in os.listdir(output_dir):
+        if file_name.endswith(".json"):
+            file_path = os.path.join(output_dir, file_name)
+            with open(file_path, "r") as json_file:
+                content = json.load(json_file)
+                combined_contents[file_name[:-5]] = content
+
+    # Write combined content to a json file
+    with open(json_path, "w") as json_file:
+        json.dump(combined_contents, json_file)
+    print("All evaluation completed!")
+
+    # Calculate average score and accuracy
+    score_sum = 0
+    count = 0
+    yes_count = 0
+    no_count = 0
+    for key, result in tqdm(combined_contents.items()):
+        try:
+            # Computing score
+            count += 1
+            score_match = result[0]['score']
+            score = int(score_match)
+            score_sum += score
+
+            # Computing accuracy
+            pred = result[0]['pred']
+            if "yes" in pred.lower():
+                yes_count += 1
+            elif "no" in pred.lower():
+                no_count += 1
+        except:
+            print(result)
+
+    average_score = score_sum / count
+    accuracy = yes_count / (yes_count + no_count)
+    print("Yes count:", yes_count)
+    print("No count:", no_count)
+    print("Accuracy:", accuracy)
+    print("Average score:", average_score)
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/llava/eval/video/run_inference_benchmark_consistency.py b/llava/eval/video/run_inference_benchmark_consistency.py
new file mode 100644
index 0000000..72c5030
--- /dev/null
+++ b/llava/eval/video/run_inference_benchmark_consistency.py
@@ -0,0 +1,96 @@
+import os
+import argparse
+import json
+from tqdm import tqdm
+# from video_chatgpt.eval.model_utils import initialize_model, load_video
+# from video_chatgpt.inference import video_chatgpt_infer
+
+from llava.eval.video.run_inference_video_qa import get_model_output
+from llava.mm_utils import get_model_name_from_path
+from llava.model.builder import load_pretrained_model
+
+
+def parse_args():
+    """
+    Parse command-line arguments.
+    """
+    parser = argparse.ArgumentParser()
+
+    # Define the command-line arguments
+    parser.add_argument('--model_path', help='', required=True)
+    parser.add_argument('--cache_dir', help='', required=True)
+    parser.add_argument('--video_dir', help='Directory containing video files.', required=True)
+    parser.add_argument('--gt_file', help='Path to the ground truth file.', required=True)
+    parser.add_argument('--output_dir', help='Directory to save the model results JSON.', required=True)
+    parser.add_argument('--output_name', help='Name of the file for storing results JSON.', required=True)
+    # parser.add_argument("--model-name", type=str, required=True)
+    parser.add_argument("--device", type=str, required=False, default='cuda:0')
+    parser.add_argument('--model_base', help='', default=None, type=str, required=False)
+    parser.add_argument("--model_max_length", type=int, required=False, default=2048)
+    # parser.add_argument("--conv-mode", type=str, required=False, default='video-chatgpt_v1')
+    # parser.add_argument("--projection_path", type=str, required=True)
+
+    return parser.parse_args()
+
+
+def run_inference(args):
+    """
+    Run inference on a set of video files using the provided model.
+
+    Args:
+        args: Command-line arguments.
+    """
+    # Initialize the model
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)
+    model = model.to(args.device)
+
+    # Load the ground truth file
+    with open(args.gt_file) as file:
+        gt_contents = json.load(file)
+
+    # Create the output directory if it doesn't exist
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+
+    output_list = []  # List to store the output results
+    # conv_mode = args.conv_mode
+
+    video_formats = ['.mp4', '.avi', '.mov', '.mkv']
+
+    # Iterate over each sample in the ground truth file
+    for sample in tqdm(gt_contents):
+        video_name = sample['video_name']
+        sample_set = sample
+        question_1 = sample['Q1']
+        question_2 = sample['Q2']
+
+        try:
+            # Load the video file
+            for fmt in video_formats:  # Added this line
+                temp_path = os.path.join(args.video_dir, f"{video_name}{fmt}")
+                if os.path.exists(temp_path):
+                    video_path = temp_path
+
+                    # Run inference on the video for the first question and add the output to the list
+                    output_1 = get_model_output(model, processor['video'], tokenizer, video_path, question_1, args)
+                    sample_set['pred1'] = output_1
+
+                    # Run inference on the video for the second question and add the output to the list
+                    output_2 = get_model_output(model, processor['video'], tokenizer, video_path, question_2, args)
+                    sample_set['pred2'] = output_2
+
+                    output_list.append(sample_set)
+                    break
+
+        except Exception as e:
+            print(f"Error processing video file '{video_name}': {e}")
+
+    # Save the output list to a JSON file
+    with open(os.path.join(args.output_dir, f"{args.output_name}.json"), 'w') as file:
+        json.dump(output_list, file)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    run_inference(args)
diff --git a/llava/eval/video/run_inference_benchmark_general.py b/llava/eval/video/run_inference_benchmark_general.py
new file mode 100644
index 0000000..8eed474
--- /dev/null
+++ b/llava/eval/video/run_inference_benchmark_general.py
@@ -0,0 +1,87 @@
+import os
+import argparse
+import json
+from tqdm import tqdm
+# from video_chatgpt.eval.model_utils import initialize_model, load_video
+# from video_chatgpt.inference import video_chatgpt_infer
+
+from llava.eval.video.run_inference_video_qa import get_model_output
+from llava.mm_utils import get_model_name_from_path
+from llava.model.builder import load_pretrained_model
+
+
+def parse_args():
+    """
+    Parse command-line arguments.
+    """
+    parser = argparse.ArgumentParser()
+
+    # Define the command-line arguments
+    parser.add_argument('--model_path', help='', required=True)
+    parser.add_argument('--cache_dir', help='', required=True)
+    parser.add_argument('--video_dir', help='Directory containing video files.', required=True)
+    parser.add_argument('--gt_file', help='Path to the ground truth file.', required=True)
+    parser.add_argument('--output_dir', help='Directory to save the model results JSON.', required=True)
+    parser.add_argument('--output_name', help='Name of the file for storing results JSON.', required=True)
+    # parser.add_argument("--model-name", type=str, required=True)
+    parser.add_argument("--device", type=str, required=False, default='cuda:0')
+    parser.add_argument('--model_base', help='', default=None, type=str, required=False)
+    parser.add_argument("--model_max_length", type=int, required=False, default=2048)
+    # parser.add_argument("--conv-mode", type=str, required=False, default='video-chatgpt_v1')
+    # parser.add_argument("--projection_path", type=str, required=True)
+
+    return parser.parse_args()
+
+
+def run_inference(args):
+    """
+    Run inference on a set of video files using the provided model.
+
+    Args:
+        args: Command-line arguments.
+    """# Initialize the model
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)
+    model = model.to(args.device)
+
+    # Load the ground truth file
+    with open(args.gt_file) as file:
+        gt_contents = json.load(file)
+
+    # Create the output directory if it doesn't exist
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+
+    output_list = []  # List to store the output results
+    # conv_mode = args.conv_mode
+
+    video_formats = ['.mp4', '.avi', '.mov', '.mkv']
+
+    # Iterate over each sample in the ground truth file
+    for sample in tqdm(gt_contents):
+        video_name = sample['video_name']
+        sample_set = sample
+        question = sample['Q']
+
+        try:
+            # Load the video file
+            for fmt in video_formats:  # Added this line
+                temp_path = os.path.join(args.video_dir, f"{video_name}{fmt}")
+                if os.path.exists(temp_path):
+                    video_path = temp_path
+                    output = get_model_output(model, processor['video'], tokenizer, video_path, question, args)
+                    sample_set['pred'] = output
+                    output_list.append(sample_set)
+                    break
+
+        except Exception as e:
+            print(f"Error processing video file '{video_name}': {e}")
+
+    # Save the output list to a JSON file
+    with open(os.path.join(args.output_dir, f"{args.output_name}.json"), 'w') as file:
+        json.dump(output_list, file)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    run_inference(args)
diff --git a/llava/eval/video/run_inference_video_qa.py b/llava/eval/video/run_inference_video_qa.py
new file mode 100644
index 0000000..0adf6af
--- /dev/null
+++ b/llava/eval/video/run_inference_video_qa.py
@@ -0,0 +1,171 @@
+import math
+import os
+import argparse
+import json
+
+import torch
+import transformers
+from tqdm import tqdm
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.constants import DEFAULT_X_START_TOKEN, DEFAULT_X_TOKEN, DEFAULT_X_END_TOKEN, X_TOKEN_INDEX
+from llava.mm_utils import get_model_name_from_path, tokenizer_X_token, KeywordsStoppingCriteria
+from llava.model.builder import load_pretrained_model
+from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM
+from llava.train.train import smart_tokenizer_and_embedding_resize
+
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+def parse_args():
+    """
+    Parse command-line arguments.
+    """
+    parser = argparse.ArgumentParser()
+
+    # Define the command-line arguments
+    parser.add_argument('--model_path', help='', required=True)
+    parser.add_argument('--cache_dir', help='', required=True)
+    parser.add_argument('--video_dir', help='Directory containing video files.', required=True)
+    parser.add_argument('--gt_file_question', help='Path to the ground truth file containing question.', required=True)
+    parser.add_argument('--gt_file_answers', help='Path to the ground truth file containing answers.', required=True)
+    parser.add_argument('--output_dir', help='Directory to save the model results JSON.', required=True)
+    parser.add_argument('--output_name', help='Name of the file for storing results JSON.', required=True)
+    parser.add_argument("--num_chunks", type=int, default=1)
+    parser.add_argument("--chunk_idx", type=int, default=0)
+    parser.add_argument("--device", type=str, required=False, default='cuda:0')
+    parser.add_argument('--model_base', help='', default=None, type=str, required=False)
+    parser.add_argument("--model_max_length", type=int, required=False, default=2048)
+
+    return parser.parse_args()
+
+def get_model_output(model, video_processor, tokenizer, video, qs, args):
+    if model.config.mm_use_x_start_end:
+        qs = DEFAULT_X_START_TOKEN['VIDEO'] + DEFAULT_X_TOKEN['VIDEO'] + DEFAULT_X_END_TOKEN['VIDEO'] + '\n' + qs
+    else:
+        qs = DEFAULT_X_TOKEN['VIDEO'] + '\n' + qs
+
+    conv_mode = "llava_v1"
+    args.conv_mode = conv_mode
+
+    conv = conv_templates[args.conv_mode].copy()
+    conv.append_message(conv.roles[0], qs)
+    conv.append_message(conv.roles[1], None)
+    prompt = conv.get_prompt()
+
+
+    video_tensor = video_processor.preprocess(video, return_tensors='pt')['pixel_values'][0].half().to(args.device)
+    # print(video_tensor.shape)
+    input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['VIDEO'], return_tensors='pt').unsqueeze(0).to(args.device)
+
+    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+    keywords = [stop_str]
+    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+    '''
+    images (X_modalities) [
+            [img_feature, img_feature, video_feature, audio_feature],
+            ['image', 'image', 'video', 'audio']
+            ]
+    '''
+
+    with torch.inference_mode():
+        output_ids = model.generate(
+            input_ids,
+            images=[[video_tensor], ['video']],
+            do_sample=True,
+            temperature=0.2,
+            max_new_tokens=1024,
+            use_cache=True,
+            stopping_criteria=[stopping_criteria])
+
+    input_token_len = input_ids.shape[1]
+    n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+    if n_diff_input_output > 0:
+        print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+    outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+    outputs = outputs.strip()
+    if outputs.endswith(stop_str):
+        outputs = outputs[:-len(stop_str)]
+    outputs = outputs.strip()
+    print(outputs)
+    return outputs
+
+
+def run_inference(args):
+    """
+    Run inference on ActivityNet QA DataSet using the Video-ChatGPT model.
+
+    Args:
+        args: Command-line arguments.
+    """
+    # Initialize the model
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)
+    model = model.to(args.device)
+
+    # Load both ground truth file containing questions and answers
+    # with open(args.gt_file_question) as file:
+    #     gt_questions = json.load(file)
+    # with open(args.gt_file_answers) as file:
+    #     gt_answers = json.load(file)
+
+    gt_questions = json.load(open(args.gt_file_question, "r"))
+    gt_questions = get_chunk(gt_questions, args.num_chunks, args.chunk_idx)
+    gt_answers = json.load(open(args.gt_file_answers, "r"))
+    # gt_answers = get_chunk(gt_answers, args.num_chunks, args.chunk_idx)
+
+    answers_file = os.path.join(args.output_dir, f"{args.output_name}.json")
+    os.makedirs(args.output_dir, exist_ok=True)
+    ans_file = open(answers_file, "w")
+
+    # Create the output directory if it doesn't exist
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+
+    output_list = []  # List to store the output results
+
+
+    video_formats = ['.mp4', '.avi', '.mov', '.mkv']
+
+    # Iterate over each sample in the ground truth file
+    index = 0
+    for sample in tqdm(gt_questions):
+        video_name = sample['video_name']
+        question = sample['question']
+        id = sample['question_id']
+        answer = gt_answers[index]['answer']
+        index += 1
+
+        sample_set = {'id': id, 'question': question, 'answer': answer}
+
+        # Load the video file
+        for fmt in tqdm(video_formats):  # Added this line
+            temp_path = os.path.join(args.video_dir, f"{video_name}{fmt}")
+            if os.path.exists(temp_path):
+                video_path = temp_path
+                # try:
+                # Run inference on the video and add the output to the list
+                output = get_model_output(model, processor['video'], tokenizer, video_path, question, args)
+                sample_set['pred'] = output
+                output_list.append(sample_set)
+                # except Exception as e:
+                #     print(f"Error processing video file '{video_name}': {e}")
+                ans_file.write(json.dumps(sample_set) + "\n")
+                break
+
+    ans_file.close()
+    # Save the output list to a JSON file
+    # with open(os.path.join(args.output_dir, f"{args.output_name}.json"), 'w') as file:
+    #     json.dump(output_list, file)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    run_inference(args)
diff --git a/llava/eval/video/run_inference_video_qa_act.py b/llava/eval/video/run_inference_video_qa_act.py
new file mode 100644
index 0000000..61eb1ba
--- /dev/null
+++ b/llava/eval/video/run_inference_video_qa_act.py
@@ -0,0 +1,171 @@
+import math
+import os
+import argparse
+import json
+
+import torch
+import transformers
+from tqdm import tqdm
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.constants import DEFAULT_X_START_TOKEN, DEFAULT_X_TOKEN, DEFAULT_X_END_TOKEN, X_TOKEN_INDEX
+from llava.mm_utils import get_model_name_from_path, tokenizer_X_token, KeywordsStoppingCriteria
+from llava.model.builder import load_pretrained_model
+from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM
+from llava.train.train import smart_tokenizer_and_embedding_resize
+
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+def parse_args():
+    """
+    Parse command-line arguments.
+    """
+    parser = argparse.ArgumentParser()
+
+    # Define the command-line arguments
+    parser.add_argument('--model_path', help='', required=True)
+    parser.add_argument('--cache_dir', help='', required=True)
+    parser.add_argument('--video_dir', help='Directory containing video files.', required=True)
+    parser.add_argument('--gt_file_question', help='Path to the ground truth file containing question.', required=True)
+    parser.add_argument('--gt_file_answers', help='Path to the ground truth file containing answers.', required=True)
+    parser.add_argument('--output_dir', help='Directory to save the model results JSON.', required=True)
+    parser.add_argument('--output_name', help='Name of the file for storing results JSON.', required=True)
+    parser.add_argument("--num_chunks", type=int, default=1)
+    parser.add_argument("--chunk_idx", type=int, default=0)
+    parser.add_argument("--device", type=str, required=False, default='cuda:0')
+    parser.add_argument('--model_base', help='', default=None, type=str, required=False)
+    parser.add_argument("--model_max_length", type=int, required=False, default=2048)
+
+    return parser.parse_args()
+
+def get_model_output(model, video_processor, tokenizer, video, qs, args):
+    if model.config.mm_use_x_start_end:
+        qs = DEFAULT_X_START_TOKEN['VIDEO'] + DEFAULT_X_TOKEN['VIDEO'] + DEFAULT_X_END_TOKEN['VIDEO'] + '\n' + qs
+    else:
+        qs = DEFAULT_X_TOKEN['VIDEO'] + '\n' + qs
+
+    conv_mode = "llava_v1"
+    args.conv_mode = conv_mode
+
+    conv = conv_templates[args.conv_mode].copy()
+    conv.append_message(conv.roles[0], qs)
+    conv.append_message(conv.roles[1], None)
+    prompt = conv.get_prompt()
+
+
+    video_tensor = video_processor.preprocess(video, return_tensors='pt')['pixel_values'][0].half().to(args.device)
+    # print(video_tensor.shape)
+    input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['VIDEO'], return_tensors='pt').unsqueeze(0).to(args.device)
+
+    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+    keywords = [stop_str]
+    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+    '''
+    images (X_modalities) [
+            [img_feature, img_feature, video_feature, audio_feature],
+            ['image', 'image', 'video', 'audio']
+            ]
+    '''
+
+    with torch.inference_mode():
+        output_ids = model.generate(
+            input_ids,
+            images=[[video_tensor], ['video']],
+            do_sample=True,
+            temperature=0.2,
+            max_new_tokens=1024,
+            use_cache=True,
+            stopping_criteria=[stopping_criteria])
+
+    input_token_len = input_ids.shape[1]
+    n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+    if n_diff_input_output > 0:
+        print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+    outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+    outputs = outputs.strip()
+    if outputs.endswith(stop_str):
+        outputs = outputs[:-len(stop_str)]
+    outputs = outputs.strip()
+    print(outputs)
+    return outputs
+
+
+def run_inference(args):
+    """
+    Run inference on ActivityNet QA DataSet using the Video-ChatGPT model.
+
+    Args:
+        args: Command-line arguments.
+    """
+    # Initialize the model
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)
+    model = model.to(args.device)
+
+    # Load both ground truth file containing questions and answers
+    # with open(args.gt_file_question) as file:
+    #     gt_questions = json.load(file)
+    # with open(args.gt_file_answers) as file:
+    #     gt_answers = json.load(file)
+
+    gt_questions = json.load(open(args.gt_file_question, "r"))
+    gt_questions = get_chunk(gt_questions, args.num_chunks, args.chunk_idx)
+    gt_answers = json.load(open(args.gt_file_answers, "r"))
+    # gt_answers = get_chunk(gt_answers, args.num_chunks, args.chunk_idx)
+
+    answers_file = os.path.join(args.output_dir, f"{args.output_name}.json")
+    os.makedirs(args.output_dir, exist_ok=True)
+    ans_file = open(answers_file, "w")
+
+    # Create the output directory if it doesn't exist
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+
+    output_list = []  # List to store the output results
+
+
+    video_formats = ['.mp4', '.avi', '.mov', '.mkv']
+
+    # Iterate over each sample in the ground truth file
+    index = 0
+    for sample in tqdm(gt_questions):
+        video_name = sample['video_name']
+        question = sample['question']
+        id = sample['question_id']
+        answer = gt_answers[index]['answer']
+        index += 1
+
+        sample_set = {'id': id, 'question': question, 'answer': answer}
+
+        # Load the video file
+        for fmt in tqdm(video_formats):  # Added this line
+            temp_path = os.path.join(args.video_dir, f"v_{video_name}{fmt}")
+            if os.path.exists(temp_path):
+                video_path = temp_path
+                # try:
+                # Run inference on the video and add the output to the list
+                output = get_model_output(model, processor['video'], tokenizer, video_path, question, args)
+                sample_set['pred'] = output
+                output_list.append(sample_set)
+                # except Exception as e:
+                #     print(f"Error processing video file '{video_name}': {e}")
+                ans_file.write(json.dumps(sample_set) + "\n")
+                break
+
+    ans_file.close()
+    # Save the output list to a JSON file
+    # with open(os.path.join(args.output_dir, f"{args.output_name}.json"), 'w') as file:
+    #     json.dump(output_list, file)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    run_inference(args)
diff --git a/llava/eval/webpage/index.html b/llava/eval/webpage/index.html
new file mode 100644
index 0000000..c2e3cf0
--- /dev/null
+++ b/llava/eval/webpage/index.html
@@ -0,0 +1,162 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Who's GPT-4's favorite? Battles between State-of-the-Art Chatbots</title>
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css">
+    <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
+    <link rel="stylesheet" href="styles.css">
+</head>
+
+<body>
+    <nav class="navbar navbar-expand-lg navbar-dark bg-dark">
+        <a class="navbar-brand" href="#">🏔️ Vicuna Evaluation Examples</a>
+        <button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
+          <span class="navbar-toggler-icon"></span>
+        </button>
+        <div class="collapse navbar-collapse" id="navbarNav">
+          <ul class="navbar-nav mr-auto">
+            <li class="nav-item">
+                <a class="nav-link" href="https://chat.lmsys.org/">Demo</a>
+              </li>
+              <li class="nav-item">
+                <a class="nav-link" href="https://vicuna.lmsys.org">Blog</a>
+              </li>
+              <li class="nav-item">
+                <a class="nav-link" href="https://github.com/lm-sys/FastChat">Github</a>
+              </li>
+          </ul>
+        </div>
+    </nav>
+
+    <div class="container mt-5">
+        <h2 class="text-center mb-5">Who's GPT-4's favorite? Battles between State-of-the-Art Chatbots</h2>
+
+        <!-- Selection -->
+        <div class="form-row">
+            <div class="form-group col-md-2">
+                <label for="category-select">Category</label>
+                <select class="form-control" id="category-select"></select>
+            </div>
+            <div class="form-group col-md-8">
+                <label for="question-select">Question</label>
+                <select class="form-control" id="question-select"></select>
+            </div>
+            <div class="form-group col-md-2">
+                <div class="col-md-2"><label>&nbsp;</label></div>
+                <div class="btn-group" role="group" aria-label="Left and Right Controller">
+                    <button type="button" class="form-control btn btn-primary" id="prev-question"><i class="material-icons">keyboard_arrow_left</i></button>
+                    <button type="button" class="form-control btn btn-primary" id="next-question"><i class="material-icons">keyboard_arrow_right</i></button>
+                </div>
+            </div>
+        </div>
+
+        <!-- "Battle" -->
+        <div class="row mb-4" style="justify-content: center;">
+            <div class="col" style="display: flex; justify-content: center; align-items: center;">
+                <label class="adjustable-font-size" id="other-score-label">*/10</label>
+            </div>
+            <div class="col">
+                <div class="vertical-flex-layout">
+                    <img class="shadow figure-img img-fluid" src="" alt="other logo" width="150" id="other-model-figure">
+                </div>
+            </div>
+            <div class="col">
+                <div class="vertical-flex-layout">
+                    <!-- from: https://fonts.google.com/icons?icon.query=battle&selected=Material+Symbols+Outlined:swords:FILL@0;wght@300;GRAD@0;opsz@48&icon.style=Outlined -->
+                    <img class="figure-img img-fluid" src="figures/swords_FILL0_wght300_GRAD0_opsz48.svg" width="60" height="60">
+                </div>
+            </div>
+            <div class="col">
+                <div class="vertical-flex-layout">
+                    <img class="shadow figure-img img-fluid" src="figures/vicuna.jpeg" alt="vicuna logo" width="150" id="our-model-figure">
+                </div>
+            </div>
+            <div class="col" style="display: flex; justify-content: center; align-items: center;">
+                <label class="adjustable-font-size" id="our-score-label">*/10</label>
+            </div>
+        </div>
+
+        <!-- Question Card -->
+        <div class="card mb-4">
+            <div class="card-body" id="selected-question"></div>
+        </div>
+
+        <!-- Answer Cards -->
+        <div class="row">
+            <div class="col-md-6">
+                <div class="card mb-4 expandable-card">
+                    <div class="card-header" style="padding-bottom: 0.2rem" id="other-model-header-bg">
+                        <div class="row">
+                            <div class="col-md-5" style="align-items: center; display: flex;">
+                                <label id="other-model-header">Assistant #1</label>
+                            </div>
+                            <div class="col-md-7">
+                                <select class="form-control" id="model-select" style="height: fit-content; margin-top: -0.3rem;"></select>
+                            </div>
+                        </div>
+                    </div>
+                    <div class="card-body">
+                        <div class="card-text-container">
+                            <div class="card-text" id="other-model-answer"></div>
+                        </div>
+                        <div class="btn btn-primary expand-btn" style="display:flex;"></div>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-6">
+                <div class="card mb-4 expandable-card">
+                    <div class="card-header" id="our-model-header">
+                        Assistant #2 (Vicuna, our model)
+                    </div>
+                    <div class="card-body">
+                        <div class="card-text-container">
+                            <div class="card-text" id="our-model-answer"></div>
+                        </div>
+                        <div class="btn btn-primary expand-btn" style="display:flex;"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Evaluation -->
+        <div class="card expandable-card">
+            <div class="card-header" style="background-color: #c9c9f2;" id="evaluation-header">GPT-4 Evaluation</div>
+            <div class="card-body">
+                <div class="card-text-container">
+                    <div class="card-text" id="evaluation-result"></div>
+                </div>
+                <div class="btn btn-primary expand-btn" style="display:flex;"></div>
+            </div>
+        </div>
+    </div>
+
+    <div class="container-fluid bg-light py-2">
+        <div class="text-center">
+            <small class="text-muted">This website is co-authored with <a href="https://openai.com" target="_blank">GPT-4</a>.</small>
+        </div>
+    </div>
+
+    <!-- Marked.js -->
+    <script src="https://cdn.jsdelivr.net/npm/marked@4.3.0/lib/marked.umd.min.js"></script>
+    <!-- Bootstrap and Popper.js JavaScript dependencies -->
+    <script src="https://code.jquery.com/jquery-3.5.1.slim.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.6/dist/umd/popper.min.js"></script>
+    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.min.js"></script>
+
+    <script src="script.js"></script>
+    <script>
+      // Fetch the JSON file
+      fetch('data.json')
+        .then(response => response.json())
+        .then(json_data => {
+            // Populate the models and questions.
+            populateModels(json_data.models);
+            populateQuestions(json_data.questions);
+            displayQuestion(currentQuestionIndex);
+        }).catch(error => console.error(error));
+    </script>
+</body>
+
+</html>
diff --git a/llava/eval/webpage/script.js b/llava/eval/webpage/script.js
new file mode 100644
index 0000000..4b71e3d
--- /dev/null
+++ b/llava/eval/webpage/script.js
@@ -0,0 +1,245 @@
+// Description: Script for the evaluation webpage.
+
+let currentQuestionIndex = 1;
+
+// Store the model name mapping for later use.
+modelNameMapping = {
+    "gpt35": "ChatGPT-3.5",
+    "gpt4": "GPT-4",
+    "alpaca": "Alpaca-13b",
+    "vicuna": "Vicuna-13b",
+    "llama": "LLaMA-13b",
+    "bard": "Bard",
+};
+
+modelFigureMapping = {
+    "vicuna": "figures/vicuna.jpeg",
+    // Image from: https://commons.wikimedia.org/wiki/File:ChatGPT_logo.svg
+    "gpt35": "figures/chatgpt.svg",
+    // Image from: https://www.reddit.com/r/logodesign/comments/1128aat/google_ai_bard_logo_design/
+    "bard": "figures/bard.jpg",
+    // Image from: https://crfm.stanford.edu/2023/03/13/alpaca.html
+    "alpaca": "figures/alpaca.png",
+    // Image adapted from https://commons.wikimedia.org/wiki/File:Llama_on_Machu_Picchu.jpg
+    "llama": "figures/llama.jpg",
+}
+
+// Store the question data in a mapping for later use.
+questionMapping = {};
+// Store the question ids in a mapping for later use.
+categoryMapping = {};
+// Store the number of questions for later use.
+questionsCount = 0;
+
+
+function text2Markdown(text) {
+    // Normalize the text for markdown rendering.
+    text = text.trim().replaceAll('\n\n', '\n').replaceAll('\n', '\n\n');
+    return marked.parse(text);
+}
+
+function capitalizeFirstChar(str) {
+    if (!str || str.length === 0) {
+      return str;
+    }
+    return str.charAt(0).toUpperCase() + str.slice(1);
+}
+
+function updateQuestionSelect(question_id) {
+    const select = document.getElementById('question-select');
+    // Clear the question select.
+    select.innerHTML = '';
+    // Populate the question select.
+    category = questionMapping[question_id].category;
+    categoryMapping[category].forEach(question_id => {
+        const question = questionMapping[question_id];
+        const option = document.createElement('option');
+        option.value = question_id;
+        option.textContent = 'Q' + question_id.toString() + ': ' + question.question;
+        select.appendChild(option);
+    });
+    select.value = question_id;
+}
+
+function updateModelSelect() {
+    const select = document.getElementById('model-select');
+    img_path = modelFigureMapping[select.value];
+    document.getElementById('other-model-figure').src = img_path;
+}
+
+function populateModels(models) {
+    const select = document.getElementById('model-select');
+    models.forEach(model => {
+        const option = document.createElement('option');
+        option.value = model;
+        option.textContent = modelNameMapping[model];
+        select.appendChild(option);
+    });
+    updateModelSelect();
+}
+
+function populateQuestions(questions) {
+    const category_select = document.getElementById('category-select');
+
+    questionsCount = questions.length;
+    questions.forEach(question => {
+        const option = document.createElement('option');
+        // Store the question data in a mapping for later use.
+        questionMapping[question.id] = {
+            category: question.category,
+            question: question.question,
+            answers: question.answers,
+            evaluations: question.evaluations,
+            scores: question.scores,
+        };
+        // Store the question id in the category mapping.
+        if (question.category in categoryMapping) {
+            categoryMapping[question.category].push(question.id);
+        } else {
+            categoryMapping[question.category] = [question.id];
+            const category_option = document.createElement('option');
+            category_option.value = question.category;
+            category_option.textContent = capitalizeFirstChar(question.category);
+            category_select.appendChild(category_option);
+        }
+    });
+    // Set the default category.
+    updateQuestionSelect(currentQuestionIndex);
+}
+
+function displayQuestion(index) {
+    const question = questionMapping[index].question;
+    document.getElementById('selected-question').innerHTML = text2Markdown('**Question:** ' + question);
+    displayAnswers(index);
+}
+
+function displayAnswers(index) {
+    const question = questionMapping[index];
+    const otherModel = document.getElementById('model-select').value;
+    // render the answers with markdown
+    document.getElementById('other-model-answer').innerHTML = text2Markdown(question.answers[otherModel]);
+    document.getElementById('our-model-answer').innerHTML = text2Markdown(question.answers.vicuna);
+
+    // Display evaluation
+    score = question.scores[otherModel];
+    score_text = modelNameMapping[otherModel] + " " + score[0] + "/10, Vicuna-13b " + score[1] + "/10";
+    document.getElementById('evaluation-header').textContent = "GPT-4 Evaluation" + " (Score: " + score_text + ")";
+    document.getElementById('evaluation-result').innerHTML = text2Markdown(question.evaluations[otherModel]);
+
+    // Update model names
+    let assistant1_title = "Assistant #1"; // (" + modelNameMapping[otherModel] + ")";
+    let assistant2_title = "Assistant #2 (Vicuna-13b, our model)";
+    // Update scores/labels.
+    let assistant1_score_label = score[0].toString() + '/10';
+    let assistant2_score_label = score[1].toString() + '/10';
+
+    const colorRed ='#fa9'; // '#eb978d';
+    // const colorGreen = '#c9f2c9';
+    const colorBlue = '#8ef'; // '#71dbf9';
+    const colorYellow = '#fe7'; // '#fada57';
+    let otherModelHeaderColor = '';
+    let ourModelHeaderColor = '';
+    // Update the winner.
+    if (score[0] == score[1]) {
+        assistant1_title = '🏆 ' + assistant1_title;
+        assistant1_score_label = '🏆 ' + assistant1_score_label;
+        assistant2_title = '🏆 ' + assistant2_title;
+        assistant2_score_label = '🏆 ' + assistant2_score_label;
+        otherModelHeaderColor = colorYellow;
+        ourModelHeaderColor = colorYellow;
+    } else if (score[0] > score[1]) {
+        assistant1_title = '🏆 ' + assistant1_title;
+        assistant1_score_label = '🏆 ' + assistant1_score_label;
+        otherModelHeaderColor = colorBlue;
+        ourModelHeaderColor = colorRed;
+    } else if (score[0] < score[1]) {
+        assistant2_title = '🏆 ' + assistant2_title;
+        assistant2_score_label = '🏆 ' + assistant2_score_label;
+        otherModelHeaderColor = colorRed;
+        ourModelHeaderColor = colorBlue;
+    }
+
+    document.getElementById('other-model-header-bg').style.backgroundColor = otherModelHeaderColor;
+    document.getElementById('our-model-header').style.backgroundColor = ourModelHeaderColor;
+
+    document.getElementById('other-model-header').textContent = assistant1_title;
+    document.getElementById('our-model-header').textContent = assistant2_title;
+
+    document.getElementById('other-score-label').textContent = assistant1_score_label;
+    document.getElementById('our-score-label').textContent = assistant2_score_label;
+
+    // Update expand buttons visibility for both cards after displaying answers
+    // Reset the expanded state and update expand buttons visibility for both cards after displaying answers
+    document.querySelectorAll('.expandable-card').forEach(card => {
+        card.classList.remove('expanded');
+        updateExpandButtonVisibility(card);
+        const expandBtn = card.querySelector('.expand-btn');
+        expandBtn.innerHTML = '<i class="material-icons" style="pointer-events: none">keyboard_arrow_down</i> Show more';   // .textContent = 'Show more';
+    });
+}
+
+document.getElementById('question-select').addEventListener('change', e => {
+    currentQuestionIndex = parseInt(e.target.value);
+    displayQuestion(currentQuestionIndex);
+});
+
+document.getElementById('category-select').addEventListener('change', e => {
+    let currentCategory = e.target.value;
+    const questionIds = categoryMapping[currentCategory];
+    currentQuestionIndex = questionIds[0];
+    updateQuestionSelect(currentQuestionIndex);
+    displayQuestion(currentQuestionIndex);
+});
+
+// Update expand buttons whenever the model is changed
+document.getElementById('model-select').addEventListener('change', () => {
+    displayAnswers(currentQuestionIndex);
+    document.querySelectorAll('.expandable-card').forEach(card => {
+        updateExpandButtonVisibility(card);
+    });
+    updateModelSelect();
+});
+
+function switchQuestionAndCategory() {
+    document.getElementById('question-select').value = currentQuestionIndex;
+    old_category = document.getElementById('category-select').value;
+    new_category = questionMapping[currentQuestionIndex].category;
+    if (old_category != new_category) {
+        document.getElementById('category-select').value = new_category;
+        updateQuestionSelect(currentQuestionIndex);
+    }
+    displayQuestion(currentQuestionIndex);
+}
+
+document.getElementById('prev-question').addEventListener('click', () => {
+    // Question index starts from 1.
+    currentQuestionIndex = Math.max(1, currentQuestionIndex - 1);
+    switchQuestionAndCategory();
+});
+
+document.getElementById('next-question').addEventListener('click', () => {
+    // Question index starts from 1.
+    currentQuestionIndex = Math.min(questionsCount, currentQuestionIndex + 1);
+    switchQuestionAndCategory();
+});
+
+function updateExpandButtonVisibility(card) {
+    const cardTextContainer = card.querySelector('.card-text-container');
+    const expandBtn = card.querySelector('.expand-btn');
+    if (cardTextContainer.scrollHeight > cardTextContainer.offsetHeight) {
+        expandBtn.style.display = 'flex';
+    } else {
+        expandBtn.style.display = 'none';
+        card.classList.add('expanded');
+    }
+}
+
+document.querySelectorAll('.expand-btn').forEach(btn => {
+    btn.addEventListener('click', e => {
+        const card = e.target.closest('.expandable-card');
+        card.classList.toggle('expanded');
+        const more = '<i class="material-icons" style="pointer-events: none">keyboard_arrow_down</i> Show more';
+        const less = '<i class="material-icons" style="pointer-events: none">keyboard_arrow_up</i> Show less';
+        e.target.innerHTML = card.classList.contains('expanded') ? less : more;
+    });
+});
diff --git a/llava/eval/webpage/styles.css b/llava/eval/webpage/styles.css
new file mode 100644
index 0000000..7b6d6fc
--- /dev/null
+++ b/llava/eval/webpage/styles.css
@@ -0,0 +1,105 @@
+body {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    background-color: #f8f9fa;
+}
+
+.navbar-dark .navbar-nav .nav-link {
+    color: #f1cf68;
+    font-size: 1.1rem;
+    padding: 0.5rem 0.6rem;
+}
+
+.card-header {
+    font-weight: bold;
+}
+
+.card {
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    transition: 0.3s;
+}
+
+.card:hover {
+    box-shadow: 0 8px 16px rgba(0, 0, 0, 0.2);
+}
+
+button {
+    transition: background-color 0.3s;
+}
+
+button:hover {
+    background-color: #007bff;
+}
+
+@media (max-width: 767px) {
+    .form-row .form-group {
+        margin-bottom: 10px;
+    }
+}
+
+/* Extra styles */
+
+.expandable-card .card-text-container {
+    max-height: 200px;
+    overflow-y: hidden;
+    position: relative;
+}
+
+.expandable-card.expanded .card-text-container {
+    max-height: none;
+}
+
+.expand-btn {
+    position: relative;
+    display: none;
+    background-color: rgba(255, 255, 255, 0.8);
+    color: #510c75;
+    border-color: transparent;
+}
+
+.expand-btn:hover {
+    background-color: rgba(200, 200, 200, 0.8);
+    text-decoration: none;
+    border-color: transparent;
+    color: #510c75;
+}
+
+.expand-btn:focus {
+    outline: none;
+    text-decoration: none;
+}
+
+.expandable-card:not(.expanded) .card-text-container:after {
+    content: "";
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    width: 100%;
+    height: 90px;
+    background: linear-gradient(rgba(255, 255, 255, 0.2), rgba(255, 255, 255, 1));
+}
+
+.expandable-card:not(.expanded) .expand-btn {
+    margin-top: -40px;
+}
+
+.card-body {
+    padding-bottom: 5px;
+}
+
+.vertical-flex-layout {
+    justify-content: center;
+    align-items: center;
+    height: 100%;
+    display: flex;
+    flex-direction: column;
+    gap: 5px;
+}
+
+.figure-img {
+    max-width: 100%;
+    height: auto;
+}
+
+.adjustable-font-size {
+    font-size: calc(0.5rem + 2vw);
+}
diff --git a/llava/mm_utils.py b/llava/mm_utils.py
new file mode 100644
index 0000000..1027216
--- /dev/null
+++ b/llava/mm_utils.py
@@ -0,0 +1,123 @@
+from PIL import Image
+from io import BytesIO
+import base64
+
+import torch
+from transformers import StoppingCriteria
+from llava.constants import X_INDEX_TOKEN
+
+
+def load_image_from_base64(image):
+    return Image.open(BytesIO(base64.b64decode(image)))
+
+
+def expand2square(pil_img, background_color):
+    width, height = pil_img.size
+    if width == height:
+        return pil_img
+    elif width > height:
+        result = Image.new(pil_img.mode, (width, width), background_color)
+        result.paste(pil_img, (0, (width - height) // 2))
+        return result
+    else:
+        result = Image.new(pil_img.mode, (height, height), background_color)
+        result.paste(pil_img, ((height - width) // 2, 0))
+        return result
+
+
+def process_images(images, image_processor, model_cfg):
+    image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None)
+    new_images = []
+    if image_aspect_ratio == 'pad':
+        for image in images:
+            image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean))
+            image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
+            new_images.append(image)
+    else:
+        return image_processor(images, return_tensors='pt')['pixel_values']
+    if all(x.shape == new_images[0].shape for x in new_images):
+        new_images = torch.stack(new_images, dim=0)
+    return new_images
+
+
+# def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
+#     prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
+#
+#     def insert_separator(X, sep):
+#         return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
+#
+#     input_ids = []
+#     offset = 0
+#     if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
+#         offset = 1
+#         input_ids.append(prompt_chunks[0][0])
+#
+#     for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
+#         input_ids.extend(x[offset:])
+#
+#     if return_tensors is not None:
+#         if return_tensors == 'pt':
+#             return torch.tensor(input_ids, dtype=torch.long)
+#         raise ValueError(f'Unsupported tensor type: {return_tensors}')
+#     return input_ids
+
+
+def tokenizer_X_token(prompt, tokenizer, X_token_index, return_tensors=None):
+    prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split(f'<{X_INDEX_TOKEN[X_token_index].lower()}>')]
+
+    def insert_separator(X, sep):
+        return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
+
+    input_ids = []
+    offset = 0
+    if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
+        offset = 1
+        input_ids.append(prompt_chunks[0][0])
+
+    for x in insert_separator(prompt_chunks, [X_token_index] * (offset + 1)):
+        input_ids.extend(x[offset:])
+
+    if return_tensors is not None:
+        if return_tensors == 'pt':
+            return torch.tensor(input_ids, dtype=torch.long)
+        raise ValueError(f'Unsupported tensor type: {return_tensors}')
+    return input_ids
+
+def get_model_name_from_path(model_path):
+    model_path = model_path.strip("/")
+    model_paths = model_path.split("/")
+    if model_paths[-1].startswith('checkpoint-'):
+        return model_paths[-2] + "_" + model_paths[-1]
+    else:
+        return model_paths[-1]
+
+
+
+
+class KeywordsStoppingCriteria(StoppingCriteria):
+    def __init__(self, keywords, tokenizer, input_ids):
+        self.keywords = keywords
+        self.keyword_ids = []
+        self.max_keyword_len = 0
+        for keyword in keywords:
+            cur_keyword_ids = tokenizer(keyword).input_ids
+            if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
+                cur_keyword_ids = cur_keyword_ids[1:]
+            if len(cur_keyword_ids) > self.max_keyword_len:
+                self.max_keyword_len = len(cur_keyword_ids)
+            self.keyword_ids.append(torch.tensor(cur_keyword_ids))
+        self.tokenizer = tokenizer
+        self.start_len = input_ids.shape[1]
+
+    def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)"  # TODO
+        offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
+        self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
+        for keyword_id in self.keyword_ids:
+            if (output_ids[0, -keyword_id.shape[0]:] == keyword_id).all():
+                return True
+        outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
+        for keyword in self.keywords:
+            if keyword in outputs:
+                return True
+        return False
\ No newline at end of file
diff --git a/llava/model/__init__.py b/llava/model/__init__.py
new file mode 100644
index 0000000..fa79960
--- /dev/null
+++ b/llava/model/__init__.py
@@ -0,0 +1,2 @@
+from .language_model.llava_llama import LlavaLlamaForCausalLM, LlavaConfig
+from .language_model.llava_mpt import LlavaMPTForCausalLM, LlavaMPTConfig
diff --git a/llava/model/apply_delta.py b/llava/model/apply_delta.py
new file mode 100644
index 0000000..666dd96
--- /dev/null
+++ b/llava/model/apply_delta.py
@@ -0,0 +1,48 @@
+"""
+Usage:
+python3 -m fastchat.model.apply_delta --base ~/model_weights/llama-7b --target ~/model_weights/vicuna-7b --delta lmsys/vicuna-7b-delta
+"""
+import argparse
+
+import torch
+from tqdm import tqdm
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from llava import LlavaLlamaForCausalLM
+
+
+def apply_delta(base_model_path, target_model_path, delta_path):
+    print("Loading base model")
+    base = AutoModelForCausalLM.from_pretrained(
+        base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+
+    print("Loading delta")
+    delta = LlavaLlamaForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+    delta_tokenizer = AutoTokenizer.from_pretrained(delta_path)
+
+    print("Applying delta")
+    for name, param in tqdm(delta.state_dict().items(), desc="Applying delta"):
+        if name not in base.state_dict():
+            assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model'
+            continue
+        if param.data.shape == base.state_dict()[name].shape:
+            param.data += base.state_dict()[name]
+        else:
+            assert name in ['model.embed_tokens.weight', 'lm_head.weight'], \
+                f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}'
+            bparam = base.state_dict()[name]
+            param.data[:bparam.shape[0], :bparam.shape[1]] += bparam
+
+    print("Saving target model")
+    delta.save_pretrained(target_model_path)
+    delta_tokenizer.save_pretrained(target_model_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--base-model-path", type=str, required=True)
+    parser.add_argument("--target-model-path", type=str, required=True)
+    parser.add_argument("--delta-path", type=str, required=True)
+
+    args = parser.parse_args()
+
+    apply_delta(args.base_model_path, args.target_model_path, args.delta_path)
diff --git a/llava/model/builder.py b/llava/model/builder.py
new file mode 100644
index 0000000..cf0cff4
--- /dev/null
+++ b/llava/model/builder.py
@@ -0,0 +1,173 @@
+#    Copyright 2023 Haotian Liu
+#
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
+
+
+import os
+import warnings
+import shutil
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
+import torch
+from llava.model import *
+from llava.constants import DEFAULT_X_PATCH_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+
+
+def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda"):
+    kwargs = {"device_map": device_map,
+              # "offload_folder": model_path,
+              "cache_dir": r'./'
+              }
+
+    if load_8bit:
+        kwargs['load_in_8bit'] = True
+    elif load_4bit:
+        kwargs['load_in_4bit'] = True
+        kwargs['quantization_config'] = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type='nf4'
+        )
+    else:
+        kwargs['torch_dtype'] = torch.float16
+
+    if 'llava' in model_name.lower():
+        # Load LLaVA model
+        if 'lora' in model_name.lower() and model_base is None:
+            warnings.warn('There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.')
+        if 'lora' in model_name.lower() and model_base is not None:
+            lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
+            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
+            print('Loading LLaVA from base model...')
+            model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
+            token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
+            if model.lm_head.weight.shape[0] != token_num:
+                model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
+                model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
+
+            print('Loading additional LLaVA weights...')
+            if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
+                non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu')
+            else:
+                # this is probably from HF Hub
+                from huggingface_hub import hf_hub_download
+                def load_from_hf(repo_id, filename, subfolder=None):
+                    cache_file = hf_hub_download(
+                        repo_id=repo_id,
+                        filename=filename,
+                        subfolder=subfolder)
+                    return torch.load(cache_file, map_location='cpu')
+                non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
+            non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()}
+            if any(k.startswith('model.model.') for k in non_lora_trainables):
+                non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()}
+            model.load_state_dict(non_lora_trainables, strict=False)
+
+            from peft import PeftModel
+            print('Loading LoRA weights...')
+            model = PeftModel.from_pretrained(model, model_path)
+            print('Merging LoRA weights...')
+            model = model.merge_and_unload()
+            print('Model is loaded...')
+        elif model_base is not None:
+            # this may be mm projector only
+            print('Loading LLaVA from base model...')
+            if 'mpt' in model_name.lower():
+                if not os.path.isfile(os.path.join(model_path, 'configuration_mpt.py')):
+                    shutil.copyfile(os.path.join(model_base, 'configuration_mpt.py'), os.path.join(model_path, 'configuration_mpt.py'))
+                tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
+                cfg_pretrained = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+                model = LlavaMPTForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
+            else:
+                tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
+                cfg_pretrained = AutoConfig.from_pretrained(model_path)
+                model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
+
+            mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
+            mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
+            model.load_state_dict(mm_projector_weights, strict=False)
+        else:
+            if 'mpt' in model_name.lower():
+                tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
+                model = LlavaMPTForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
+            else:
+                tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+                # config = AutoConfig.from_pretrained(model_path)
+                # model1 = LlavaLlamaForCausalLM(config)
+                # a = torch.load(rf'{model_path}/pytorch_model-00001-of-00003.bin')
+                # b = torch.load(rf'{model_path}/pytorch_model-00002-of-00003.bin')
+                # c = torch.load(rf'{model_path}/pytorch_model-00003-of-00003.bin')
+                # model1.load_state_dict(a, strict=False)
+                # model1.load_state_dict(b, strict=False)
+                # model1.load_state_dict(c, strict=False)
+                model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
+                print()
+    else:
+        # Load language model
+        if model_base is not None:
+            # PEFT model
+            from peft import PeftModel
+            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
+            model = AutoModelForCausalLM.from_pretrained(model_base, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
+            print(f"Loading LoRA weights from {model_path}")
+            model = PeftModel.from_pretrained(model, model_path)
+            print(f"Merging weights")
+            model = model.merge_and_unload()
+            print('Convert to FP16...')
+            model.to(torch.float16)
+        else:
+            use_fast = False
+            if 'mpt' in model_name.lower():
+                tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
+                model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs)
+            else:
+                tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+                model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
+
+    processor = {}
+    if 'llava' in model_name.lower():
+        mm_use_x_start_end = getattr(model.config, "mm_use_x_start_end", False)
+        mm_use_x_patch_token = getattr(model.config, "mm_use_x_patch_token", True)
+        X = model.config.X
+        if mm_use_x_patch_token:
+            for x in X:
+                tokenizer.add_tokens([DEFAULT_X_PATCH_TOKEN[x.upper()]], special_tokens=True)
+        if mm_use_x_start_end:
+            for x in X:
+                tokenizer.add_tokens([DEFAULT_X_START_TOKEN[x.upper()], DEFAULT_X_END_TOKEN[x.upper()]], special_tokens=True)
+        model.resize_token_embeddings(len(tokenizer))
+        print(X)    
+        if 'Image' in X:
+            image_tower = model.get_image_tower()
+            if not image_tower.is_loaded:
+                image_tower.load_model()
+            image_tower.to(device=device, dtype=torch.float16)
+            image_processor = image_tower.image_processor
+            processor['image'] = image_processor
+
+        if 'Video' in X:
+            video_tower = model.get_video_tower()
+            if not video_tower.is_loaded:
+                video_tower.load_model()
+            video_tower.to(device=device, dtype=torch.float16)
+            video_processor = video_tower.video_processor
+            processor['video'] = video_processor
+
+    if hasattr(model.config, "max_sequence_length"):
+        context_len = model.config.max_sequence_length
+    else:
+        context_len = 2048
+
+    return tokenizer, model, processor, context_len
+    # return tokenizer, model1, processor, context_len
diff --git a/llava/model/consolidate.py b/llava/model/consolidate.py
new file mode 100644
index 0000000..1e32421
--- /dev/null
+++ b/llava/model/consolidate.py
@@ -0,0 +1,29 @@
+"""
+Usage:
+python3 -m llava.model.consolidate --src ~/model_weights/llava-7b --dst ~/model_weights/llava-7b_consolidate
+"""
+import argparse
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from llava.model import *
+from llava.model.utils import auto_upgrade
+
+
+def consolidate_ckpt(src_path, dst_path):
+    print("Loading model")
+    auto_upgrade(src_path)
+    src_model = AutoModelForCausalLM.from_pretrained(src_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+    src_tokenizer = AutoTokenizer.from_pretrained(src_path, use_fast=False)
+    src_model.save_pretrained(dst_path)
+    src_tokenizer.save_pretrained(dst_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--src", type=str, required=True)
+    parser.add_argument("--dst", type=str, required=True)
+
+    args = parser.parse_args()
+
+    consolidate_ckpt(args.src, args.dst)
diff --git a/llava/model/language_model/llava_llama.py b/llava/model/language_model/llava_llama.py
new file mode 100644
index 0000000..ef032cd
--- /dev/null
+++ b/llava/model/language_model/llava_llama.py
@@ -0,0 +1,145 @@
+#    Copyright 2023 Haotian Liu
+#
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
+import time
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+
+from transformers import AutoConfig, AutoModelForCausalLM, \
+                         LlamaConfig, LlamaModel, LlamaForCausalLM
+
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
+
+
+class LlavaConfig(LlamaConfig):
+    model_type = "llava"
+
+
+class LlavaLlamaModel(LlavaMetaModel, LlamaModel):
+    config_class = LlavaConfig
+
+    def __init__(self, config: LlamaConfig):
+        super(LlavaLlamaModel, self).__init__(config)
+
+
+class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM):
+    config_class = LlavaConfig
+
+    def __init__(self, config):
+        super(LlamaForCausalLM, self).__init__(config)
+        self.model = LlavaLlamaModel(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_model(self):
+        return self.model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        images: Optional[torch.FloatTensor] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        # print(222222222222222222222222222222222222222222222)
+        input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)
+        # input_ids已经没了，因为text已经融合到inputs_embeds里面：前text+img+后text，总共大几百个token
+        # 之后的input_ids是上一轮预测得token，图片和之前得文本信息融到past_key_values
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        
+        # print(66666666666666666666666)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict
+        )
+
+        # print(777777777)
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        # print(88888888888888)
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model/pipeline parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        # print(99999999999)
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+                "images": kwargs.get("images", None),
+            }
+        )
+        return model_inputs
+
+AutoConfig.register("llava", LlavaConfig)
+AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM)
\ No newline at end of file
diff --git a/llava/model/language_model/llava_llama_v1.py b/llava/model/language_model/llava_llama_v1.py
new file mode 100644
index 0000000..1419d20
--- /dev/null
+++ b/llava/model/language_model/llava_llama_v1.py
@@ -0,0 +1,155 @@
+#    Copyright 2023 Haotian Liu
+#
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
+import time
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+
+from transformers import AutoConfig, AutoModelForCausalLM, \
+                         LlamaConfig, LlamaModel, LlamaForCausalLM
+
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
+
+
+class LlavaConfig(LlamaConfig):
+    model_type = "llava"
+
+
+class LlavaLlamaModel(LlavaMetaModel, LlamaModel):
+    config_class = LlavaConfig
+
+    def __init__(self, config: LlamaConfig):
+        super(LlavaLlamaModel, self).__init__(config)
+
+
+class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM):
+    config_class = LlavaConfig
+
+    def __init__(self, config):
+        super(LlamaForCausalLM, self).__init__(config)
+        self.model = LlavaLlamaModel(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_model(self):
+        return self.model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        images: Optional[torch.FloatTensor] = None,
+        paths: Optional[str] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        # print(paths)
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        
+        # print(images)
+        if past_key_values is None:
+            input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images, paths)
+        else:
+            input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)
+        # input_ids已经没了，因为text已经融合到inputs_embeds里面：前text+img+后text，总共大几百个token
+        # 之后的input_ids是上一轮预测得token，图片和之前得文本信息融到past_key_values
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        
+        # print(66666666666666666666666)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict
+        )
+
+        # print(777777777)
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        # print(88888888888888)
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model/pipeline parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        # print(99999999999)
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+            
+        if past_key_values is None:
+            model_inputs.update({"paths": kwargs.get("paths", None)})
+
+        # print(kwargs.get("paths", None))
+        model_inputs.update(
+            {
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+                "images": kwargs.get("images", None),
+            }
+        )
+        return model_inputs
+
+AutoConfig.register("llava", LlavaConfig)
+AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM)
diff --git a/llava/model/language_model/llava_mpt.py b/llava/model/language_model/llava_mpt.py
new file mode 100644
index 0000000..39dc880
--- /dev/null
+++ b/llava/model/language_model/llava_mpt.py
@@ -0,0 +1,113 @@
+#    Copyright 2023 Haotian Liu
+#
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
+
+
+from typing import List, Optional, Tuple
+import warnings
+
+import torch
+import torch.nn.functional as F
+import math
+
+from transformers import AutoConfig, AutoModelForCausalLM
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from .mpt.modeling_mpt import MPTConfig, MPTForCausalLM, MPTModel
+from llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
+
+
+class LlavaMPTConfig(MPTConfig):
+    model_type = "llava_mpt"
+
+
+class LlavaMPTModel(LlavaMetaModel, MPTModel):
+    config_class = LlavaMPTConfig
+
+    def __init__(self, config: MPTConfig):
+        config.hidden_size = config.d_model
+        super(LlavaMPTModel, self).__init__(config)
+    
+    def embed_tokens(self, x):
+        return self.wte(x)
+
+
+class LlavaMPTForCausalLM(MPTForCausalLM, LlavaMetaForCausalLM):
+    config_class = LlavaMPTConfig
+    supports_gradient_checkpointing = True
+
+    def __init__(self, config):
+        super(MPTForCausalLM, self).__init__(config)
+
+        if not config.tie_word_embeddings:
+            raise ValueError('MPTForCausalLM only supports tied word embeddings')
+        self.transformer = LlavaMPTModel(config)
+        self.logit_scale = None
+        if config.logit_scale is not None:
+            logit_scale = config.logit_scale
+            if isinstance(logit_scale, str):
+                if logit_scale == 'inv_sqrt_d_model':
+                    logit_scale = 1 / math.sqrt(config.d_model)
+                else:
+                    raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
+            self.logit_scale = logit_scale
+
+    def get_model(self):
+        return self.transformer
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, LlavaMPTModel):
+            module.gradient_checkpointing = value
+
+    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, images=None):
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)
+        outputs = self.transformer(input_ids=input_ids, inputs_embeds=inputs_embeds, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache)
+        # FIXME: this is a hack to fix the multiple gpu inference issue in https://github.com/haotian-liu/LLaVA/issues/338
+        logits = F.linear(outputs.last_hidden_state.to(self.transformer.wte.weight.device), self.transformer.wte.weight)
+        if self.logit_scale is not None:
+            if self.logit_scale == 0:
+                warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.')
+            logits *= self.logit_scale
+        loss = None
+        if labels is not None:
+            labels = torch.roll(labels, shifts=-1)
+            labels[:, -1] = -100
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1))
+        return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states)
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
+        if inputs_embeds is not None:
+            raise NotImplementedError('inputs_embeds is not implemented for MPT yet')
+        attention_mask = kwargs['attention_mask'].bool()
+        if attention_mask[:, -1].sum() != attention_mask.shape[0]:
+            raise NotImplementedError('MPT does not support generation with right padding.')
+        if self.transformer.attn_uses_sequence_id and self.training:
+            sequence_id = torch.zeros_like(input_ids[:1])
+        else:
+            sequence_id = None
+        if past_key_values is not None:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+        if self.transformer.prefix_lm:
+            prefix_mask = torch.ones_like(attention_mask)
+            if kwargs.get('use_cache') == False:
+                raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.')
+        else:
+            prefix_mask = None
+        return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True), "images": kwargs.get("images", None)}
+
+
+AutoConfig.register("llava_mpt", LlavaMPTConfig)
+AutoModelForCausalLM.register(LlavaMPTConfig, LlavaMPTForCausalLM)
diff --git a/llava/model/llava_arch.py b/llava/model/llava_arch.py
new file mode 100644
index 0000000..96431c8
--- /dev/null
+++ b/llava/model/llava_arch.py
@@ -0,0 +1,346 @@
+#    Copyright 2023 Haotian Liu
+#
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
+
+
+from abc import ABC, abstractmethod
+
+import torch
+import torch.nn as nn
+
+from .multimodal_encoder.builder import build_image_tower, build_video_tower
+from .multimodal_projector.builder import build_vision_projector
+
+from llava.constants import IGNORE_INDEX, X_TOKEN_INDEX, DEFAULT_X_PATCH_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+
+
+class LlavaMetaModel:
+
+    def __init__(self, config):
+        super(LlavaMetaModel, self).__init__(config)
+
+        if hasattr(config, "mm_image_tower"):
+            self.image_tower = build_image_tower(config, delay_load=True)
+            self.mm_projector = build_vision_projector(config)
+        if hasattr(config, "mm_video_tower"):
+            self.video_tower = build_video_tower(config, delay_load=True)
+            self.mm_projector = build_vision_projector(config)
+
+    def get_image_tower(self):
+        image_tower = getattr(self, 'image_tower', None)
+        if type(image_tower) is list:
+            image_tower = image_tower[0]
+        return image_tower
+
+    def get_video_tower(self):
+        video_tower = getattr(self, 'video_tower', None)
+        if type(video_tower) is list:
+            video_tower = video_tower[0]
+        return video_tower
+
+    def initialize_image_modules(self, model_args, fsdp=None):
+        image_tower = model_args.image_tower
+        mm_vision_select_layer = model_args.mm_vision_select_layer
+        mm_vision_select_feature = model_args.mm_vision_select_feature
+        pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
+
+        self.config.mm_image_tower = image_tower
+
+        image_tower = build_image_tower(model_args)
+
+        if fsdp is not None and len(fsdp) > 0:
+            self.image_tower = [image_tower]
+        else:
+            self.image_tower = image_tower
+
+        self.config.use_mm_proj = True
+        self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
+        self.config.mm_hidden_size = image_tower.hidden_size
+        self.config.mm_vision_select_layer = mm_vision_select_layer
+        self.config.mm_vision_select_feature = mm_vision_select_feature
+
+        self.mm_projector = build_vision_projector(self.config)
+
+        if pretrain_mm_mlp_adapter is not None:
+            mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
+            def get_w(weights, keyword):
+                return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
+
+            self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
+
+    def initialize_video_modules(self, model_args, fsdp=None):
+        video_tower = model_args.video_tower
+        mm_vision_select_layer = model_args.mm_vision_select_layer
+        mm_vision_select_feature = model_args.mm_vision_select_feature
+        pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
+
+        self.config.mm_video_tower = video_tower
+
+        video_tower = build_video_tower(model_args)
+
+        if fsdp is not None and len(fsdp) > 0:
+            self.video_tower = [video_tower]
+        else:
+            self.video_tower = video_tower
+
+        self.config.use_mm_proj = True
+        self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
+        self.config.mm_hidden_size = video_tower.hidden_size
+        self.config.mm_vision_select_layer = mm_vision_select_layer
+        self.config.mm_vision_select_feature = mm_vision_select_feature
+
+        self.mm_projector = build_vision_projector(self.config)
+
+        if pretrain_mm_mlp_adapter is not None:
+            mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
+            def get_w(weights, keyword):
+                return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
+
+            self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
+
+class LlavaMetaForCausalLM(ABC):
+
+    @abstractmethod
+    def get_model(self):
+        pass
+
+    def get_image_tower(self):
+        return self.get_model().get_image_tower()
+
+    def get_video_tower(self):
+        return self.get_model().get_video_tower()
+
+    def get_all_tower(self, keys):
+        tower = {key: getattr(self, f'get_{key}_tower') for key in keys}
+        return tower
+
+    def encode_images(self, images):
+        image_features = self.get_model().get_image_tower()(images)
+        image_features = self.get_model().mm_projector(image_features)
+        return image_features
+
+    def encode_videos(self, videos):
+        video_features = self.get_model().get_video_tower()(videos)
+        print("1111",video_features.shape)
+        video_features = self.get_model().mm_projector(video_features)
+        print("2222",video_features.shape)
+        import pdb; pdb.set_trace()
+        return video_features
+
+    def prepare_inputs_labels_for_multimodal(
+        self, input_ids, attention_mask, past_key_values, labels, X_modalities
+    ):
+        '''
+        X_modalities [
+        [img_feature, img_feature, video_feature, audio_feature],
+        ['image', 'image', 'video', 'audio']
+        ]
+        '''
+        Xs, keys = X_modalities
+        all_tower = self.get_all_tower(set(keys)) if len(keys) > 0 else None
+        
+        # print(2.5)
+        if all_tower is None or X_modalities[0][0] is None or input_ids.shape[1] == 1:
+            if past_key_values is not None and all_tower is not None and Xs is not None and input_ids.shape[1] == 1:
+                attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device)
+            return input_ids, attention_mask, past_key_values, None, labels
+
+
+        # print(keys)
+        # X_features = [getattr(self, f'encode_{key}s')(X.unsqueeze(0)) for X, key in zip(Xs, keys)]  # expand to get batchsize
+        # X_features = [x.flatten(0, 1) for x in X_features]
+        # print(X_features[0].size())
+        # import pdb; pdb.set_trace()
+        
+        X_features = []
+        # Xs = [Xs]
+        
+        for X, key in zip(Xs, keys):
+            stackX = torch.stack(X, dim=0).view(3,3,8,224,224)
+            # stackX = torch.stack(X, dim=0)
+            # print(stackX.shape)
+            
+            encoded_feature = getattr(self, f'encode_{key}s')(stackX)
+            print("Feature Dim", encoded_feature.shape)
+            X_features.append(encoded_feature)
+            
+        # flattened_X_features = []
+        # for x in X_features:
+        #     flattened_feature = x.flatten(0, 1)
+        #     flattened_X_features.append(flattened_feature)
+
+        # X_features = flattened_X_features
+
+
+        new_input_embeds = []
+        new_labels = [] if labels is not None else None
+        
+        # print("BATCH size", len(X_features), input_ids.shape)
+        for batch_idx, cur_input_ids in enumerate(input_ids):
+            cur_X_idx = 0
+            if (torch.any(torch.stack([cur_input_ids == X_TOKEN_INDEX[key.upper()] for key in keys]), dim=0)).sum() == 0:
+                # multimodal LLM, but the current sample is not multimodal
+                # FIXME: this is a hacky fix, for deepspeed zero3 to work
+                half_len = cur_input_ids.shape[0] // 2
+                cur_X_features = X_features[cur_X_idx]
+                cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len])
+                cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:])
+                cur_input_embeds = torch.cat([cur_input_embeds_1, cur_X_features[0:0], cur_input_embeds_2], dim=0)
+                new_input_embeds.append(cur_input_embeds)
+                if labels is not None:
+                    new_labels.append(labels[batch_idx])
+                cur_X_idx += 1 
+                continue
+            X_token_indices = torch.where(torch.any(torch.stack([cur_input_ids == X_TOKEN_INDEX[key.upper()] for key in keys]), dim=0))[0] 
+            # print(batch_idx, cur_input_ids.size(),X_token_indices)
+            cur_new_input_embeds = []
+            if labels is not None:
+                cur_labels = labels[batch_idx]
+                cur_new_labels = []
+                assert cur_labels.shape == cur_input_ids.shape
+            # print(4444444444)
+            while X_token_indices.numel() > 0:
+                cur_X_features = X_features[batch_idx][cur_X_idx]
+                X_token_start = X_token_indices[0]
+                if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_x_start_end', False):
+                    cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:X_token_start-1]).detach())
+                    cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[X_token_start-1:X_token_start]))
+                    cur_new_input_embeds.append(cur_X_features)
+                    cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[X_token_start+1:X_token_start+2]))
+                    if labels is not None:
+                        cur_new_labels.append(cur_labels[:X_token_start])
+                        cur_new_labels.append(torch.full((cur_X_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
+                        cur_new_labels.append(cur_labels[X_token_start:X_token_start+1])
+                        cur_labels = cur_labels[X_token_start+2:]
+                else:
+                    cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:X_token_start])) 
+                    cur_new_input_embeds.append(cur_X_features)
+                    if labels is not None:
+                        cur_new_labels.append(cur_labels[:X_token_start])
+                        cur_new_labels.append(torch.full((cur_X_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
+                        cur_labels = cur_labels[X_token_start+1:]
+                cur_X_idx += 1
+                if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_x_start_end', False):
+                    cur_input_ids = cur_input_ids[X_token_start+2:]
+                else:
+                    cur_input_ids = cur_input_ids[X_token_start+1:] 
+                X_token_indices = torch.where(torch.any(torch.stack([cur_input_ids == X_TOKEN_INDEX[key.upper()] for key in keys]), dim=0))[0]
+            # print(55555555555555555)
+            if cur_input_ids.numel() > 0:
+                if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_x_start_end', False):
+                    cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids).detach())
+                else:
+                    cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids))
+                if labels is not None:
+                    cur_new_labels.append(cur_labels)
+                    
+            for i in cur_new_input_embeds:
+                print(i.size())
+            cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds] 
+            cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
+            
+            
+            
+            new_input_embeds.append(cur_new_input_embeds)
+            if labels is not None:
+                cur_new_labels = torch.cat(cur_new_labels, dim=0)
+                new_labels.append(cur_new_labels)
+
+        if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
+            max_len = max(x.shape[0] for x in new_input_embeds)
+
+            new_input_embeds_align = []
+            for cur_new_embed in new_input_embeds:
+                cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0)
+                new_input_embeds_align.append(cur_new_embed)
+            new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
+            
+            print("New emb", new_input_embeds.shape)
+            # import pdb;pdb.set_trace()
+
+            if labels is not None:
+                new_labels_align = []
+                _new_labels = new_labels
+                for cur_new_label in new_labels:
+                    cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0)
+                    new_labels_align.append(cur_new_label)
+                new_labels = torch.stack(new_labels_align, dim=0)
+
+            if attention_mask is not None:
+                new_attention_mask = []
+                for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels):
+                    new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device)
+                    new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device)
+                    cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0)
+                    new_attention_mask.append(cur_new_attention_mask)
+                attention_mask = torch.stack(new_attention_mask, dim=0)
+                assert attention_mask.shape == new_labels.shape
+        else:
+            new_input_embeds = torch.stack(new_input_embeds, dim=0)
+            if labels is not None:
+                new_labels  = torch.stack(new_labels, dim=0)
+
+            if attention_mask is not None:
+                new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device)
+                attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1)
+                assert attention_mask.shape == new_input_embeds.shape[:2]
+
+        return None, attention_mask, past_key_values, new_input_embeds, new_labels
+
+    def initialize_X_tokenizer(self, model_args, tokenizer):
+        if model_args.mm_use_x_patch_token:
+            for x in model_args.X:
+                tokenizer.add_tokens([DEFAULT_X_PATCH_TOKEN[x.upper()]], special_tokens=True)
+            # tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
+            self.resize_token_embeddings(len(tokenizer))
+
+        if model_args.mm_use_x_start_end:
+            num_new_tokens = 0
+            for x in model_args.X:
+                num_new_tokens += tokenizer.add_tokens([DEFAULT_X_START_TOKEN[x.upper()], DEFAULT_X_END_TOKEN[x.upper()]], special_tokens=True)
+            self.resize_token_embeddings(len(tokenizer))
+
+            if num_new_tokens > 0:
+                input_embeddings = self.get_input_embeddings().weight.data
+                output_embeddings = self.get_output_embeddings().weight.data
+
+                input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
+                    dim=0, keepdim=True)
+                output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
+                    dim=0, keepdim=True)
+
+                input_embeddings[-num_new_tokens:] = input_embeddings_avg
+                output_embeddings[-num_new_tokens:] = output_embeddings_avg
+
+            if model_args.tune_mm_mlp_adapter:
+                for p in self.get_input_embeddings().parameters():
+                    p.requires_grad = True
+                for p in self.get_output_embeddings().parameters():
+                    p.requires_grad = False
+
+            if model_args.pretrain_mm_mlp_adapter:
+                mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
+                embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
+                assert num_new_tokens == 2
+                if input_embeddings.shape == embed_tokens_weight.shape:
+                    input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
+                elif embed_tokens_weight.shape[0] == num_new_tokens:
+                    input_embeddings[-num_new_tokens:] = embed_tokens_weight
+                else:
+                    raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
+        elif model_args.mm_use_x_patch_token:
+            if model_args.tune_mm_mlp_adapter:
+                for p in self.get_input_embeddings().parameters():
+                    p.requires_grad = False
+                for p in self.get_output_embeddings().parameters():
+                    p.requires_grad = False
\ No newline at end of file
diff --git a/llava/model/make_delta.py b/llava/model/make_delta.py
new file mode 100644
index 0000000..4ae55d5
--- /dev/null
+++ b/llava/model/make_delta.py
@@ -0,0 +1,52 @@
+"""
+Usage:
+python3 -m llava.model.make_delta --base ~/model_weights/llama-7b --target ~/model_weights/llava-7b --delta ~/model_weights/llava-7b-delta --hub-repo-id liuhaotian/llava-7b-delta
+"""
+import argparse
+
+import torch
+from tqdm import tqdm
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from llava.model.utils import auto_upgrade
+
+
+def make_delta(base_model_path, target_model_path, delta_path, hub_repo_id):
+    print("Loading base model")
+    base = AutoModelForCausalLM.from_pretrained(
+        base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+
+    print("Loading target model")
+    auto_upgrade(target_model_path)
+    target = AutoModelForCausalLM.from_pretrained(target_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+
+    print("Calculating delta")
+    for name, param in tqdm(target.state_dict().items(), desc="Calculating delta"):
+        if name not in base.state_dict():
+            assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model'
+            continue
+        if param.data.shape == base.state_dict()[name].shape:
+            param.data -= base.state_dict()[name]
+        else:
+            assert name in ['model.embed_tokens.weight', 'lm_head.weight'], f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}'
+            bparam = base.state_dict()[name]
+            param.data[:bparam.shape[0], :bparam.shape[1]] -= bparam
+
+    print("Saving delta")
+    if hub_repo_id:
+        kwargs = {"push_to_hub": True, "repo_id": hub_repo_id}
+    else:
+        kwargs = {}
+    target.save_pretrained(delta_path, **kwargs)
+    target_tokenizer = AutoTokenizer.from_pretrained(target_model_path)
+    target_tokenizer.save_pretrained(delta_path, **kwargs)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--base-model-path", type=str, required=True)
+    parser.add_argument("--target-model-path", type=str, required=True)
+    parser.add_argument("--delta-path", type=str, required=True)
+    parser.add_argument("--hub-repo-id", type=str, default=None)
+    args = parser.parse_args()
+
+    make_delta(args.base_model_path, args.target_model_path, args.delta_path, args.hub_repo_id)
diff --git a/llava/model/multimodal_encoder/builder.py b/llava/model/multimodal_encoder/builder.py
new file mode 100644
index 0000000..e44b7c7
--- /dev/null
+++ b/llava/model/multimodal_encoder/builder.py
@@ -0,0 +1,69 @@
+import os
+from .clip_encoder import CLIPVisionTower
+from .languagebind import LanguageBindImageTower, LanguageBindVideoTower
+from .mae_encoder import MAEVisionTower
+from transformers import CLIPModel
+
+def build_image_tower(image_tower_cfg, **kwargs):
+    image_tower = getattr(image_tower_cfg, 'mm_image_tower', getattr(image_tower_cfg, 'image_tower', None))
+    # is_absolute_path_exists = os.path.exists(image_tower)
+    is_absolute_path_exists = False
+    if is_absolute_path_exists or image_tower.startswith("openai") or image_tower.startswith("laion"):
+        return CLIPVisionTower(image_tower, args=image_tower_cfg, **kwargs)
+    if image_tower.endswith('LanguageBind_Image'):
+        return LanguageBindImageTower(image_tower, args=image_tower_cfg, cache_dir='./cache_dir', **kwargs)
+    if 'mae' in image_tower:
+        print('maemaemaemaemaemaemaemae')
+        print('maemaemaemaemaemaemaemae')
+        print('maemaemaemaemaemaemaemae')
+        print('maemaemaemaemaemaemaemae')
+        print('maemaemaemaemaemaemaemae')
+        return MAEVisionTower(image_tower, args=image_tower_cfg, cache_dir='./cache_dir', **kwargs)
+    raise ValueError(f'Unknown image tower: {image_tower}')
+
+def build_video_tower(video_tower_cfg, **kwargs):
+    video_tower = getattr(video_tower_cfg, 'mm_video_tower', getattr(video_tower_cfg, 'video_tower', None))
+
+    if video_tower.endswith('LanguageBind_Video_merge'):
+        return LanguageBindVideoTower(video_tower, args=video_tower_cfg, cache_dir='./cache_dir', **kwargs)
+    raise ValueError(f'Unknown video tower: {video_tower}')
+
+def extractor(**kwargs):
+    video_tower = "./cache_dir/LanguageBind_Video_merge"
+    class VideoTowerConfig:
+        def __init__(self):
+            self.mm_video_tower = "./cache_dir/LanguageBind_Video_merge"
+            self.mm_vision_select_feature = "patch"
+            self.mm_vision_select_layer = -2
+            self.model_type = "llava"
+            self.num_attention_heads = 32
+            self.num_hidden_layers = 32
+            self.num_key_value_heads = 32
+            self.pad_token_id = 0
+            self.pretraining_tp = 1
+            self.rms_norm_eps = 1e-05
+            self.vocab_size = 32000
+
+    video_tower_cfg = VideoTowerConfig()
+    return LanguageBindVideoTower(video_tower, args=video_tower_cfg, cache_dir='./cache_dir', **kwargs)
+
+
+# import os
+# from .clip_encoder import CLIPVisionTower
+# from .languagebind import LanguageBindImageTower, LanguageBindVideoTower
+# from transformers import CLIPModel
+
+# def build_image_tower(image_tower_cfg, **kwargs):
+#     image_tower = getattr(image_tower_cfg, 'mm_image_tower', getattr(image_tower_cfg, 'image_tower', None))
+#     is_absolute_path_exists = os.path.exists(image_tower)
+#     if is_absolute_path_exists or image_tower.startswith("openai") or image_tower.startswith("laion"):
+#         return CLIPVisionTower(image_tower, args=image_tower_cfg, **kwargs)
+#     if image_tower.endswith('LanguageBind_Image'):
+#         return LanguageBindImageTower(image_tower, args=image_tower_cfg, cache_dir='./cache_dir', **kwargs)
+#     raise ValueError(f'Unknown image tower: {image_tower}')
+
+# def build_video_tower(video_tower_cfg, **kwargs):
+#     video_tower = getattr(video_tower_cfg, 'mm_video_tower', getattr(video_tower_cfg, 'video_tower', None))
+#     if video_tower.endswith('LanguageBind_Video'):
+#         return LanguageBindVideoTower(video_tower, args=video_tower_cfg, cache_dir='./cache_dir', **kwargs)
+#     raise ValueError(f'Unknown video tower: {video_tower}')
\ No newline at end of file
diff --git a/llava/model/multimodal_encoder/clip_encoder.py b/llava/model/multimodal_encoder/clip_encoder.py
new file mode 100644
index 0000000..dbb9015
--- /dev/null
+++ b/llava/model/multimodal_encoder/clip_encoder.py
@@ -0,0 +1,78 @@
+import torch
+import torch.nn as nn
+
+from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
+
+
+class CLIPVisionTower(nn.Module):
+    def __init__(self, vision_tower, args, delay_load=False):
+        super().__init__()
+
+        self.is_loaded = False
+
+        self.vision_tower_name = vision_tower
+        self.select_layer = args.mm_vision_select_layer
+        self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
+
+        if not delay_load:
+            self.load_model()
+        else:
+            self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
+
+    def load_model(self):
+        self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
+        self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
+        self.vision_tower.requires_grad_(False)
+
+        self.is_loaded = True
+
+    def feature_select(self, image_forward_outs):
+        image_features = image_forward_outs.hidden_states[self.select_layer]
+        if self.select_feature == 'patch':
+            image_features = image_features[:, 1:]
+        elif self.select_feature == 'cls_patch':
+            image_features = image_features
+        else:
+            raise ValueError(f'Unexpected select feature: {self.select_feature}')
+        return image_features
+
+    @torch.no_grad()
+    def forward(self, images):
+        if type(images) is list:
+            image_features = []
+            for image in images:
+                image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
+                image_feature = self.feature_select(image_forward_out).to(image.dtype)
+                image_features.append(image_feature)
+        else:
+            image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
+            image_features = self.feature_select(image_forward_outs).to(images.dtype)
+
+        return image_features
+
+    @property
+    def dummy_feature(self):
+        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
+
+    @property
+    def dtype(self):
+        return self.vision_tower.dtype
+
+    @property
+    def device(self):
+        return self.vision_tower.device
+
+    @property
+    def config(self):
+        if self.is_loaded:
+            return self.vision_tower.config
+        else:
+            return self.cfg_only
+
+    @property
+    def hidden_size(self):
+        return self.config.hidden_size
+
+    @property
+    def num_patches(self):
+        return (self.config.image_size // self.config.patch_size) ** 2
diff --git a/llava/model/multimodal_encoder/mae_encoder.py b/llava/model/multimodal_encoder/mae_encoder.py
new file mode 100644
index 0000000..377883d
--- /dev/null
+++ b/llava/model/multimodal_encoder/mae_encoder.py
@@ -0,0 +1,80 @@
+import torch
+import torch.nn as nn
+
+from transformers import ViTMAEForPreTraining, AutoConfig, AutoImageProcessor
+
+
+class MAEVisionTower(nn.Module):
+    def __init__(self, vision_tower, args, cache_dir='./cache_dir', delay_load=False):
+        super().__init__()
+
+        self.is_loaded = False
+        self.cache_dir = cache_dir
+        self.vision_tower_name = vision_tower
+        self.select_layer = args.mm_vision_select_layer
+        self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
+
+        if not delay_load:
+            self.load_model()
+        else:
+            self.cfg_only = AutoConfig.from_pretrained(self.vision_tower_name, cache_dir=self.cache_dir)
+
+    def load_model(self):
+        self.image_processor = AutoImageProcessor.from_pretrained(self.vision_tower_name, cache_dir=self.cache_dir)
+        vision_tower = ViTMAEForPreTraining.from_pretrained(self.vision_tower_name, cache_dir=self.cache_dir)
+        self.vision_tower = vision_tower.vit
+        self.vision_tower.requires_grad_(False)
+
+        self.is_loaded = True
+
+    def feature_select(self, image_forward_outs):
+        image_features = image_forward_outs.hidden_states[self.select_layer]
+        if self.select_feature == 'patch':
+            image_features = image_features[:, 1:]
+        elif self.select_feature == 'cls_patch':
+            image_features = image_features
+        else:
+            raise ValueError(f'Unexpected select feature: {self.select_feature}')
+        # print(image_features.shape)
+        return image_features
+
+    @torch.no_grad()
+    def forward(self, images):
+        if type(images) is list:
+            image_features = []
+            for image in images:
+                image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
+                image_feature = self.feature_select(image_forward_out).to(image.dtype)
+                image_features.append(image_feature)
+        else:
+            image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
+            image_features = self.feature_select(image_forward_outs).to(images.dtype)
+
+        return image_features
+
+    @property
+    def dummy_feature(self):
+        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
+
+    @property
+    def dtype(self):
+        return self.vision_tower.dtype
+
+    @property
+    def device(self):
+        return self.vision_tower.device
+
+    @property
+    def config(self):
+        if self.is_loaded:
+            return self.vision_tower.config
+        else:
+            return self.cfg_only
+
+    @property
+    def hidden_size(self):
+        return self.config.hidden_size
+
+    @property
+    def num_patches(self):
+        return (self.config.image_size // self.config.patch_size) ** 2
diff --git a/llava/model/multimodal_projector/builder.py b/llava/model/multimodal_projector/builder.py
new file mode 100644
index 0000000..8cc8e9d
--- /dev/null
+++ b/llava/model/multimodal_projector/builder.py
@@ -0,0 +1,257 @@
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import re
+
+from transformers import PretrainedConfig, Blip2PreTrainedModel, Blip2Config, Blip2QFormerModel
+
+
+class IdentityMap(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, *args, **kwargs):
+        return x
+
+    @property
+    def config(self):
+        return {"mm_projector_type": 'identity'}
+
+
+class SimpleResBlock(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.pre_norm = nn.LayerNorm(channels)
+
+        self.proj = nn.Sequential(
+            nn.Linear(channels, channels),
+            nn.GELU(),
+            nn.Linear(channels, channels)
+        )
+    def forward(self, x):
+        x = self.pre_norm(x)
+        return x + self.proj(x)
+
+
+# def build_vision_projector(config, delay_load=False, **kwargs):
+#     projector_type = getattr(config, 'mm_projector_type', 'linear')
+#
+#     if projector_type == 'linear':
+#         return nn.Linear(config.mm_hidden_size, config.hidden_size)
+#
+#     mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
+#     if mlp_gelu_match:
+#         mlp_depth = int(mlp_gelu_match.group(1))
+#         modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
+#         for _ in range(1, mlp_depth):
+#             modules.append(nn.GELU())
+#             modules.append(nn.Linear(config.hidden_size, config.hidden_size))
+#         return nn.Sequential(*modules)
+#
+#     if projector_type == 'identity':
+#         return IdentityMap()
+#
+#     raise ValueError(f'Unknown projector type: {projector_type}')
+
+
+class Blip2Model(Blip2PreTrainedModel):
+    def __init__(self, config: Blip2Config):
+        super().__init__(config)
+
+        self.query_tokens = nn.Parameter(torch.zeros(1, config.num_query_tokens, config.qformer_config.hidden_size))
+        self.qformer = Blip2QFormerModel(config.qformer_config)
+
+        # self.proj = nn.Linear(config.mm_hidden_size, config.hidden_size)
+        modules = [nn.Linear(config.mm_hidden_size, config.hidden_size), nn.GELU(), nn.Linear(config.hidden_size, config.hidden_size)]
+        self.proj = nn.Sequential(*modules)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        r"""
+        Returns:
+            vision_outputs (`BaseModelOutputWithPooling` or tuple of `torch.FloatTensor`):
+                The vision model outputs. If `return_dict=True`, the output is a [`BaseModelOutputWithPooling`] that
+                contains the image features, the pooled image features and the hidden states if
+                `output_hidden_states=True`.
+        Examples:
+        ```python
+        >>> import torch
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import Blip2Processor, Blip2Model
+
+        >>> device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        >>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
+        >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
+        >>> model.to(device)  # doctest: +IGNORE_RESULT
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+        >>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
+        >>> qformer_outputs = model.get_qformer_features(**inputs)
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # vision_outputs = self.vision_model(
+        #     pixel_values=pixel_values,
+        #     output_attentions=output_attentions,
+        #     output_hidden_states=output_hidden_states,
+        #     return_dict=return_dict,
+        # )
+        #
+        # image_embeds = vision_outputs[0]
+        # image_embeds = self.proj(pixel_values)
+        image_embeds = pixel_values
+
+
+        # print('pixel_values to proj', pixel_values.shape, image_embeds.shape)
+        # step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention
+        image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device)
+
+        query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
+        query_outputs = self.qformer(
+            query_embeds=query_tokens,
+            encoder_hidden_states=image_embeds,
+            encoder_attention_mask=image_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        ).last_hidden_state
+        # print('qformer out', query_outputs.shape)
+        query_outputs = self.proj(query_outputs)
+        return query_outputs
+
+
+def qformer_config_template(config, projector_type):
+    pattern = r"qformer(\d+)_(\d+)"
+
+    match = re.search(pattern, projector_type)
+    num_hidden_layers = int(match.group(1))
+    num_query_tokens = int(match.group(2))
+
+    qformer_config = type('Blip2Config', (PretrainedConfig,), {
+        "initializer_factor": 1.0,
+        "initializer_range": 0.02,
+        "model_type": "blip-2",
+        "num_query_tokens": num_query_tokens,
+        "hidden_size": config.hidden_size,
+        "mm_hidden_size": config.mm_hidden_size,
+        "qformer_config": type('qformer_config', (PretrainedConfig,), {
+            "_name_or_path": "",
+            "add_cross_attention": False,
+            "architectures": None,
+            "attention_probs_dropout_prob": 0.0,
+            "bad_words_ids": None,
+            "begin_suppress_tokens": None,
+            "bos_token_id": None,
+            "chunk_size_feed_forward": 0,
+            "classifier_dropout": None,
+            "cross_attention_frequency": 1,
+            "cross_attention_hidden_size": None,
+            "decoder_start_token_id": None,
+            "diversity_penalty": 0.0,
+            "do_sample": False,
+            "early_stopping": False,
+            "encoder_hidden_size": config.mm_hidden_size,
+            "encoder_no_repeat_ngram_size": 0,
+            "eos_token_id": None,
+            "exponential_decay_length_penalty": None,
+            "finetuning_task": None,
+            "forced_bos_token_id": None,
+            "forced_eos_token_id": None,
+            "hidden_act": "gelu",
+            "hidden_dropout_prob": 0.0,
+            "hidden_size": config.mm_hidden_size,
+            "id2label": {
+                "0": "LABEL_0",
+                "1": "LABEL_1"
+            },
+            "initializer_range": 0.02,
+            "intermediate_size": config.mm_hidden_size * 4,
+            "is_decoder": False,
+            "is_encoder_decoder": False,
+            "label2id": {
+                "LABEL_0": 0,
+                "LABEL_1": 1
+            },
+            "layer_norm_eps": 1e-12,
+            "length_penalty": 1.0,
+            "max_length": 20,
+            "max_position_embeddings": 512,
+            "min_length": 0,
+            "model_type": "blip_2_qformer",
+            "no_repeat_ngram_size": 0,
+            "num_attention_heads": 32,
+            "num_beam_groups": 1,
+            "num_beams": 1,
+            "num_hidden_layers": num_hidden_layers,
+            "num_return_sequences": 1,
+            "output_attentions": False,
+            "output_hidden_states": False,
+            "output_scores": False,
+            "pad_token_id": 0,
+            "position_embedding_type": "absolute",
+            "prefix": None,
+            "problem_type": None,
+            "pruned_heads": {},
+            "remove_invalid_values": False,
+            "repetition_penalty": 1.0,
+            "return_dict": True,
+            "return_dict_in_generate": False,
+            "sep_token_id": None,
+            "suppress_tokens": None,
+            "task_specific_params": None,
+            "temperature": 1.0,
+            "tf_legacy_loss": False,
+            "tie_encoder_decoder": False,
+            "tie_word_embeddings": True,
+            "tokenizer_class": None,
+            "top_k": 50,
+            "top_p": 1.0,
+            "torch_dtype": None,
+            "torchscript": False,
+            "transformers_version": "4.27.0.dev0",
+            "typical_p": 1.0,
+            "use_bfloat16": False,
+            "vocab_size": 30522
+        })()
+    })()
+    return qformer_config
+
+def build_vision_projector(config, delay_load=False, **kwargs):
+    projector_type = getattr(config, 'mm_projector_type', 'linear')
+
+    if projector_type == 'linear':
+        return nn.Linear(config.mm_hidden_size, config.hidden_size)
+
+    elif projector_type == 'identity':
+        return IdentityMap()
+
+    elif projector_type.startswith('qformer'):  # qformer2_64
+        qformer_config = qformer_config_template(config, projector_type)
+        return Blip2Model(qformer_config)
+    else:
+        mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
+        if mlp_gelu_match:
+            mlp_depth = int(mlp_gelu_match.group(1))
+            modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(config.hidden_size, config.hidden_size))
+            return nn.Sequential(*modules)
+
+    raise ValueError(f'Unknown projector type: {projector_type}')
\ No newline at end of file
diff --git a/llava/model/utils.py b/llava/model/utils.py
new file mode 100644
index 0000000..2563f89
--- /dev/null
+++ b/llava/model/utils.py
@@ -0,0 +1,20 @@
+from transformers import AutoConfig
+
+
+def auto_upgrade(config):
+    cfg = AutoConfig.from_pretrained(config)
+    if 'llava' in config and 'llava' not in cfg.model_type:
+        assert cfg.model_type == 'llama'
+        print("You are using newer LLaVA code base, while the checkpoint of v0 is from older code base.")
+        print("You must upgrade the checkpoint to the new code base (this can be done automatically).")
+        confirm = input("Please confirm that you want to upgrade the checkpoint. [Y/N]")
+        if confirm.lower() in ["y", "yes"]:
+            print("Upgrading checkpoint...")
+            assert len(cfg.architectures) == 1
+            setattr(cfg.__class__, "model_type", "llava")
+            cfg.architectures[0] = 'LlavaLlamaForCausalLM'
+            cfg.save_pretrained(config)
+            print("Checkpoint upgraded.")
+        else:
+            print("Checkpoint upgrade aborted.")
+            exit(1)
diff --git a/llava/serve/__init__.py b/llava/serve/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/llava/serve/cli.py b/llava/serve/cli.py
new file mode 100644
index 0000000..4bd366d
--- /dev/null
+++ b/llava/serve/cli.py
@@ -0,0 +1,145 @@
+import argparse
+import torch
+
+from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import process_images, tokenizer_X_token, get_model_name_from_path, KeywordsStoppingCriteria
+
+from PIL import Image
+
+import requests
+from PIL import Image
+from io import BytesIO
+from transformers import TextStreamer
+
+
+def load_image(image_file):
+    if image_file.startswith('http://') or image_file.startswith('https://'):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    else:
+        image = Image.open(image_file).convert('RGB')
+    return image
+
+
+def main(args):
+    # Model
+    disable_torch_init()
+    assert not (args.image_file and args.video_file)
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name,
+                                                                     args.load_8bit, args.load_4bit, device=args.device)
+    # print(model, tokenizer, processor)
+    image_processor = processor['image']
+    video_processor = processor['video']
+    if 'llama-2' in model_name.lower():
+        conv_mode = "llava_llama_2"
+    elif "v1" in model_name.lower():
+        conv_mode = "llava_v1"
+    elif "mpt" in model_name.lower():
+        conv_mode = "mpt"
+    else:
+        conv_mode = "llava_v0"
+
+    if args.conv_mode is not None and conv_mode != args.conv_mode:
+        print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
+    else:
+        args.conv_mode = conv_mode
+
+    conv = conv_templates[args.conv_mode].copy()
+    if "mpt" in model_name.lower():
+        roles = ('user', 'assistant')
+    else:
+        roles = conv.roles
+    image = args.image_file
+    video = args.video_file
+    # print(image, video)
+    if args.image_file:
+        image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values']
+        if type(image_tensor) is list:
+            tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor]
+        else:
+            tensor = image_tensor.to(model.device, dtype=torch.float16)
+        key = ['image']
+        # print(tensor.shape)
+    elif args.video_file:
+        video_tensor = video_processor(video, return_tensors='pt')['pixel_values']
+        if type(video_tensor) is list:
+            tensor = [video.to(model.device, dtype=torch.float16) for video in video_tensor]
+        else:
+            tensor = video_tensor.to(model.device, dtype=torch.float16)
+        key = ['video']
+        # print(tensor.shape)
+    while True:
+        try:
+            inp = input(f"{roles[0]}: ")
+        except EOFError:
+            inp = ""
+        if not inp:
+            print("exit...")
+            break
+
+        print(f"{roles[1]}: ", end="")
+
+        if image is not None:
+            # first message
+            inp = DEFAULT_X_TOKEN['IMAGE'] + '\n' + inp
+            conv.append_message(conv.roles[0], inp)
+            image = None
+        elif video is not None:
+            # first message
+            inp = DEFAULT_X_TOKEN['VIDEO'] + '\n' + inp
+            conv.append_message(conv.roles[0], inp)
+            video = None
+        else:
+            # later messages
+            conv.append_message(conv.roles[0], inp)
+        conv.append_message(conv.roles[1], None)
+        prompt = conv.get_prompt()
+        if args.image_file:
+            input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt').unsqueeze(0).cuda()
+        elif args.video_file:
+            input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['VIDEO'], return_tensors='pt').unsqueeze(0).cuda()
+        
+        # print(input_ids.shape)
+        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+        keywords = [stop_str]
+        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+        streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+        with torch.inference_mode():
+            output_ids = model.generate(
+                input_ids,
+                images=[tensor, key],
+                do_sample=True,
+                temperature=args.temperature,
+                max_new_tokens=args.max_new_tokens,
+                streamer=streamer,
+                use_cache=True,
+                stopping_criteria=[stopping_criteria])
+
+        outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
+        conv.messages[-1][-1] = outputs
+
+        if args.debug:
+            print("\n", {"prompt": prompt, "outputs": outputs}, "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--image-file", type=str, default=None)
+    parser.add_argument("--video-file", type=str)
+    parser.add_argument("--device", type=str, default="cuda")
+    parser.add_argument("--conv-mode", type=str, default=None)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--max-new-tokens", type=int, default=512)
+    parser.add_argument("--load-8bit", action="store_true")
+    parser.add_argument("--load-4bit", action="store_true")
+    parser.add_argument("--debug", action="store_true")
+    parser.add_argument("--image-aspect-ratio", type=str, default='pad')
+    args = parser.parse_args()
+    main(args)
diff --git a/llava/serve/controller.py b/llava/serve/controller.py
new file mode 100644
index 0000000..b61fca6
--- /dev/null
+++ b/llava/serve/controller.py
@@ -0,0 +1,298 @@
+"""
+A controller manages distributed workers.
+It sends worker addresses to clients.
+"""
+import argparse
+import asyncio
+import dataclasses
+from enum import Enum, auto
+import json
+import logging
+import time
+from typing import List, Union
+import threading
+
+from fastapi import FastAPI, Request
+from fastapi.responses import StreamingResponse
+import numpy as np
+import requests
+import uvicorn
+
+from llava.constants import CONTROLLER_HEART_BEAT_EXPIRATION
+from llava.utils import build_logger, server_error_msg
+
+
+logger = build_logger("controller", "controller.log")
+
+
+class DispatchMethod(Enum):
+    LOTTERY = auto()
+    SHORTEST_QUEUE = auto()
+
+    @classmethod
+    def from_str(cls, name):
+        if name == "lottery":
+            return cls.LOTTERY
+        elif name == "shortest_queue":
+            return cls.SHORTEST_QUEUE
+        else:
+            raise ValueError(f"Invalid dispatch method")
+
+
+@dataclasses.dataclass
+class WorkerInfo:
+    model_names: List[str]
+    speed: int
+    queue_length: int
+    check_heart_beat: bool
+    last_heart_beat: str
+
+
+def heart_beat_controller(controller):
+    while True:
+        time.sleep(CONTROLLER_HEART_BEAT_EXPIRATION)
+        controller.remove_stable_workers_by_expiration()
+
+
+class Controller:
+    def __init__(self, dispatch_method: str):
+        # Dict[str -> WorkerInfo]
+        self.worker_info = {}
+        self.dispatch_method = DispatchMethod.from_str(dispatch_method)
+
+        self.heart_beat_thread = threading.Thread(
+            target=heart_beat_controller, args=(self,))
+        self.heart_beat_thread.start()
+
+        logger.info("Init controller")
+
+    def register_worker(self, worker_name: str, check_heart_beat: bool,
+                        worker_status: dict):
+        if worker_name not in self.worker_info:
+            logger.info(f"Register a new worker: {worker_name}")
+        else:
+            logger.info(f"Register an existing worker: {worker_name}")
+
+        if not worker_status:
+            worker_status = self.get_worker_status(worker_name)
+        if not worker_status:
+            return False
+
+        self.worker_info[worker_name] = WorkerInfo(
+            worker_status["model_names"], worker_status["speed"], worker_status["queue_length"],
+            check_heart_beat, time.time())
+
+        logger.info(f"Register done: {worker_name}, {worker_status}")
+        return True
+
+    def get_worker_status(self, worker_name: str):
+        try:
+            r = requests.post(worker_name + "/worker_get_status", timeout=5)
+        except requests.exceptions.RequestException as e:
+            logger.error(f"Get status fails: {worker_name}, {e}")
+            return None
+
+        if r.status_code != 200:
+            logger.error(f"Get status fails: {worker_name}, {r}")
+            return None
+
+        return r.json()
+
+    def remove_worker(self, worker_name: str):
+        del self.worker_info[worker_name]
+
+    def refresh_all_workers(self):
+        old_info = dict(self.worker_info)
+        self.worker_info = {}
+
+        for w_name, w_info in old_info.items():
+            if not self.register_worker(w_name, w_info.check_heart_beat, None):
+                logger.info(f"Remove stale worker: {w_name}")
+
+    def list_models(self):
+        model_names = set()
+
+        for w_name, w_info in self.worker_info.items():
+            model_names.update(w_info.model_names)
+
+        return list(model_names)
+
+    def get_worker_address(self, model_name: str):
+        if self.dispatch_method == DispatchMethod.LOTTERY:
+            worker_names = []
+            worker_speeds = []
+            for w_name, w_info in self.worker_info.items():
+                if model_name in w_info.model_names:
+                    worker_names.append(w_name)
+                    worker_speeds.append(w_info.speed)
+            worker_speeds = np.array(worker_speeds, dtype=np.float32)
+            norm = np.sum(worker_speeds)
+            if norm < 1e-4:
+                return ""
+            worker_speeds = worker_speeds / norm
+            if True:  # Directly return address
+                pt = np.random.choice(np.arange(len(worker_names)),
+                    p=worker_speeds)
+                worker_name = worker_names[pt]
+                return worker_name
+
+            # Check status before returning
+            while True:
+                pt = np.random.choice(np.arange(len(worker_names)),
+                    p=worker_speeds)
+                worker_name = worker_names[pt]
+
+                if self.get_worker_status(worker_name):
+                    break
+                else:
+                    self.remove_worker(worker_name)
+                    worker_speeds[pt] = 0
+                    norm = np.sum(worker_speeds)
+                    if norm < 1e-4:
+                        return ""
+                    worker_speeds = worker_speeds / norm
+                    continue
+            return worker_name
+        elif self.dispatch_method == DispatchMethod.SHORTEST_QUEUE:
+            worker_names = []
+            worker_qlen = []
+            for w_name, w_info in self.worker_info.items():
+                if model_name in w_info.model_names:
+                    worker_names.append(w_name)
+                    worker_qlen.append(w_info.queue_length / w_info.speed)
+            if len(worker_names) == 0:
+                return ""
+            min_index = np.argmin(worker_qlen)
+            w_name = worker_names[min_index]
+            self.worker_info[w_name].queue_length += 1
+            logger.info(f"names: {worker_names}, queue_lens: {worker_qlen}, ret: {w_name}")
+            return w_name
+        else:
+            raise ValueError(f"Invalid dispatch method: {self.dispatch_method}")
+
+    def receive_heart_beat(self, worker_name: str, queue_length: int):
+        if worker_name not in self.worker_info:
+            logger.info(f"Receive unknown heart beat. {worker_name}")
+            return False
+
+        self.worker_info[worker_name].queue_length = queue_length
+        self.worker_info[worker_name].last_heart_beat = time.time()
+        logger.info(f"Receive heart beat. {worker_name}")
+        return True
+
+    def remove_stable_workers_by_expiration(self):
+        expire = time.time() - CONTROLLER_HEART_BEAT_EXPIRATION
+        to_delete = []
+        for worker_name, w_info in self.worker_info.items():
+            if w_info.check_heart_beat and w_info.last_heart_beat < expire:
+                to_delete.append(worker_name)
+
+        for worker_name in to_delete:
+            self.remove_worker(worker_name)
+
+    def worker_api_generate_stream(self, params):
+        worker_addr = self.get_worker_address(params["model"])
+        if not worker_addr:
+            logger.info(f"no worker: {params['model']}")
+            ret = {
+                "text": server_error_msg,
+                "error_code": 2,
+            }
+            yield json.dumps(ret).encode() + b"\0"
+
+        try:
+            response = requests.post(worker_addr + "/worker_generate_stream",
+                json=params, stream=True, timeout=5)
+            for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
+                if chunk:
+                    yield chunk + b"\0"
+        except requests.exceptions.RequestException as e:
+            logger.info(f"worker timeout: {worker_addr}")
+            ret = {
+                "text": server_error_msg,
+                "error_code": 3,
+            }
+            yield json.dumps(ret).encode() + b"\0"
+
+
+    # Let the controller act as a worker to achieve hierarchical
+    # management. This can be used to connect isolated sub networks.
+    def worker_api_get_status(self):
+        model_names = set()
+        speed = 0
+        queue_length = 0
+
+        for w_name in self.worker_info:
+            worker_status = self.get_worker_status(w_name)
+            if worker_status is not None:
+                model_names.update(worker_status["model_names"])
+                speed += worker_status["speed"]
+                queue_length += worker_status["queue_length"]
+
+        return {
+            "model_names": list(model_names),
+            "speed": speed,
+            "queue_length": queue_length,
+        }
+
+
+app = FastAPI()
+
+
+@app.post("/register_worker")
+async def register_worker(request: Request):
+    data = await request.json()
+    controller.register_worker(
+        data["worker_name"], data["check_heart_beat"],
+        data.get("worker_status", None))
+
+
+@app.post("/refresh_all_workers")
+async def refresh_all_workers():
+    models = controller.refresh_all_workers()
+
+
+@app.post("/list_models")
+async def list_models():
+    models = controller.list_models()
+    return {"models": models}
+
+
+@app.post("/get_worker_address")
+async def get_worker_address(request: Request):
+    data = await request.json()
+    addr = controller.get_worker_address(data["model"])
+    return {"address": addr}
+
+
+@app.post("/receive_heart_beat")
+async def receive_heart_beat(request: Request):
+    data = await request.json()
+    exist = controller.receive_heart_beat(
+        data["worker_name"], data["queue_length"])
+    return {"exist": exist}
+
+
+@app.post("/worker_generate_stream")
+async def worker_api_generate_stream(request: Request):
+    params = await request.json()
+    generator = controller.worker_api_generate_stream(params)
+    return StreamingResponse(generator)
+
+
+@app.post("/worker_get_status")
+async def worker_api_get_status(request: Request):
+    return controller.worker_api_get_status()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--host", type=str, default="localhost")
+    parser.add_argument("--port", type=int, default=21001)
+    parser.add_argument("--dispatch-method", type=str, choices=[
+        "lottery", "shortest_queue"], default="shortest_queue")
+    args = parser.parse_args()
+    logger.info(f"args: {args}")
+
+    controller = Controller(args.dispatch_method)
+    uvicorn.run(app, host=args.host, port=args.port, log_level="info")
diff --git a/llava/serve/eval_custom.py b/llava/serve/eval_custom.py
new file mode 100644
index 0000000..6a6d43d
--- /dev/null
+++ b/llava/serve/eval_custom.py
@@ -0,0 +1,159 @@
+import argparse
+import torch
+
+from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import process_images, tokenizer_X_token, get_model_name_from_path, KeywordsStoppingCriteria
+import requests
+from PIL import Image
+from io import BytesIO
+from transformers import TextStreamer
+
+import os
+import json
+from tqdm import tqdm
+
+
+def load_image(image_file):
+    if image_file.startswith('http://') or image_file.startswith('https://'):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    else:
+        image = Image.open(image_file).convert('RGB')
+    return image
+
+
+def main(args):
+    # Questions:
+    # q1 = "What is the action of ego car?"
+    # q2 = "Why does the ego car doing this?"
+    # json_file = "./video_process/conv_new_icl/conversation_bddx_eval.json"
+    json_file = args.input
+    # out_json_paths = [f"./video_process/conv_new_base_vicuna_iclzs/BDDX_Test_pred_{cap}.json" for cap in ['action','justification']]
+    os.makedirs(args.output, exist_ok=True)
+    out_json_paths = [f"{args.output}/BDDX_Test_pred_{cap}.json" for cap in ['action','justification']]
+    
+    # Model
+    disable_torch_init()
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name,
+                                                                     args.load_8bit, args.load_4bit, device=args.device)
+    # print(model, tokenizer, processor)
+    # image_processor = processor['image']
+    video_processor = processor['video']
+
+
+    conv_mode = "driving"
+
+    if args.conv_mode is not None and conv_mode != args.conv_mode:
+        print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
+    else:
+        args.conv_mode = conv_mode
+
+    conv = conv_templates[args.conv_mode].copy()
+
+
+    # gt
+    with open(json_file, 'r') as file:
+        data = json.load(file)
+
+    # Pred
+    out_jsons = [[],[]]
+    
+    for item in tqdm(data):
+        q1, q2 = item["conversations"][0]["value"], item["conversations"][2]["value"]
+        conv.messages.clear()
+        if "mpt" in model_name.lower():
+            roles = ('user', 'assistant')
+        else:
+            roles = conv.roles
+            
+        video, vid = item["video"], item['id']
+        video = os.path.join("./video_process",video)
+
+        video_tensor = video_processor(video, return_tensors='pt')['pixel_values']
+        if type(video_tensor) is list:
+            tensor = [video.to(model.device, dtype=torch.float16) for video in video_tensor]
+        else:
+            tensor = video_tensor.to(model.device, dtype=torch.float16)
+        key = ['video']
+
+
+        inst_answers = []
+        for qid, question in enumerate([q1,q2]):
+            # print(question)
+            inp = question
+            
+            if video is not None:
+                # First Message
+                # inp = DEFAULT_X_TOKEN['VIDEO'] + '\n' + inp
+                inp = inp
+                conv.append_message(conv.roles[0], inp)
+                video = None
+            else:
+                # later messages
+                conv.append_message(conv.roles[0], inp)
+
+            conv.append_message(conv.roles[1], None)
+            prompt = conv.get_prompt()
+            input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['VIDEO'], return_tensors='pt').unsqueeze(0).cuda()
+            
+            # print(input_ids.shape)
+            # print(prompt)
+
+            stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+            keywords = [stop_str]
+            stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+            streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+            
+            with torch.inference_mode():
+                output_ids = model.generate(
+                    input_ids,
+                    images=[tensor, key],
+                    do_sample=True,
+                    temperature=args.temperature,
+                    max_new_tokens=args.max_new_tokens,
+                    streamer=streamer,
+                    use_cache=True,
+                    stopping_criteria=[stopping_criteria])
+
+            outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
+            conv.messages[-1][-1] = outputs
+
+            if args.debug:
+                print("\n", {"prompt": prompt, "outputs": outputs}, "\n")
+                
+            
+            inst_pred = {
+                "image_id":vid,
+                "caption":outputs.replace("</s>","")
+            }
+
+            out_jsons[qid].append(inst_pred)
+        # break
+    
+    # Save separate json for action and justification
+    for i in range(2):
+        with open(out_json_paths[i],"w") as of:
+            json.dump(out_jsons[i], of, indent=4)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--device", type=str, default="cuda")
+    parser.add_argument("--conv-mode", type=str, default=None)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--max-new-tokens", type=int, default=512)
+    parser.add_argument("--load-8bit", action="store_true")
+    parser.add_argument("--load-4bit", action="store_true")
+    parser.add_argument("--debug", action="store_true")
+    parser.add_argument("--image-aspect-ratio", type=str, default='pad')
+    parser.add_argument("--input", type=str, default=None)
+    parser.add_argument("--output", type=str, default=None)
+    args = parser.parse_args()
+    main(args)
diff --git a/llava/serve/eval_custom_chunck.py b/llava/serve/eval_custom_chunck.py
new file mode 100644
index 0000000..b2d5329
--- /dev/null
+++ b/llava/serve/eval_custom_chunck.py
@@ -0,0 +1,184 @@
+import argparse
+import torch
+
+from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import process_images, tokenizer_X_token, get_model_name_from_path, KeywordsStoppingCriteria
+import requests
+from PIL import Image
+from io import BytesIO
+from transformers import TextStreamer
+
+import os
+import json
+from tqdm import tqdm
+
+# video_root = "/zhaobai46g/Project/Video-LLaVA/video_process/"
+# retr_strategy = 'visual'
+# train_match_file, test_match_file = [f"./retrieval/BDDX_RAG_{retr_strategy}_vpmatch_{split}.json" for split in ['train','test']]
+# with open(train_match_file, "r") as fm:
+#      train_match = json.load(fm)
+# with open(test_match_file, "r") as ft:
+#      test_match = json.load(ft)
+# PATH_RETRIEVAL_MATCH = {**train_match, **test_match}
+
+def load_image(image_file):
+    if image_file.startswith('http://') or image_file.startswith('https://'):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    else:
+        image = Image.open(image_file).convert('RGB')
+    return image
+
+
+def split_list_into_subsets(real_dst_train, cur_worker, num_chunks=4):
+    # num_chunks = 8
+    chunk_size = len(real_dst_train) // num_chunks
+    chunk_index = cur_worker
+    if chunk_index == num_chunks - 1:
+        subset_indices = range(chunk_index * chunk_size, len(real_dst_train))
+    else:
+        subset_indices = range(chunk_index * chunk_size, (chunk_index + 1) * chunk_size)
+    subset = [real_dst_train[i] for i in subset_indices]
+    return subset
+
+
+def main(args):
+    # Questions:
+
+    json_file = args.input
+    os.makedirs(args.output, exist_ok=True)
+    out_json_paths = [f"{args.output}/BDDX_Test_pred_{cap}_{args.cur_worker}.json" for cap in ['action','justification','control_signal']]
+    
+    # Model
+    disable_torch_init()
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name,
+                                                                     args.load_8bit, args.load_4bit, device=args.device)
+    # print(model, tokenizer, processor)
+    # image_processor = processor['image']
+    video_processor = processor['video']
+
+
+    conv_mode = "driving"
+
+    if args.conv_mode is not None and conv_mode != args.conv_mode:
+        print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
+    else:
+        args.conv_mode = conv_mode
+
+    conv = conv_templates[args.conv_mode].copy()
+
+
+    # gt
+    with open(json_file, 'r') as file:
+        data = json.load(file)
+        
+    # Split data
+    sub_data = split_list_into_subsets(data, cur_worker=args.cur_worker, num_chunks=args.total_worker)
+    data = sub_data
+
+    # Pred
+    out_jsons = [[],[],[]]
+    
+    for item in tqdm(data):
+        q1, q2, q3 = item["conversations"][0]["value"], item["conversations"][2]["value"], item["conversations"][4]["value"]
+        conv.messages.clear()
+        if "mpt" in model_name.lower():
+            roles = ('user', 'assistant')
+        else:
+            roles = conv.roles
+            
+        vps, vid = item["video"], item['id']
+        
+        video_paths = [os.path.join("./video_process",vp) for vp in vps]
+
+        
+        video_tensor = [video_processor(video_path, return_tensors='pt')['pixel_values'] for video_path in video_paths]
+        if type(video_tensor) is list:
+            tensor = [[video.to(model.device, dtype=torch.float16) for video in video_tensor]]
+        else:
+            tensor = video_tensor.to(model.device, dtype=torch.float16)
+        key = ['video']
+
+        
+        inst_answers = []
+        for qid, question in enumerate([q1,q2,q3]):
+            # print(question)
+            inp = question
+            
+            if vps is not None:
+                # First Message
+                # inp = DEFAULT_X_TOKEN['VIDEO'] + '\n' + inp
+                inp = inp
+                conv.append_message(conv.roles[0], inp)
+                video = None
+            else:
+                # later messages
+                conv.append_message(conv.roles[0], inp)
+
+            conv.append_message(conv.roles[1], None)
+            prompt = conv.get_prompt()
+            input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['VIDEO'], return_tensors='pt').unsqueeze(0).cuda()
+
+            stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+            keywords = [stop_str]
+            stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+            streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+            
+            print(len(tensor), key)
+            # import pdb;pdb.set_trace()
+            with torch.inference_mode():
+                output_ids = model.generate(
+                    input_ids,
+                    images=[tensor, key],
+                    do_sample=True,
+                    temperature=args.temperature,
+                    max_new_tokens=args.max_new_tokens,
+                    streamer=streamer,
+                    use_cache=True,
+                    stopping_criteria=[stopping_criteria])
+
+            outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
+            conv.messages[-1][-1] = outputs
+
+            if args.debug:
+                print("\n", {"prompt": prompt, "outputs": outputs}, "\n")
+                
+            
+            inst_pred = {
+                "image_id":vid,
+                "caption":outputs.replace("</s>","")
+            }
+
+            out_jsons[qid].append(inst_pred)
+        # import pdb; pdb.set_trace()
+        # break
+    
+    # Save separate json for action and justification
+    for i in range(3):
+        with open(out_json_paths[i],"w") as of:
+            json.dump(out_jsons[i], of, indent=4)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--device", type=str, default="cuda")
+    parser.add_argument("--conv-mode", type=str, default=None)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--max-new-tokens", type=int, default=512)
+    parser.add_argument("--load-8bit", action="store_true")
+    parser.add_argument("--load-4bit", action="store_true")
+    parser.add_argument("--debug", action="store_true")
+    parser.add_argument("--image-aspect-ratio", type=str, default='pad')
+    parser.add_argument("--input", type=str, default=None)
+    parser.add_argument("--output", type=str, default=None)
+    parser.add_argument("--total_worker", type=int, default=4)
+    parser.add_argument("--cur_worker", type=int, default=0)
+    args = parser.parse_args()
+    main(args)
diff --git a/llava/serve/eval_custom_predsig.py b/llava/serve/eval_custom_predsig.py
new file mode 100644
index 0000000..cf4feb9
--- /dev/null
+++ b/llava/serve/eval_custom_predsig.py
@@ -0,0 +1,166 @@
+import argparse
+import torch
+
+from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import process_images, tokenizer_X_token, get_model_name_from_path, KeywordsStoppingCriteria
+import requests
+from PIL import Image
+from io import BytesIO
+from transformers import TextStreamer
+
+import os
+import json
+from tqdm import tqdm
+
+# video_root = "/zhaobai46g/Project/Video-LLaVA/video_process/"
+# retr_strategy = 'visual'
+# train_match_file, test_match_file = [f"./retrieval/BDDX_RAG_{retr_strategy}_vpmatch_{split}.json" for split in ['train','test']]
+# with open(train_match_file, "r") as fm:
+#      train_match = json.load(fm)
+# with open(test_match_file, "r") as ft:
+#      test_match = json.load(ft)
+# PATH_RETRIEVAL_MATCH = {**train_match, **test_match}
+
+def load_image(image_file):
+    if image_file.startswith('http://') or image_file.startswith('https://'):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    else:
+        image = Image.open(image_file).convert('RGB')
+    return image
+
+
+def main(args):
+    # Questions:
+
+    json_file = args.input
+    os.makedirs(args.output, exist_ok=True)
+    out_json_paths = [f"{args.output}/BDDX_Test_pred_{cap}.json" for cap in ['action','justification','control_signal']]
+    
+    # Model
+    disable_torch_init()
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name,
+                                                                     args.load_8bit, args.load_4bit, device=args.device)
+    # print(model, tokenizer, processor)
+    # image_processor = processor['image']
+    video_processor = processor['video']
+
+
+    conv_mode = "driving"
+
+    if args.conv_mode is not None and conv_mode != args.conv_mode:
+        print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
+    else:
+        args.conv_mode = conv_mode
+
+    conv = conv_templates[args.conv_mode].copy()
+
+
+    # gt
+    with open(json_file, 'r') as file:
+        data = json.load(file)
+
+    # Pred
+    out_jsons = [[],[],[]]
+    
+    for item in tqdm(data):
+        q1, q2, q3 = item["conversations"][0]["value"], item["conversations"][2]["value"], item["conversations"][4]["value"]
+        conv.messages.clear()
+        if "mpt" in model_name.lower():
+            roles = ('user', 'assistant')
+        else:
+            roles = conv.roles
+            
+        vps, vid = item["video"], item['id']
+        
+        video_paths = [os.path.join("./video_process",vp) for vp in vps]
+
+        
+        video_tensor = [video_processor(video_path, return_tensors='pt')['pixel_values'] for video_path in video_paths]
+        if type(video_tensor) is list:
+            tensor = [[video.to(model.device, dtype=torch.float16) for video in video_tensor]]
+        else:
+            tensor = video_tensor.to(model.device, dtype=torch.float16)
+        key = ['video']
+
+        
+        inst_answers = []
+        for qid, question in enumerate([q1,q2,q3]):
+            # print(question)
+            inp = question
+            
+            if vps is not None:
+                # First Message
+                # inp = DEFAULT_X_TOKEN['VIDEO'] + '\n' + inp
+                inp = inp
+                conv.append_message(conv.roles[0], inp)
+                video = None
+            else:
+                # later messages
+                conv.append_message(conv.roles[0], inp)
+
+            conv.append_message(conv.roles[1], None)
+            prompt = conv.get_prompt()
+            input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['VIDEO'], return_tensors='pt').unsqueeze(0).cuda()
+
+            stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+            keywords = [stop_str]
+            stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+            streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+            
+            print(len(tensor), key)
+            # import pdb;pdb.set_trace()
+            with torch.inference_mode():
+                output_ids = model.generate(
+                    input_ids,
+                    images=[tensor, key],
+                    do_sample=True,
+                    temperature=args.temperature,
+                    max_new_tokens=args.max_new_tokens,
+                    streamer=streamer,
+                    use_cache=True,
+                    stopping_criteria=[stopping_criteria])
+
+            outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
+            conv.messages[-1][-1] = outputs
+
+            if args.debug:
+                print("\n", {"prompt": prompt, "outputs": outputs}, "\n")
+                
+            
+            inst_pred = {
+                "image_id":vid,
+                "caption":outputs.replace("</s>","")
+            }
+
+            out_jsons[qid].append(inst_pred)
+        # import pdb; pdb.set_trace()
+        # break
+    
+    # Save separate json for action and justification
+    for i in range(3):
+        with open(out_json_paths[i],"w") as of:
+            json.dump(out_jsons[i], of, indent=4)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--device", type=str, default="cuda")
+    parser.add_argument("--conv-mode", type=str, default=None)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--max-new-tokens", type=int, default=512)
+    parser.add_argument("--load-8bit", action="store_true")
+    parser.add_argument("--load-4bit", action="store_true")
+    parser.add_argument("--debug", action="store_true")
+    parser.add_argument("--image-aspect-ratio", type=str, default='pad')
+    parser.add_argument("--input", type=str, default=None)
+    parser.add_argument("--output", type=str, default=None)
+    args = parser.parse_args()
+    main(args)
diff --git a/llava/serve/examples/desert.jpg b/llava/serve/examples/desert.jpg
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diff --git a/llava/serve/examples/sample_demo_13.mp4 b/llava/serve/examples/sample_demo_13.mp4
new file mode 100644
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diff --git a/llava/serve/examples/sample_demo_22.mp4 b/llava/serve/examples/sample_demo_22.mp4
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diff --git a/llava/serve/examples/sample_demo_3.mp4 b/llava/serve/examples/sample_demo_3.mp4
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diff --git a/llava/serve/examples/sample_demo_8.mp4 b/llava/serve/examples/sample_demo_8.mp4
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diff --git a/llava/serve/examples/sample_demo_9.mp4 b/llava/serve/examples/sample_demo_9.mp4
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HcmV?d00001

diff --git a/llava/serve/gradio_utils.py b/llava/serve/gradio_utils.py
new file mode 100644
index 0000000..8762421
--- /dev/null
+++ b/llava/serve/gradio_utils.py
@@ -0,0 +1,141 @@
+import torch
+
+from llava.constants import X_TOKEN_INDEX
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.mm_utils import get_model_name_from_path, KeywordsStoppingCriteria, tokenizer_X_token
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+
+title_markdown = ("""
+<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
+  <a href="https://github.com/PKU-YuanGroup/LanguageBind" style="margin-right: 20px; text-decoration: none; display: flex; align-items: center;">
+    <img src="https://z1.ax1x.com/2023/11/07/pil4sqH.png" alt="LanguageBind🚀" style="max-width: 120px; height: auto;">
+  </a>
+  <div>
+    <h1 >Video-LLaVA: Improved LLaVA with United Visual Representation</h1>
+    <h5 style="margin: 0;">If you like our project, please give us a star ✨ on Github for the latest update.</h5>
+  </div>
+</div>
+
+
+<div align="center">
+    <div style="display:flex; gap: 0.25rem;" align="center">
+        <a href='https://github.com/PKU-YuanGroup/LanguageBind'><img src='https://img.shields.io/badge/Github-Code-blue'></a>
+        <a href="https://arxiv.org/pdf/2310.01852.pdf"><img src="https://img.shields.io/badge/Arxiv-2310.01852-red"></a>
+        <a href='https://github.com/PKU-YuanGroup/LanguageBind/stargazers'><img src='https://img.shields.io/github/stars/PKU-YuanGroup/LanguageBind.svg?style=social'></a>
+    </div>
+</div>
+""")
+
+block_css = """
+#buttons button {
+    min-width: min(120px,100%);
+}
+"""
+
+
+tos_markdown = ("""
+### Terms of use
+By using this service, users are required to agree to the following terms:
+The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
+Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator.
+For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
+""")
+
+
+learn_more_markdown = ("""
+### License
+The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
+""")
+
+
+class Chat:
+    def __init__(self, model_path, conv_mode, model_base=None, load_8bit=False, load_4bit=False, device='cuda'):
+        disable_torch_init()
+        model_name = get_model_name_from_path(model_path)
+        self.tokenizer, self.model, processor, context_len = load_pretrained_model(model_path, model_base, model_name,
+                                                                         load_8bit, load_4bit,
+                                                                         device=device)
+        self.image_processor = processor['image']
+        self.video_processor = processor['video']
+        self.conv_mode = conv_mode
+        self.device = self.model.device
+        print(self.model)
+
+    def get_prompt(self, qs, state):
+        state.append_message(state.roles[0], qs)
+        state.append_message(state.roles[1], None)
+        return state
+
+    @torch.inference_mode()
+    def generate(self, images_tensor: list, prompt: str, first_run: bool, state):
+        tokenizer, model, image_processor = self.tokenizer, self.model, self.image_processor
+
+        state = self.get_prompt(prompt, state)
+        prompt = state.get_prompt()
+        print('\n\n\n')
+        print(prompt)
+
+        if 'image' in images_tensor[1] and 'video' not in images_tensor[1]:
+            input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt').unsqueeze(0).to(self.device)
+        elif 'image' not in images_tensor[1] and 'video' in images_tensor[1]:
+            input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['VIDEO'], return_tensors='pt').unsqueeze(0).to(self.device)
+        elif 'image' in images_tensor[1] and 'video' in images_tensor[1]:
+            # <video>\nxxxxxxx\n<image>
+            '''
+            tensor([[1, -200, 29871, 13, 3068, 366, 1074, 1716, 278, 1967, 322, 4863, 29973, 319, 1799, 9047, 13566, 29901]])
+            tensor([[1, -201, 29871, 13]])
+            '''
+            print("split: ", prompt.split('\n<image>'))
+            # print("\n", tokenizer_X_token('\n', tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt'))
+            # print("?", tokenizer_X_token('?', tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt'))
+            # print("image", tokenizer_X_token('image', tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt'))
+            # print("image?", tokenizer_X_token('image?', tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt'))
+            # print("USER: <image>\nWhat is unusual about this image?", tokenizer_X_token('USER: <image>\nWhat is unusual about this image?', tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt'))
+            input_ids1 = tokenizer_X_token(prompt.split('\n<image>')[0], tokenizer, X_TOKEN_INDEX['VIDEO'], return_tensors='pt').unsqueeze(0).to(self.device)
+            print('input_ids1', input_ids1)
+            input_ids2 = tokenizer_X_token(prompt.split('\n<image>')[-1], tokenizer, X_TOKEN_INDEX['VIDEO'], return_tensors='pt').unsqueeze(0).to(self.device)
+            print('input_ids2', input_ids2)
+            input_ids3 = tokenizer_X_token('\n<image>', tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt').unsqueeze(0).to(self.device)
+            print('input_ids3', input_ids3)
+
+            input_ids = torch.cat([input_ids1, input_ids3[:, 1:], input_ids2[:, 1:]], dim=-1)
+            print('input_ids', input_ids)
+            print(*[tokenizer.decode(i) for i in input_ids2[0]])
+        else:
+            input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt').unsqueeze(0).to(self.device)
+        
+        temperature = 0.2
+        
+        max_new_tokens = 1024
+
+        stop_str = conv_templates[self.conv_mode].copy().sep if conv_templates[self.conv_mode].copy().sep_style != SeparatorStyle.TWO else \
+        conv_templates[self.conv_mode].copy().sep2
+        keywords = [stop_str]
+        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+        # streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+        # print(input_ids, images_tensor[0][0].shape)
+        with torch.inference_mode():
+            output_ids = model.generate(
+                input_ids,
+                images=images_tensor,
+                do_sample=True,
+                temperature=temperature,
+                max_new_tokens=max_new_tokens,
+                # streamer=streamer,
+                use_cache=True,
+                stopping_criteria=[stopping_criteria])
+
+        input_token_len = input_ids.shape[1]
+        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+        if n_diff_input_output > 0:
+            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+        outputs = outputs.strip()
+        if outputs.endswith(stop_str):
+            outputs = outputs[:-len(stop_str)]
+        outputs = outputs.strip()
+
+        print('response', outputs)
+        return outputs, state
+
diff --git a/llava/serve/gradio_web_server.py b/llava/serve/gradio_web_server.py
new file mode 100644
index 0000000..b88e214
--- /dev/null
+++ b/llava/serve/gradio_web_server.py
@@ -0,0 +1,253 @@
+import shutil
+import subprocess
+
+import torch
+import gradio as gr
+from fastapi import FastAPI
+import os
+from PIL import Image
+import tempfile
+from decord import VideoReader, cpu
+from transformers import TextStreamer
+
+from llava.constants import DEFAULT_X_TOKEN, X_TOKEN_INDEX
+from llava.conversation import conv_templates, SeparatorStyle, Conversation
+from llava.serve.gradio_utils import Chat, tos_markdown, learn_more_markdown, title_markdown, block_css
+
+
+def save_image_to_local(image):
+    filename = os.path.join('temp', next(tempfile._get_candidate_names()) + '.jpg')
+    image = Image.open(image)
+    image.save(filename)
+    # print(filename)
+    return filename
+
+
+def save_video_to_local(video_path):
+    filename = os.path.join('temp', next(tempfile._get_candidate_names()) + '.mp4')
+    shutil.copyfile(video_path, filename)
+    return filename
+
+
+def generate(image1, video, textbox_in, first_run, state, state_, images_tensor):
+    flag = 1
+    if not textbox_in:
+        if len(state_.messages) > 0:
+            textbox_in = state_.messages[-1][1]
+            state_.messages.pop(-1)
+            flag = 0
+        else:
+            return "Please enter instruction"
+
+    image1 = image1 if image1 else "none"
+    video = video if video else "none"
+    # assert not (os.path.exists(image1) and os.path.exists(video))
+
+    if type(state) is not Conversation:
+        state = conv_templates[conv_mode].copy()
+        state_ = conv_templates[conv_mode].copy()
+        images_tensor = [[], []]
+
+    first_run = False if len(state.messages) > 0 else True
+
+    text_en_in = textbox_in.replace("picture", "image")
+
+    # images_tensor = [[], []]
+    image_processor = handler.image_processor
+    if os.path.exists(image1) and not os.path.exists(video):
+        tensor = image_processor.preprocess(image1, return_tensors='pt')['pixel_values'][0]
+        # print(tensor.shape)
+        tensor = tensor.to(handler.model.device, dtype=dtype)
+        images_tensor[0] = images_tensor[0] + [tensor]
+        images_tensor[1] = images_tensor[1] + ['image']
+    video_processor = handler.video_processor
+    if not os.path.exists(image1) and os.path.exists(video):
+        tensor = video_processor(video, return_tensors='pt')['pixel_values'][0]
+        # print(tensor.shape)
+        tensor = tensor.to(handler.model.device, dtype=dtype)
+        images_tensor[0] = images_tensor[0] + [tensor]
+        images_tensor[1] = images_tensor[1] + ['video']
+    if os.path.exists(image1) and os.path.exists(video):
+        tensor = video_processor(video, return_tensors='pt')['pixel_values'][0]
+        # print(tensor.shape)
+        tensor = tensor.to(handler.model.device, dtype=dtype)
+        images_tensor[0] = images_tensor[0] + [tensor]
+        images_tensor[1] = images_tensor[1] + ['video']
+        
+        
+        tensor = image_processor.preprocess(image1, return_tensors='pt')['pixel_values'][0]
+        # print(tensor.shape)
+        tensor = tensor.to(handler.model.device, dtype=dtype)
+        images_tensor[0] = images_tensor[0] + [tensor]
+        images_tensor[1] = images_tensor[1] + ['image']
+        
+
+
+    if os.path.exists(image1) and not os.path.exists(video):
+        text_en_in = DEFAULT_X_TOKEN['IMAGE'] + '\n' + text_en_in
+    if not os.path.exists(image1) and os.path.exists(video):
+        text_en_in = DEFAULT_X_TOKEN['VIDEO'] + '\n' + text_en_in
+    if os.path.exists(image1) and os.path.exists(video):
+        text_en_in = DEFAULT_X_TOKEN['VIDEO'] + '\n' + text_en_in + '\n' + DEFAULT_X_TOKEN['IMAGE']
+
+    text_en_out, state_ = handler.generate(images_tensor, text_en_in, first_run=first_run, state=state_)
+    state_.messages[-1] = (state_.roles[1], text_en_out)
+
+    text_en_out = text_en_out.split('#')[0]
+    textbox_out = text_en_out
+
+    show_images = ""
+    if os.path.exists(image1):
+        filename = save_image_to_local(image1)
+        show_images += f'<img src="./file={filename}" style="display: inline-block;width: 250px;max-height: 400px;">'
+    if os.path.exists(video):
+        filename = save_video_to_local(video)
+        show_images += f'<video controls playsinline width="500" style="display: inline-block;"  src="./file={filename}"></video>'
+
+    if flag:
+        state.append_message(state.roles[0], textbox_in + "\n" + show_images)
+    state.append_message(state.roles[1], textbox_out)
+
+    return (state, state_, state.to_gradio_chatbot(), False, gr.update(value=None, interactive=True), images_tensor, gr.update(value=image1 if os.path.exists(image1) else None, interactive=True), gr.update(value=video if os.path.exists(video) else None, interactive=True))
+
+def regenerate(state, state_):
+    state.messages.pop(-1)
+    state_.messages.pop(-1)
+    if len(state.messages) > 0:
+        return state, state_, state.to_gradio_chatbot(), False
+    return (state, state_, state.to_gradio_chatbot(), True)
+
+
+def clear_history(state, state_):
+    state = conv_templates[conv_mode].copy()
+    state_ = conv_templates[conv_mode].copy()
+    return (gr.update(value=None, interactive=True),
+        gr.update(value=None, interactive=True),\
+        gr.update(value=None, interactive=True),\
+        True, state, state_, state.to_gradio_chatbot(), [[], []])
+
+
+
+conv_mode = "llava_v1"
+model_path = 'LanguageBind/Video-LLaVA-7B'
+device = 'cuda'
+load_8bit = False
+load_4bit = True
+dtype = torch.float16
+handler = Chat(model_path, conv_mode=conv_mode, load_8bit=load_8bit, load_4bit=load_8bit, device=device)
+# handler.model.to(dtype=dtype)
+if not os.path.exists("temp"):
+    os.makedirs("temp")
+
+app = FastAPI()
+
+textbox = gr.Textbox(
+        show_label=False, placeholder="Enter text and press ENTER", container=False
+    )
+with gr.Blocks(title='Video-LLaVA🚀', theme=gr.themes.Default(), css=block_css) as demo:
+    gr.Markdown(title_markdown)
+    state = gr.State()
+    state_ = gr.State()
+    first_run = gr.State()
+    images_tensor = gr.State()
+
+    with gr.Row():
+        with gr.Column(scale=3):
+            image1 = gr.Image(label="Input Image", type="filepath")
+            video = gr.Video(label="Input Video")
+
+            cur_dir = os.path.dirname(os.path.abspath(__file__))
+            gr.Examples(
+                examples=[
+                    [
+                        f"{cur_dir}/examples/extreme_ironing.jpg",
+                        "What is unusual about this image?",
+                    ],
+                    [
+                        f"{cur_dir}/examples/waterview.jpg",
+                        "What are the things I should be cautious about when I visit here?",
+                    ],
+                    [
+                        f"{cur_dir}/examples/desert.jpg",
+                        "If there are factual errors in the questions, point it out; if not, proceed answering the question. What’s happening in the desert?",
+                    ],
+                ],
+                inputs=[image1, textbox],
+            )
+
+        with gr.Column(scale=7):
+            chatbot = gr.Chatbot(label="Video-LLaVA", bubble_full_width=True).style(height=750)
+            with gr.Row():
+                with gr.Column(scale=8):
+                    textbox.render()
+                with gr.Column(scale=1, min_width=50):
+                    submit_btn = gr.Button(
+                        value="Send", variant="primary", interactive=True
+                    )
+            with gr.Row(elem_id="buttons") as button_row:
+                upvote_btn = gr.Button(value="👍  Upvote", interactive=True)
+                downvote_btn = gr.Button(value="👎  Downvote", interactive=True)
+                flag_btn = gr.Button(value="⚠️  Flag", interactive=True)
+                # stop_btn = gr.Button(value="⏹️  Stop Generation", interactive=False)
+                regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=True)
+                clear_btn = gr.Button(value="🗑️  Clear history", interactive=True)
+
+    with gr.Row():
+        gr.Examples(
+            examples=[
+                [
+                    f"{cur_dir}/examples/sample_img_22.png",
+                    f"{cur_dir}/examples/sample_demo_22.mp4",
+                    "Are the instruments in the pictures used in the video?",
+                ],
+                [
+                    f"{cur_dir}/examples/sample_img_13.png",
+                    f"{cur_dir}/examples/sample_demo_13.mp4",
+                    "Does the flag in the image appear in the video?",
+                ],
+                [
+                    f"{cur_dir}/examples/sample_img_8.png",
+                    f"{cur_dir}/examples/sample_demo_8.mp4",
+                    "Are the image and the video depicting the same place?",
+                ],
+            ],
+            inputs=[image1, video, textbox],
+        )
+        gr.Examples(
+            examples=[
+                [
+                    f"{cur_dir}/examples/sample_demo_1.mp4",
+                    "Why is this video funny?",
+                ],
+                [
+                    f"{cur_dir}/examples/sample_demo_3.mp4",
+                    "Can you identify any safety hazards in this video?"
+                ],
+                [
+                    f"{cur_dir}/examples/sample_demo_9.mp4",
+                    "Describe the video.",
+                ],
+                [
+                    f"{cur_dir}/examples/sample_demo_22.mp4",
+                    "Describe the activity in the video.",
+                ],
+            ],
+            inputs=[video, textbox],
+        )
+    gr.Markdown(tos_markdown)
+    gr.Markdown(learn_more_markdown)
+
+    submit_btn.click(generate, [image1, video, textbox, first_run, state, state_, images_tensor],
+                     [state, state_, chatbot, first_run, textbox, images_tensor, image1, video])
+
+    regenerate_btn.click(regenerate, [state, state_], [state, state_, chatbot, first_run]).then(
+        generate, [image1, video, textbox, first_run, state, state_, images_tensor], [state, state_, chatbot, first_run, textbox, images_tensor, image1, video])
+
+    clear_btn.click(clear_history, [state, state_],
+                    [image1, video, textbox, first_run, state, state_, chatbot, images_tensor])
+
+# app = gr.mount_gradio_app(app, demo, path="/")
+demo.launch()
+
+
+# uvicorn llava.serve.gradio_web_server:app
diff --git a/llava/serve/model_worker.py b/llava/serve/model_worker.py
new file mode 100644
index 0000000..a7bcd08
--- /dev/null
+++ b/llava/serve/model_worker.py
@@ -0,0 +1,285 @@
+"""
+A model worker executes the model.
+"""
+import argparse
+import asyncio
+import json
+import time
+import threading
+import uuid
+
+from fastapi import FastAPI, Request, BackgroundTasks
+from fastapi.responses import StreamingResponse
+import requests
+import torch
+import uvicorn
+from functools import partial
+
+from llava.constants import WORKER_HEART_BEAT_INTERVAL
+from llava.utils import (build_logger, server_error_msg,
+    pretty_print_semaphore)
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import process_images, load_image_from_base64, tokenizer_image_token, KeywordsStoppingCriteria
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+from transformers import TextIteratorStreamer
+from threading import Thread
+
+
+GB = 1 << 30
+
+worker_id = str(uuid.uuid4())[:6]
+logger = build_logger("model_worker", f"model_worker_{worker_id}.log")
+global_counter = 0
+
+model_semaphore = None
+
+
+def heart_beat_worker(controller):
+
+    while True:
+        time.sleep(WORKER_HEART_BEAT_INTERVAL)
+        controller.send_heart_beat()
+
+
+class ModelWorker:
+    def __init__(self, controller_addr, worker_addr,
+                 worker_id, no_register,
+                 model_path, model_base, model_name,
+                 load_8bit, load_4bit, device):
+        self.controller_addr = controller_addr
+        self.worker_addr = worker_addr
+        self.worker_id = worker_id
+        if model_path.endswith("/"):
+            model_path = model_path[:-1]
+        if model_name is None:
+            model_paths = model_path.split("/")
+            if model_paths[-1].startswith('checkpoint-'):
+                self.model_name = model_paths[-2] + "_" + model_paths[-1]
+            else:
+                self.model_name = model_paths[-1]
+        else:
+            self.model_name = model_name
+
+        self.device = device
+        logger.info(f"Loading the model {self.model_name} on worker {worker_id} ...")
+        self.tokenizer, self.model, self.image_processor, self.context_len = load_pretrained_model(
+            model_path, model_base, self.model_name, load_8bit, load_4bit, device=self.device)
+        self.is_multimodal = 'llava' in self.model_name.lower()
+
+        if not no_register:
+            self.register_to_controller()
+            self.heart_beat_thread = threading.Thread(
+                target=heart_beat_worker, args=(self,))
+            self.heart_beat_thread.start()
+
+    def register_to_controller(self):
+        logger.info("Register to controller")
+
+        url = self.controller_addr + "/register_worker"
+        data = {
+            "worker_name": self.worker_addr,
+            "check_heart_beat": True,
+            "worker_status": self.get_status()
+        }
+        r = requests.post(url, json=data)
+        assert r.status_code == 200
+
+    def send_heart_beat(self):
+        logger.info(f"Send heart beat. Models: {[self.model_name]}. "
+                    f"Semaphore: {pretty_print_semaphore(model_semaphore)}. "
+                    f"global_counter: {global_counter}")
+
+        url = self.controller_addr + "/receive_heart_beat"
+
+        while True:
+            try:
+                ret = requests.post(url, json={
+                    "worker_name": self.worker_addr,
+                    "queue_length": self.get_queue_length()}, timeout=5)
+                exist = ret.json()["exist"]
+                break
+            except requests.exceptions.RequestException as e:
+                logger.error(f"heart beat error: {e}")
+            time.sleep(5)
+
+        if not exist:
+            self.register_to_controller()
+
+    def get_queue_length(self):
+        if model_semaphore is None:
+            return 0
+        else:
+            return args.limit_model_concurrency - model_semaphore._value + (len(
+                model_semaphore._waiters) if model_semaphore._waiters is not None else 0)
+
+    def get_status(self):
+        return {
+            "model_names": [self.model_name],
+            "speed": 1,
+            "queue_length": self.get_queue_length(),
+        }
+
+    @torch.inference_mode()
+    def generate_stream(self, params):
+        tokenizer, model, image_processor = self.tokenizer, self.model, self.image_processor
+
+        prompt = params["prompt"]
+        ori_prompt = prompt
+        images = params.get("images", None)
+        num_image_tokens = 0
+        if images is not None and len(images) > 0 and self.is_multimodal:
+            if len(images) > 0:
+                if len(images) != prompt.count(DEFAULT_IMAGE_TOKEN):
+                    raise ValueError("Number of images does not match number of <image> tokens in prompt")
+
+                images = [load_image_from_base64(image) for image in images]
+                images = process_images(images, image_processor, model.config)
+
+                if type(images) is list:
+                    images = [image.to(self.model.device, dtype=torch.float16) for image in images]
+                else:
+                    images = images.to(self.model.device, dtype=torch.float16)
+
+                replace_token = DEFAULT_IMAGE_TOKEN
+                if getattr(self.model.config, 'mm_use_im_start_end', False):
+                    replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
+                prompt = prompt.replace(DEFAULT_IMAGE_TOKEN, replace_token)
+
+                num_image_tokens = prompt.count(replace_token) * model.get_vision_tower().num_patches
+            else:
+                images = None
+            image_args = {"images": images}
+        else:
+            images = None
+            image_args = {}
+
+        temperature = float(params.get("temperature", 1.0))
+        top_p = float(params.get("top_p", 1.0))
+        max_context_length = getattr(model.config, 'max_position_embeddings', 2048)
+        max_new_tokens = min(int(params.get("max_new_tokens", 256)), 1024)
+        stop_str = params.get("stop", None)
+        do_sample = True if temperature > 0.001 else False
+
+        input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device)
+        keywords = [stop_str]
+        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+        streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15)
+
+        max_new_tokens = min(max_new_tokens, max_context_length - input_ids.shape[-1] - num_image_tokens)
+
+        if max_new_tokens < 1:
+            yield json.dumps({"text": ori_prompt + "Exceeds max token length. Please start a new conversation, thanks.", "error_code": 0}).encode() + b"\0"
+            return
+
+        thread = Thread(target=model.generate, kwargs=dict(
+            inputs=input_ids,
+            do_sample=do_sample,
+            temperature=temperature,
+            top_p=top_p,
+            max_new_tokens=max_new_tokens,
+            streamer=streamer,
+            stopping_criteria=[stopping_criteria],
+            use_cache=True,
+            **image_args
+        ))
+        thread.start()
+
+        generated_text = ori_prompt
+        for new_text in streamer:
+            generated_text += new_text
+            if generated_text.endswith(stop_str):
+                generated_text = generated_text[:-len(stop_str)]
+            yield json.dumps({"text": generated_text, "error_code": 0}).encode() + b"\0"
+
+    def generate_stream_gate(self, params):
+        try:
+            for x in self.generate_stream(params):
+                yield x
+        except ValueError as e:
+            print("Caught ValueError:", e)
+            ret = {
+                "text": server_error_msg,
+                "error_code": 1,
+            }
+            yield json.dumps(ret).encode() + b"\0"
+        except torch.cuda.CudaError as e:
+            print("Caught torch.cuda.CudaError:", e)
+            ret = {
+                "text": server_error_msg,
+                "error_code": 1,
+            }
+            yield json.dumps(ret).encode() + b"\0"
+        except Exception as e:
+            print("Caught Unknown Error", e)
+            ret = {
+                "text": server_error_msg,
+                "error_code": 1,
+            }
+            yield json.dumps(ret).encode() + b"\0"
+
+
+app = FastAPI()
+
+
+def release_model_semaphore(fn=None):
+    model_semaphore.release()
+    if fn is not None:
+        fn()
+
+
+@app.post("/worker_generate_stream")
+async def generate_stream(request: Request):
+    global model_semaphore, global_counter
+    global_counter += 1
+    params = await request.json()
+
+    if model_semaphore is None:
+        model_semaphore = asyncio.Semaphore(args.limit_model_concurrency)
+    await model_semaphore.acquire()
+    worker.send_heart_beat()
+    generator = worker.generate_stream_gate(params)
+    background_tasks = BackgroundTasks()
+    background_tasks.add_task(partial(release_model_semaphore, fn=worker.send_heart_beat))
+    return StreamingResponse(generator, background=background_tasks)
+
+
+@app.post("/worker_get_status")
+async def get_status(request: Request):
+    return worker.get_status()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--host", type=str, default="localhost")
+    parser.add_argument("--port", type=int, default=21002)
+    parser.add_argument("--worker-address", type=str,
+        default="http://localhost:21002")
+    parser.add_argument("--controller-address", type=str,
+        default="http://localhost:21001")
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--model-name", type=str)
+    parser.add_argument("--device", type=str, default="cuda")
+    parser.add_argument("--multi-modal", action="store_true", help="Multimodal mode is automatically detected with model name, please make sure `llava` is included in the model path.")
+    parser.add_argument("--limit-model-concurrency", type=int, default=5)
+    parser.add_argument("--stream-interval", type=int, default=1)
+    parser.add_argument("--no-register", action="store_true")
+    parser.add_argument("--load-8bit", action="store_true")
+    parser.add_argument("--load-4bit", action="store_true")
+    args = parser.parse_args()
+    logger.info(f"args: {args}")
+
+    if args.multi_modal:
+        logger.warning("Multimodal mode is automatically detected with model name, please make sure `llava` is included in the model path.")
+
+    worker = ModelWorker(args.controller_address,
+                         args.worker_address,
+                         worker_id,
+                         args.no_register,
+                         args.model_path,
+                         args.model_base,
+                         args.model_name,
+                         args.load_8bit,
+                         args.load_4bit,
+                         args.device)
+    uvicorn.run(app, host=args.host, port=args.port, log_level="info")
diff --git a/llava/serve/register_worker.py b/llava/serve/register_worker.py
new file mode 100644
index 0000000..2c2c402
--- /dev/null
+++ b/llava/serve/register_worker.py
@@ -0,0 +1,26 @@
+"""
+Manually register workers.
+
+Usage:
+python3 -m fastchat.serve.register_worker --controller http://localhost:21001 --worker-name http://localhost:21002
+"""
+
+import argparse
+
+import requests
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--controller-address", type=str)
+    parser.add_argument("--worker-name", type=str)
+    parser.add_argument("--check-heart-beat", action="store_true")
+    args = parser.parse_args()
+
+    url = args.controller_address + "/register_worker"
+    data = {
+        "worker_name": args.worker_name,
+        "check_heart_beat": args.check_heart_beat,
+        "worker_status": None,
+    }
+    r = requests.post(url, json=data)
+    assert r.status_code == 200
diff --git a/llava/serve/test_message.py b/llava/serve/test_message.py
new file mode 100644
index 0000000..6b090fa
--- /dev/null
+++ b/llava/serve/test_message.py
@@ -0,0 +1,62 @@
+import argparse
+import json
+
+import requests
+
+from llava.conversation import default_conversation
+
+
+def main():
+    if args.worker_address:
+        worker_addr = args.worker_address
+    else:
+        controller_addr = args.controller_address
+        ret = requests.post(controller_addr + "/refresh_all_workers")
+        ret = requests.post(controller_addr + "/list_models")
+        models = ret.json()["models"]
+        models.sort()
+        print(f"Models: {models}")
+
+        ret = requests.post(controller_addr + "/get_worker_address",
+            json={"model": args.model_name})
+        worker_addr = ret.json()["address"]
+        print(f"worker_addr: {worker_addr}")
+
+    if worker_addr == "":
+        return
+
+    conv = default_conversation.copy()
+    conv.append_message(conv.roles[0], args.message)
+    prompt = conv.get_prompt()
+
+    headers = {"User-Agent": "LLaVA Client"}
+    pload = {
+        "model": args.model_name,
+        "prompt": prompt,
+        "max_new_tokens": args.max_new_tokens,
+        "temperature": 0.7,
+        "stop": conv.sep,
+    }
+    response = requests.post(worker_addr + "/worker_generate_stream", headers=headers,
+            json=pload, stream=True)
+
+    print(prompt.replace(conv.sep, "\n"), end="")
+    for chunk in response.iter_lines(chunk_size=8192, decode_unicode=False, delimiter=b"\0"):
+        if chunk:
+            data = json.loads(chunk.decode("utf-8"))
+            output = data["text"].split(conv.sep)[-1]
+            print(output, end="\r")
+    print("")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--controller-address", type=str, default="http://localhost:21001")
+    parser.add_argument("--worker-address", type=str)
+    parser.add_argument("--model-name", type=str, default="facebook/opt-350m")
+    parser.add_argument("--max-new-tokens", type=int, default=32)
+    parser.add_argument("--message", type=str, default=
+        "Tell me a story with more than 1000 words.")
+    args = parser.parse_args()
+
+    main()
diff --git a/llava/serve/video_caption.py b/llava/serve/video_caption.py
new file mode 100644
index 0000000..343f197
--- /dev/null
+++ b/llava/serve/video_caption.py
@@ -0,0 +1,148 @@
+import argparse
+import torch
+
+from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import process_images, tokenizer_X_token, get_model_name_from_path, KeywordsStoppingCriteria
+import requests
+from PIL import Image
+from io import BytesIO
+from transformers import TextStreamer
+
+import os
+import json
+from tqdm import tqdm
+
+
+def load_image(image_file):
+    if image_file.startswith('http://') or image_file.startswith('https://'):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    else:
+        image = Image.open(image_file).convert('RGB')
+    return image
+
+
+def main(args):
+    # Questions:
+    # q1 = "What is the action of ego car?"
+    # q2 = "Why does the ego car doing this?"
+    json_file = "./video_process/conv_icl3/conversation_bddx_icl_eval.json"
+    out_json_paths = [f"./video_process/BDDX_train_caption.json"]
+    
+    # Model
+    disable_torch_init()
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name,
+                                                                     args.load_8bit, args.load_4bit, device=args.device)
+
+    video_processor = processor['video']
+
+
+    conv_mode = "v1"
+
+    if args.conv_mode is not None and conv_mode != args.conv_mode:
+        print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
+    else:
+        args.conv_mode = conv_mode
+
+    conv = conv_templates[args.conv_mode].copy()
+
+
+    # gt
+    with open(json_file, 'r') as file:
+        data = json.load(file)
+
+    # Pred
+    out_jsons = [[],[]]
+    
+    for item in tqdm(data):
+        
+        conv.messages.clear()
+        if "mpt" in model_name.lower():
+            roles = ('user', 'assistant')
+        else:
+            roles = conv.roles
+            
+        video, vid = item["video"], item['id']
+        video = os.path.join("./video_process",video)
+
+        video_tensor = video_processor(video, return_tensors='pt')['pixel_values']
+        if type(video_tensor) is list:
+            tensor = [video.to(model.device, dtype=torch.float16) for video in video_tensor]
+        else:
+            tensor = video_tensor.to(model.device, dtype=torch.float16)
+        key = ['video']
+
+
+        
+        inp = "Please Describe the video"
+        
+        if video is not None:
+            # First Message
+            inp = DEFAULT_X_TOKEN['VIDEO'] + '\n' + inp
+            conv.append_message(conv.roles[0], inp)
+            video = None
+        else:
+            # later messages
+            conv.append_message(conv.roles[0], inp)
+
+        conv.append_message(conv.roles[1], None)
+        prompt = conv.get_prompt()
+        input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['VIDEO'], return_tensors='pt').unsqueeze(0).cuda()
+        
+        print(input_ids.shape)
+        
+        # print(input_ids.shape)
+        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+        keywords = [stop_str]
+        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+        streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+        with torch.inference_mode():
+            output_ids = model.generate(
+                input_ids,
+                images=[tensor, key],
+                do_sample=True,
+                temperature=args.temperature,
+                max_new_tokens=args.max_new_tokens,
+                streamer=streamer,
+                use_cache=True,
+                stopping_criteria=[stopping_criteria])
+
+        outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
+        conv.messages[-1][-1] = outputs
+
+        if args.debug:
+            print("\n", {"prompt": prompt, "outputs": outputs}, "\n")
+            
+        
+        inst_pred = {
+            "image_id":vid,
+            "caption":outputs.replace("</s>","")
+        }
+
+        out_jsons[qid].append(inst_pred)
+        # break
+    
+    # Save separate json for action and justification
+    with open(out_json_paths[i],"w") as of:
+        json.dump(out_jsons[i], of, indent=4)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--device", type=str, default="cuda")
+    parser.add_argument("--conv-mode", type=str, default=None)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--max-new-tokens", type=int, default=512)
+    parser.add_argument("--load-8bit", action="store_true")
+    parser.add_argument("--load-4bit", action="store_true")
+    parser.add_argument("--debug", action="store_true")
+    parser.add_argument("--image-aspect-ratio", type=str, default='pad')
+    args = parser.parse_args()
+    main(args)
diff --git a/llava/train/llama_flash_attn_monkey_patch.py b/llava/train/llama_flash_attn_monkey_patch.py
new file mode 100644
index 0000000..31db2ef
--- /dev/null
+++ b/llava/train/llama_flash_attn_monkey_patch.py
@@ -0,0 +1,115 @@
+from typing import Optional, Tuple
+import warnings
+
+import torch
+
+import transformers
+from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
+
+try:
+    from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
+except ImportError:
+    from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func
+from flash_attn.bert_padding import unpad_input, pad_input
+
+
+def forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.Tensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    if output_attentions:
+        warnings.warn(
+            "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
+        )
+
+    bsz, q_len, _ = hidden_states.size()
+
+    query_states = (
+        self.q_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+    key_states = (
+        self.k_proj(hidden_states)
+        .view(bsz, q_len, self.num_key_value_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+    value_states = (
+        self.v_proj(hidden_states)
+        .view(bsz, q_len, self.num_key_value_heads, self.head_dim)
+        .transpose(1, 2)
+    )  # shape: (b, num_heads, s, head_dim)
+
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[-2]
+
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    query_states, key_states = apply_rotary_pos_emb(
+        query_states, key_states, cos, sin, position_ids
+    )
+
+    if past_key_value is not None:
+        # reuse k, v
+        key_states = torch.cat([past_key_value[0], key_states], dim=2)
+        value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+
+    # repeat k/v heads if n_kv_heads < n_heads
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    # Transform the data into the format required by flash attention
+    qkv = torch.stack([query_states, key_states, value_states], dim=2)
+    qkv = qkv.transpose(1, 3)  # shape: [b, s, 3, num_heads, head_dim]
+    key_padding_mask = attention_mask
+
+    if key_padding_mask is None:
+        qkv = qkv.reshape(-1, 3, self.num_heads, self.head_dim)
+        cu_q_lens = torch.arange(
+            0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device
+        )
+        max_s = q_len
+        output = flash_attn_unpadded_qkvpacked_func(
+            qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
+        )
+        output = output.view(bsz, q_len, -1)
+    else:
+        qkv = qkv.reshape(bsz, q_len, -1)
+        qkv, indices, cu_q_lens, max_s = unpad_input(qkv, key_padding_mask)
+        qkv = qkv.view(-1, 3, self.num_heads, self.head_dim)
+        output_unpad = flash_attn_unpadded_qkvpacked_func(
+            qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
+        )
+        output_unpad = output_unpad.reshape(-1, self.num_heads * self.head_dim)
+        output = pad_input(output_unpad, indices, bsz, q_len)
+
+    return self.o_proj(output), None, past_key_value
+
+
+# Disable the transformation of the attention mask in LlamaModel as the flash attention
+# requires the attention mask to be the same as the key_padding_mask
+def _prepare_decoder_attention_mask(
+    self, attention_mask, input_shape, inputs_embeds, past_key_values_length
+):
+    # [bsz, seq_len]
+    return attention_mask
+
+
+def replace_llama_attn_with_flash_attn():
+    cuda_major, cuda_minor = torch.cuda.get_device_capability()
+    if cuda_major < 8:
+        warnings.warn(
+            "Flash attention is only supported on A100 or H100 GPU during training due to head dim > 64 backward."
+            "ref: https://github.com/HazyResearch/flash-attention/issues/190#issuecomment-1523359593"
+        )
+    transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (
+        _prepare_decoder_attention_mask
+    )
+    transformers.models.llama.modeling_llama.LlamaAttention.forward = forward
diff --git a/llava/train/llama_xformers_attn_monkey_patch.py b/llava/train/llama_xformers_attn_monkey_patch.py
new file mode 100644
index 0000000..a5c65da
--- /dev/null
+++ b/llava/train/llama_xformers_attn_monkey_patch.py
@@ -0,0 +1,129 @@
+"""
+Directly copied the code from https://raw.githubusercontent.com/oobabooga/text-generation-webui/main/modules/llama_attn_hijack.py and made some adjustments
+"""
+
+import logging
+import math
+from typing import Optional, Tuple
+
+import torch
+import transformers.models.llama.modeling_llama
+from torch import nn
+
+try:
+    import xformers.ops
+except ImportError:
+    logging.error("xformers not found! Please install it before trying to use it.")
+
+
+def replace_llama_attn_with_xformers_attn():
+    transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
+
+
+def xformers_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    # pylint: disable=duplicate-code
+    bsz, q_len, _ = hidden_states.size()
+
+    query_states = (
+        self.q_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+    key_states = (
+        self.k_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+    value_states = (
+        self.v_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[-2]
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    (
+        query_states,
+        key_states,
+    ) = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(
+        query_states, key_states, cos, sin, position_ids
+    )
+    # [bsz, nh, t, hd]
+
+    if past_key_value is not None:
+        # reuse k, v, self_attention
+        key_states = torch.cat([past_key_value[0], key_states], dim=2)
+        value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+
+    # We only apply xformers optimizations if we don't need to output the whole attention matrix
+    if not output_attentions:
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        # This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros.
+        # We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros.
+        if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
+            # input and output should be of form (bsz, q_len, num_heads, head_dim)
+            attn_output = xformers.ops.memory_efficient_attention(
+                query_states, key_states, value_states, attn_bias=None
+            )
+        else:
+            # input and output should be of form (bsz, q_len, num_heads, head_dim)
+            attn_output = xformers.ops.memory_efficient_attention(
+                query_states,
+                key_states,
+                value_states,
+                attn_bias=xformers.ops.LowerTriangularMask(),
+            )
+        attn_weights = None
+    else:
+        attn_weights = torch.matmul(
+            query_states, key_states.transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+            attn_weights = torch.max(
+                attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
+            )
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2)
+
+    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+    attn_output = self.o_proj(attn_output)
+    return attn_output, attn_weights, past_key_value
\ No newline at end of file
diff --git a/llava/train/llava_trainer.py b/llava/train/llava_trainer.py
new file mode 100644
index 0000000..b59087d
--- /dev/null
+++ b/llava/train/llava_trainer.py
@@ -0,0 +1,176 @@
+import os
+import torch
+
+from torch.utils.data import Sampler
+
+from transformers import Trainer
+from transformers.trainer import (
+    has_length,
+)
+from typing import List, Optional
+
+
+def maybe_zero_3(param, ignore_status=False, name=None):
+    from deepspeed import zero
+    from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
+    if hasattr(param, "ds_id"):
+        if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
+            if not ignore_status:
+                print(name, 'no ignore status')
+        with zero.GatheredParameters([param]):
+            param = param.data.detach().cpu().clone()
+    else:
+        param = param.detach().cpu().clone()
+    return param
+
+
+def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
+    to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
+    to_return = {k: maybe_zero_3(v, ignore_status=True, name=k).cpu() for k, v in to_return.items()}
+    return to_return
+
+
+def split_to_even_chunks(indices, lengths, num_chunks):
+    """
+    Split a list of indices into `chunks` chunks of roughly equal lengths.
+    """
+
+    if len(indices) % num_chunks != 0:
+        return [indices[i::num_chunks] for i in range(num_chunks)]
+
+    num_indices_per_chunk = len(indices) // num_chunks
+
+    chunks = [[] for _ in range(num_chunks)]
+    chunks_lengths = [0 for _ in range(num_chunks)]
+    for index in indices:
+        shortest_chunk = chunks_lengths.index(min(chunks_lengths))
+        chunks[shortest_chunk].append(index)
+        chunks_lengths[shortest_chunk] += lengths[index]
+        if len(chunks[shortest_chunk]) == num_indices_per_chunk:
+            chunks_lengths[shortest_chunk] = float("inf")
+
+    return chunks
+
+
+def get_modality_length_grouped_indices(lengths, batch_size, world_size, generator=None):
+    # We need to use torch for the random part as a distributed sampler will set the random seed for torch.
+    assert all(l != 0 for l in lengths), "Should not have zero length."
+    mm_indices, mm_lengths = zip(*[(i, l) for i, l in enumerate(lengths) if l > 0])
+    lang_indices, lang_lengths = zip(*[(i, -l) for i, l in enumerate(lengths) if l < 0])
+
+    assert len(mm_indices) > 0, "Should have at least one multimodal sample."
+    assert len(lang_indices) > 0, "Should have at least one language sample."
+
+    mm_shuffle = [mm_indices[i] for i in get_length_grouped_indices(mm_lengths, batch_size, world_size, generator=None)]
+    lang_shuffle = [lang_indices[i] for i in get_length_grouped_indices(lang_lengths, batch_size, world_size, generator=None)]
+    megabatch_size = world_size * batch_size
+    mm_megabatches = [mm_shuffle[i : i + megabatch_size] for i in range(0, len(mm_shuffle), megabatch_size)]
+    lang_megabatches = [lang_shuffle[i : i + megabatch_size] for i in range(0, len(lang_shuffle), megabatch_size)]
+
+    last_mm = mm_megabatches[-1]
+    last_lang = lang_megabatches[-1]
+    additional_batch = last_mm + last_lang
+    megabatches = mm_megabatches[:-1] + lang_megabatches[:-1]
+    megabatch_indices = torch.randperm(len(megabatches), generator=generator)
+    megabatches = [megabatches[i] for i in megabatch_indices]
+
+    if len(additional_batch) >= megabatch_size:
+        megabatches = [additional_batch[:megabatch_size]] + megabatches
+        additional_batch = additional_batch[megabatch_size:]
+
+    if len(additional_batch) > 0:
+        megabatches.append(additional_batch)
+
+    return [i for megabatch in megabatches for i in megabatch]
+
+
+def get_length_grouped_indices(lengths, batch_size, world_size, generator=None, merge=True):
+    # We need to use torch for the random part as a distributed sampler will set the random seed for torch.
+    indices = torch.randperm(len(lengths), generator=generator)
+    megabatch_size = world_size * batch_size
+    megabatches = [indices[i : i + megabatch_size].tolist() for i in range(0, len(lengths), megabatch_size)]
+    megabatches = [sorted(megabatch, key=lambda i: lengths[i], reverse=True) for megabatch in megabatches]
+    megabatches = [split_to_even_chunks(megabatch, lengths, world_size) for megabatch in megabatches]
+
+    return [i for megabatch in megabatches for batch in megabatch for i in batch]
+
+
+class LengthGroupedSampler(Sampler):
+    r"""
+    Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while
+    keeping a bit of randomness.
+    """
+
+    def __init__(
+        self,
+        batch_size: int,
+        world_size: int,
+        lengths: Optional[List[int]] = None,
+        generator=None,
+        group_by_modality: bool = False,
+    ):
+        if lengths is None:
+            raise ValueError("Lengths must be provided.")
+
+        self.batch_size = batch_size
+        self.world_size = world_size
+        self.lengths = lengths
+        self.generator = generator
+        self.group_by_modality = group_by_modality
+
+
+    def __len__(self):
+        return len(self.lengths)
+
+    def __iter__(self):
+        if self.group_by_modality:
+            indices = get_modality_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator)
+        else:
+            indices = get_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator)
+        return iter(indices)
+
+
+class LLaVATrainer(Trainer):
+
+    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
+        if self.train_dataset is None or not has_length(self.train_dataset):
+            return None
+        
+        if self.args.group_by_modality_length:
+            lengths = self.train_dataset.modality_lengths
+            return LengthGroupedSampler(
+                # self.args.train_batch_size * self.args.gradient_accumulation_steps, # TODO: seems that we should not have gradient_accumulation_steps
+                self.args.train_batch_size,
+                world_size=self.args.world_size,
+                lengths=lengths,
+                group_by_modality=True,
+            )
+        else:
+            return super()._get_train_sampler()
+
+    def _save_checkpoint(self, model, trial, metrics=None):
+        if getattr(self.args, 'tune_mm_mlp_adapter', False):
+            from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
+            checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
+
+            run_dir = self._get_output_dir(trial=trial)
+            output_dir = os.path.join(run_dir, checkpoint_folder)
+
+            # Only save Adapter
+            keys_to_match = ['mm_projector', 'vision_resampler']
+            if getattr(self.args, "use_x_start_end", False):
+                keys_to_match.extend(['embed_tokens', 'embed_in'])
+
+            weight_to_save = get_mm_adapter_state_maybe_zero_3(self.model.named_parameters(), keys_to_match)
+
+            if self.args.local_rank == 0 or self.args.local_rank == -1:
+                self.model.config.save_pretrained(output_dir)
+                torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin'))
+        else:
+            super(LLaVATrainer, self)._save_checkpoint(model, trial, metrics)
+
+    def _save(self, output_dir: Optional[str] = None, state_dict=None):
+        if getattr(self.args, 'tune_mm_mlp_adapter', False):
+            pass
+        else:
+            super(LLaVATrainer, self)._save(output_dir, state_dict)
diff --git a/llava/train/train.py b/llava/train/train.py
new file mode 100644
index 0000000..2ab13da
--- /dev/null
+++ b/llava/train/train.py
@@ -0,0 +1,1095 @@
+# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:
+# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright:
+#    Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
+#
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
+
+import os
+import copy
+import random
+from dataclasses import dataclass, field
+import json
+import logging
+import pathlib
+from typing import Dict, Optional, Sequence, List
+
+import torch
+
+import transformers
+
+from llava.constants import IGNORE_INDEX, X_TOKEN_INDEX, DEFAULT_X_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
+from torch.utils.data import Dataset
+from llava.train.llava_trainer import LLaVATrainer
+
+from llava import conversation as conversation_lib
+from llava.model import *
+from llava.mm_utils import tokenizer_X_token
+
+from PIL import Image
+
+
+local_rank = None
+
+
+def rank0_print(*args):
+    if local_rank == 0:
+        print(*args)
+
+
+@dataclass
+class ModelArguments:
+    model_name_or_path: Optional[str] = field(default="facebook/opt-125m")
+    version: Optional[str] = field(default="v0")
+    freeze_backbone: bool = field(default=False)
+    tune_mm_mlp_adapter: bool = field(default=False)
+    X: Optional[List[str]] = field(default=None)
+    image_tower: Optional[str] = field(default=None)
+    video_tower: Optional[str] = field(default=None)
+    mm_vision_select_layer: Optional[int] = field(default=-1)   # default to the last layer
+    pretrain_mm_mlp_adapter: Optional[str] = field(default=None)
+    mm_projector_type: Optional[str] = field(default='linear')
+    mm_use_x_start_end: bool = field(default=False)
+    mm_use_x_patch_token: bool = field(default=True)
+    mm_vision_select_feature: Optional[str] = field(default="patch")
+
+
+@dataclass
+class DataArguments:
+    lazy_preprocess: bool = False
+    is_multimodal: bool = False
+    # data_path: str = field(default=None,
+    #                        metadata={"help": "Path to the training data."})
+    train_data_path: str = field(default=None,metadata={"help": "Path to the training data."})
+    eval_data_path: str = field(default=None,metadata={"help": "Path to the eval data."})
+    image_folder: Optional[str] = field(default=None)
+    image_aspect_ratio: str = 'square'
+    image_grid_pinpoints: Optional[str] = field(default=None)
+    video_folder: Optional[str] = field(default=None)
+
+
+@dataclass
+class TrainingArguments(transformers.TrainingArguments):
+    cache_dir: Optional[str] = field(default=None)
+    optim: str = field(default="adamw_torch")
+    remove_unused_columns: bool = field(default=False)
+    freeze_mm_mlp_adapter: bool = field(default=False)
+    mpt_attn_impl: Optional[str] = field(default="triton")
+    model_max_length: int = field(
+        default=512,
+        metadata={
+            "help":
+            "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
+        },
+    )
+    double_quant: bool = field(
+        default=True,
+        metadata={"help": "Compress the quantization statistics through double quantization."}
+    )
+    quant_type: str = field(
+        default="nf4",
+        metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
+    )
+    bits: int = field(
+        default=16,
+        metadata={"help": "How many bits to use."}
+    )
+    lora_enable: bool = False
+    lora_r: int = 64
+    lora_alpha: int = 16
+    lora_dropout: float = 0.05
+    lora_weight_path: str = ""
+    lora_bias: str = "none"
+    group_by_modality_length: bool = field(default=False)
+
+
+def maybe_zero_3(param, ignore_status=False, name=None):
+    from deepspeed import zero
+    from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
+    if hasattr(param, "ds_id"):
+        if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
+            if not ignore_status:
+                logging.warning(f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}")
+        with zero.GatheredParameters([param]):
+            param = param.data.detach().cpu().clone()
+    else:
+        param = param.detach().cpu().clone()
+    return param
+
+
+# Borrowed from peft.utils.get_peft_model_state_dict
+def get_peft_state_maybe_zero_3(named_params, bias):
+    if bias == "none":
+        to_return = {k: t for k, t in named_params if "lora_" in k}
+    elif bias == "all":
+        to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
+    elif bias == "lora_only":
+        to_return = {}
+        maybe_lora_bias = {}
+        lora_bias_names = set()
+        for k, t in named_params:
+            if "lora_" in k:
+                to_return[k] = t
+                bias_name = k.split("lora_")[0] + "bias"
+                lora_bias_names.add(bias_name)
+            elif "bias" in k:
+                maybe_lora_bias[k] = t
+        for k, t in maybe_lora_bias:
+            if bias_name in lora_bias_names:
+                to_return[bias_name] = t
+    else:
+        raise NotImplementedError
+    to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()}
+    return to_return
+
+
+def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True):
+    to_return = {k: t for k, t in named_params if "lora_" not in k}
+    if require_grad_only:
+        to_return = {k: t for k, t in to_return.items() if t.requires_grad}
+    to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
+    return to_return
+
+
+def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
+    to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
+    to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
+    return to_return
+
+
+def find_all_linear_names(model):
+    cls = torch.nn.Linear
+    lora_module_names = set()
+    for name, module in model.named_modules():
+        if isinstance(module, cls):
+            names = name.split('.')
+            lora_module_names.add(names[0] if len(names) == 1 else names[-1])
+
+
+    if 'lm_head' in lora_module_names: # needed for 16-bit
+        lora_module_names.remove('lm_head')
+    return list(lora_module_names)
+
+
+def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
+                                   output_dir: str):
+    """Collects the state dict and dump to disk."""
+
+    if getattr(trainer.args, "tune_mm_mlp_adapter", False):
+        # Only save Adapter
+        keys_to_match = ['mm_projector']
+        if getattr(trainer.args, "use_im_start_end", False):
+            keys_to_match.extend(['embed_tokens', 'embed_in'])
+
+        weight_to_save = get_mm_adapter_state_maybe_zero_3(trainer.model.named_parameters(), keys_to_match)
+        trainer.model.config.save_pretrained(output_dir)
+
+        current_folder = output_dir.split('/')[-1]
+        parent_folder = os.path.dirname(output_dir)
+        if trainer.args.local_rank == 0 or trainer.args.local_rank == -1:
+            if current_folder.startswith('checkpoint-'):
+                mm_projector_folder = os.path.join(parent_folder, "mm_projector")
+                os.makedirs(mm_projector_folder, exist_ok=True)
+                torch.save(weight_to_save, os.path.join(mm_projector_folder, f'{current_folder}.bin'))
+            else:
+                torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin'))
+        return
+
+    if trainer.deepspeed:
+        torch.cuda.synchronize()
+        trainer.save_model(output_dir)
+        return
+
+    state_dict = trainer.model.state_dict()
+    if trainer.args.should_save:
+        cpu_state_dict = {
+            key: value.cpu()
+            for key, value in state_dict.items()
+        }
+        del state_dict
+        trainer._save(output_dir, state_dict=cpu_state_dict)  # noqa
+
+
+def smart_tokenizer_and_embedding_resize(
+    special_tokens_dict: Dict,
+    tokenizer: transformers.PreTrainedTokenizer,
+    model: transformers.PreTrainedModel,
+):
+    """Resize tokenizer and embedding.
+
+    Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
+    """
+    num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
+    model.resize_token_embeddings(len(tokenizer))
+
+    if num_new_tokens > 0:
+        input_embeddings = model.get_input_embeddings().weight.data
+        output_embeddings = model.get_output_embeddings().weight.data
+
+        input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
+            dim=0, keepdim=True)
+        output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
+            dim=0, keepdim=True)
+
+        input_embeddings[-num_new_tokens:] = input_embeddings_avg
+        output_embeddings[-num_new_tokens:] = output_embeddings_avg
+
+
+def _tokenize_fn(strings: Sequence[str],
+                 tokenizer: transformers.PreTrainedTokenizer) -> Dict:
+    """Tokenize a list of strings."""
+    tokenized_list = [
+        tokenizer(
+            text,
+            return_tensors="pt",
+            padding="longest",
+            max_length=tokenizer.model_max_length,
+            truncation=True,
+        ) for text in strings
+    ]
+    input_ids = labels = [
+        tokenized.input_ids[0] for tokenized in tokenized_list
+    ]
+    input_ids_lens = labels_lens = [
+        tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item()
+        for tokenized in tokenized_list
+    ]
+    return dict(
+        input_ids=input_ids,
+        labels=labels,
+        input_ids_lens=input_ids_lens,
+        labels_lens=labels_lens,
+    )
+
+
+def _mask_targets(target, tokenized_lens, speakers):
+    # cur_idx = 0
+    cur_idx = tokenized_lens[0]
+    tokenized_lens = tokenized_lens[1:]
+    target[:cur_idx] = IGNORE_INDEX
+    for tokenized_len, speaker in zip(tokenized_lens, speakers):
+        if speaker == "human":
+            target[cur_idx+2:cur_idx + tokenized_len] = IGNORE_INDEX
+        cur_idx += tokenized_len
+
+
+def _add_speaker_and_signal(header, source, get_conversation=True):
+    """Add speaker and start/end signal on each round."""
+    BEGIN_SIGNAL = "### "
+    END_SIGNAL = "\n"
+    conversation = header
+    for sentence in source:
+        from_str = sentence["from"]
+        if from_str.lower() == "human":
+            from_str = conversation_lib.default_conversation.roles[0]
+        elif from_str.lower() == "gpt":
+            from_str = conversation_lib.default_conversation.roles[1]
+        else:
+            from_str = 'unknown'
+        sentence["value"] = (BEGIN_SIGNAL + from_str + ": " +
+                             sentence["value"] + END_SIGNAL)
+        if get_conversation:
+            conversation += sentence["value"]
+    conversation += BEGIN_SIGNAL
+    return conversation
+
+
+def preprocess_multimodal(
+    sources: Sequence[str],
+    data_args: DataArguments
+) -> Dict:
+    '''
+    [
+      {
+        "from": "human",
+        "value": "Render a clear and concise summary of the photo.\n<image>"
+      },
+      {
+        "from": "gpt",
+        "value": "select luxury furniture 3 - inch gel memory foam mattress topper"
+      }
+    ]
+    '''
+    is_multimodal = data_args.is_multimodal
+    if not is_multimodal:
+        return sources
+
+    # for source in sources:
+    #     for sentence in source:
+    #         if DEFAULT_IMAGE_TOKEN in sentence['value']:
+    #             sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '').strip()
+    #             sentence['value'] = DEFAULT_IMAGE_TOKEN + '\n' + sentence['value']
+    #             sentence['value'] = sentence['value'].strip()
+    #             if "mmtag" in conversation_lib.default_conversation.version:
+    #                 sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '<Image>' + DEFAULT_IMAGE_TOKEN + '</Image>')
+    #         replace_token = DEFAULT_IMAGE_TOKEN
+    #         if data_args.mm_use_im_start_end:
+    #             replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
+    #         sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token)
+
+
+    # for source in sources:
+    #     for sentence in source:
+    #         for DEFAULT_TOKEN in DEFAULT_X_TOKEN.values():
+    #             X = DEFAULT_TOKEN[1:-1]  # <image> to image
+    #             if DEFAULT_TOKEN in sentence['value']:
+    #                 sentence['value'] = sentence['value'].replace(DEFAULT_TOKEN, '').strip()
+    #                 sentence['value'] = DEFAULT_TOKEN + '\n' + sentence['value']
+    #                 sentence['value'] = sentence['value'].strip()
+    #                 if "mmtag" in conversation_lib.default_conversation.version:
+    #                     sentence['value'] = sentence['value'].replace(DEFAULT_TOKEN, f'<{X.capitalize()}>' + DEFAULT_TOKEN + f'</{X.capitalize()}>')
+    #             replace_token = DEFAULT_TOKEN
+    #             if data_args.mm_use_x_start_end:
+    #                 replace_token = DEFAULT_X_START_TOKEN[X.upper()] + replace_token + DEFAULT_X_END_TOKEN[X.upper()]
+    #             sentence["value"] = sentence["value"].replace(DEFAULT_TOKEN, replace_token)
+
+    return sources
+
+
+def preprocess_llama_2(
+    sources,
+    tokenizer: transformers.PreTrainedTokenizer,
+    X: str = None
+) -> Dict:
+    conv = conversation_lib.default_conversation.copy()
+    roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
+
+    # Apply prompt templates
+    conversations = []
+    for i, source in enumerate(sources):
+        if roles[source[0]["from"]] != conv.roles[0]:
+            # Skip the first one if it is not from human
+            source = source[1:]
+
+        conv.messages = []
+        for j, sentence in enumerate(source):
+            role = roles[sentence["from"]]
+            assert role == conv.roles[j % 2], f"{i}"
+            conv.append_message(role, sentence["value"])
+        conversations.append(conv.get_prompt())
+
+    # Tokenize conversations
+
+    if X is not None:
+        input_ids = torch.stack([tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX[X], return_tensors='pt') for prompt in conversations], dim=0)
+    else:
+        input_ids = tokenizer(
+            conversations,
+            return_tensors="pt",
+            padding="longest",
+            max_length=tokenizer.model_max_length,
+            truncation=True,
+        ).input_ids
+
+    targets = input_ids.clone()
+
+    assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_2
+
+    # Mask targets
+    sep = "[/INST] "
+    for conversation, target in zip(conversations, targets):
+        total_len = int(target.ne(tokenizer.pad_token_id).sum())
+
+        rounds = conversation.split(conv.sep2)
+        cur_len = 1
+        target[:cur_len] = IGNORE_INDEX
+        for i, rou in enumerate(rounds):
+            if rou == "":
+                break
+
+            parts = rou.split(sep)
+            if len(parts) != 2:
+                break
+            parts[0] += sep
+
+            if X is not None:
+                round_len = len(tokenizer_X_token(rou, tokenizer, X_TOKEN_INDEX[X]))
+                instruction_len = len(tokenizer_X_token(parts[0], tokenizer, X_TOKEN_INDEX[X])) - 2
+            else:
+                round_len = len(tokenizer(rou).input_ids)
+                instruction_len = len(tokenizer(parts[0]).input_ids) - 2
+
+            target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
+
+            cur_len += round_len
+        target[cur_len:] = IGNORE_INDEX
+
+        if cur_len < tokenizer.model_max_length:
+            if cur_len != total_len:
+                target[:] = IGNORE_INDEX
+                print(
+                    f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
+                    f" (ignored)"
+                )
+
+    return dict(
+        input_ids=input_ids,
+        labels=targets,
+    )
+
+
+def preprocess_v1(
+    sources,
+    tokenizer: transformers.PreTrainedTokenizer,
+    X: str = None
+) -> Dict:
+    conv = conversation_lib.default_conversation.copy()
+    roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
+
+    # Apply prompt templates
+    conversations = []
+    for i, source in enumerate(sources):
+        if roles[source[0]["from"]] != conv.roles[0]:
+            # Skip the first one if it is not from human
+            source = source[1:]
+
+        conv.messages = []
+        for j, sentence in enumerate(source):
+            role = roles[sentence["from"]]
+            assert role == conv.roles[j % 2], f"{i}"
+            conv.append_message(role, sentence["value"])
+        conversations.append(conv.get_prompt())
+
+    # Tokenize conversations
+
+    if X is not None:
+        input_ids = torch.stack([tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX[X], return_tensors='pt') for prompt in conversations], dim=0)
+    else:
+        input_ids = tokenizer(
+            conversations,
+            return_tensors="pt",
+            padding="longest",
+            max_length=tokenizer.model_max_length,
+            truncation=True,
+        ).input_ids
+
+    targets = input_ids.clone()
+
+    assert conv.sep_style == conversation_lib.SeparatorStyle.TWO
+
+    # Mask targets
+    sep = conv.sep + conv.roles[1] + ": "
+    for conversation, target in zip(conversations, targets):
+        total_len = int(target.ne(tokenizer.pad_token_id).sum())
+
+        rounds = conversation.split(conv.sep2)
+        cur_len = 1
+        target[:cur_len] = IGNORE_INDEX
+        for i, rou in enumerate(rounds):
+            if rou == "":
+                break
+
+            parts = rou.split(sep)
+            if len(parts) != 2:
+                break
+            parts[0] += sep
+
+            if X is not None:
+                round_len = len(tokenizer_X_token(rou, tokenizer, X_TOKEN_INDEX[X]))
+                instruction_len = len(tokenizer_X_token(parts[0], tokenizer, X_TOKEN_INDEX[X])) - 2
+            else:
+                round_len = len(tokenizer(rou).input_ids)
+                instruction_len = len(tokenizer(parts[0]).input_ids) - 2
+
+            target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
+
+            cur_len += round_len
+        target[cur_len:] = IGNORE_INDEX
+
+        if cur_len < tokenizer.model_max_length:
+            if cur_len != total_len:
+                target[:] = IGNORE_INDEX
+                print(
+                    f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
+                    f" (ignored)"
+                )
+
+    return dict(
+        input_ids=input_ids,
+        labels=targets,
+    )
+
+
+def preprocess_mpt(
+    sources,
+    tokenizer: transformers.PreTrainedTokenizer,
+    X: str = None
+) -> Dict:
+    conv = conversation_lib.default_conversation.copy()
+    roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
+
+    # Apply prompt templates
+    conversations = []
+    for i, source in enumerate(sources):
+        if roles[source[0]["from"]] != conv.roles[0]:
+            # Skip the first one if it is not from human
+            source = source[1:]
+
+        conv.messages = []
+        for j, sentence in enumerate(source):
+            role = roles[sentence["from"]]
+            assert role == conv.roles[j % 2], f"{i}"
+            conv.append_message(role, sentence["value"])
+        conversations.append(conv.get_prompt())
+
+    # Tokenize conversations
+    input_ids = torch.stack([tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX[X], return_tensors='pt') for prompt in conversations], dim=0)
+    targets = input_ids.clone()
+    assert conv.sep_style == conversation_lib.SeparatorStyle.MPT
+
+    # Mask targets
+    sep = conv.sep + conv.roles[1]
+    for conversation, target in zip(conversations, targets):
+        total_len = int(target.ne(tokenizer.pad_token_id).sum())
+
+        rounds = conversation.split(conv.sep)
+        re_rounds = [conv.sep.join(rounds[:3])] # system + user + gpt
+        for conv_idx in range(3, len(rounds), 2):
+            re_rounds.append(conv.sep.join(rounds[conv_idx:conv_idx+2]))    # user + gpt
+        cur_len = 0
+        target[:cur_len] = IGNORE_INDEX
+        for i, rou in enumerate(re_rounds):
+            if rou == "":
+                break
+
+            parts = rou.split(sep)
+            if len(parts) != 2:
+                break
+            parts[0] += sep
+            round_len = len(tokenizer_X_token(rou, tokenizer, X_TOKEN_INDEX[X])) + len(tokenizer_X_token(conv.sep, tokenizer))
+            instruction_len = len(tokenizer_X_token(parts[0], tokenizer, X_TOKEN_INDEX[X]))
+            target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
+
+            cur_len += round_len
+        target[cur_len:] = IGNORE_INDEX
+
+        if cur_len < tokenizer.model_max_length:
+            if cur_len != total_len:
+                target[:] = IGNORE_INDEX
+                print(
+                    f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
+                    f" (ignored)"
+                )
+
+    return dict(
+        input_ids=input_ids,
+        labels=targets,
+    )
+
+
+def preprocess_plain(
+    sources: Sequence[str],
+    tokenizer: transformers.PreTrainedTokenizer,
+    X: str
+) -> Dict:
+    DEFAULT_TOKEN = DEFAULT_X_TOKEN[X]
+    # add end signal and concatenate together
+    conversations = []
+    for source in sources:
+        assert len(source) == 2
+        assert DEFAULT_TOKEN in source[0]['value']
+        source[0]['value'] = DEFAULT_TOKEN
+        conversation = source[0]['value'] + source[1]['value'] + conversation_lib.default_conversation.sep
+        conversations.append(conversation)
+    # tokenize conversations
+    input_ids = [tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX[X], return_tensors='pt') for prompt in conversations]
+    targets = copy.deepcopy(input_ids)
+    for target, source in zip(targets, sources):
+        tokenized_len = len(tokenizer_X_token(source[0]['value'], tokenizer, X_TOKEN_INDEX[X]))
+        target[:tokenized_len] = IGNORE_INDEX
+
+    return dict(input_ids=input_ids, labels=targets)
+
+
+def preprocess(
+    sources: Sequence[str],
+    tokenizer: transformers.PreTrainedTokenizer,
+    has_X: str = None
+) -> Dict:
+    """
+    Given a list of sources, each is a conversation list. This transform:
+    1. Add signal '### ' at the beginning each sentence, with end signal '\n';
+    2. Concatenate conversations together;
+    3. Tokenize the concatenated conversation;
+    4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX.
+    """
+    X = has_X if has_X is None else has_X.upper()
+    if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.PLAIN:
+        return preprocess_plain(sources, tokenizer, X=X)
+    if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.LLAMA_2:
+        return preprocess_llama_2(sources, tokenizer, X=X)
+    if conversation_lib.default_conversation.version.startswith("v1"):
+        return preprocess_v1(sources, tokenizer, X=X)
+    if conversation_lib.default_conversation.version == "mpt":
+        return preprocess_mpt(sources, tokenizer, X=X)
+    # add end signal and concatenate together
+    conversations = []
+    for source in sources:
+        header = f"{conversation_lib.default_conversation.system}\n\n"
+        conversation = _add_speaker_and_signal(header, source)
+        conversations.append(conversation)
+    # tokenize conversations
+    def get_tokenize_len(prompts):
+        return [len(tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX[X])) for prompt in prompts]
+
+    if X is not None:
+        input_ids = [tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX[X], return_tensors='pt') for prompt in conversations]
+    else:
+        conversations_tokenized = _tokenize_fn(conversations, tokenizer)
+        input_ids = conversations_tokenized["input_ids"]
+
+    targets = copy.deepcopy(input_ids)
+    for target, source in zip(targets, sources):
+        if X is not None:
+            tokenized_lens = get_tokenize_len([header] + [s["value"] for s in source])
+        else:
+            tokenized_lens = _tokenize_fn([header] + [s["value"] for s in source], tokenizer)["input_ids_lens"]
+        speakers = [sentence["from"] for sentence in source]
+        _mask_targets(target, tokenized_lens, speakers)
+
+    return dict(input_ids=input_ids, labels=targets)
+
+
+class LazySupervisedDataset(Dataset):
+    """Dataset for supervised fine-tuning."""
+
+    def __init__(self, data_path: str,
+                 tokenizer: transformers.PreTrainedTokenizer,
+                 data_args: DataArguments):
+        super(LazySupervisedDataset, self).__init__()
+        list_data_dict = json.load(open(data_path, "r"))
+
+        rank0_print("Formatting inputs...Skip in lazy mode")
+        self.tokenizer = tokenizer
+        self.list_data_dict = list_data_dict
+        self.data_args = data_args
+
+    def __len__(self):
+        # return 20
+        return len(self.list_data_dict)
+
+    @property
+    def lengths(self):
+        length_list = []
+        for sample in self.list_data_dict:
+            img_tokens = 128 if any([x.lower() in sample for x in DEFAULT_X_TOKEN.keys()]) else 0
+            length_list.append(sum(len(conv['value'].split()) for conv in sample['conversations']) + img_tokens)
+        return length_list
+
+    @property
+    def modality_lengths(self):
+        length_list = []
+        for sample in self.list_data_dict:
+            cur_len = sum(len(conv['value'].split()) for conv in sample['conversations'])
+            cur_len = cur_len if any([x.lower() in sample for x in DEFAULT_X_TOKEN.keys()]) else -cur_len
+            length_list.append(cur_len)
+        return length_list
+
+    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
+        '''
+        {
+            "id": "004539375",
+            "image": "00453/004539375.jpg",
+            "conversations": [
+              {
+                "from": "human",
+                "value": "Render a clear and concise summary of the photo.\n<image>"
+              },
+              {
+                "from": "gpt",
+                "value": "select luxury furniture 3 - inch gel memory foam mattress topper"
+              }
+            ]
+        },
+        '''
+        try:
+            has_X = None
+            sources = self.list_data_dict[i]
+            if isinstance(i, int):
+                sources = [sources]
+            assert len(sources) == 1, "Don't know why it is wrapped to a list"  # FIXME
+            if 'image' in sources[0]:
+                image_file = self.list_data_dict[i]['image']
+                image_folder = self.data_args.image_folder
+                processor = self.data_args.image_processor
+                image = Image.open(os.path.join(image_folder, image_file)).convert('RGB')
+                if self.data_args.image_aspect_ratio == 'pad':
+                    def expand2square(pil_img, background_color):
+                        width, height = pil_img.size
+                        if width == height:
+                            return pil_img
+                        elif width > height:
+                            result = Image.new(pil_img.mode, (width, width), background_color)
+                            result.paste(pil_img, (0, (width - height) // 2))
+                            return result
+                        else:
+                            result = Image.new(pil_img.mode, (height, height), background_color)
+                            result.paste(pil_img, ((height - width) // 2, 0))
+                            return result
+                    image = expand2square(image, tuple(int(x*255) for x in processor.image_mean))
+                    image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
+                else:
+                    image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
+                sources = preprocess_multimodal(copy.deepcopy([e["conversations"] for e in sources]), self.data_args)
+                has_X = 'image'
+
+            elif 'video' in sources[0]:
+                # video_file = self.list_data_dict[i]['video']
+                # video_folder = self.data_args.video_folder
+                # processor = self.data_args.video_processor
+                # video = os.path.join(video_folder, video_file)
+                # # print(video)
+                # video = processor(video, return_tensors='pt')['pixel_values'][0]
+                # # print(video, 'success')
+                # sources = preprocess_multimodal(copy.deepcopy([e["conversations"] for e in sources]), self.data_args)
+                # has_X = 'video'
+                
+                video_files = self.list_data_dict[i]['video'] # A list 
+                video_folder = self.data_args.video_folder
+                processor = self.data_args.video_processor
+                videos = []
+                for video_file in video_files:
+                    video = os.path.join(video_folder, video_file)
+                    # print(video)
+                    # import pdb; pdb.set_trace()
+                    video = processor(video, return_tensors='pt')['pixel_values'][0]
+                    videos.append(video)
+                    
+                sources = preprocess_multimodal(copy.deepcopy([e["conversations"] for e in sources]), self.data_args)
+                has_X = 'video'
+                
+                
+            else:
+                sources = copy.deepcopy([e["conversations"] for e in sources])
+
+
+            data_dict = preprocess(
+                sources,
+                self.tokenizer,
+                has_X=has_X)
+            if isinstance(i, int):
+                data_dict = dict(input_ids=data_dict["input_ids"][0],
+                                 labels=data_dict["labels"][0])
+
+            # image exist in the data
+            if 'image' in self.list_data_dict[i]:
+                data_dict['image'] = image
+            elif 'video' in self.list_data_dict[i]:
+                data_dict['video'] = videos
+                
+            elif self.data_args.is_multimodal:
+                # image does not exist in the data, but the model is multimodal
+                # 目前只支持一个sample一个modal
+                if hasattr(self.data_args, 'image_processor') and hasattr(self.data_args, 'video_processor'):
+                    # crop_size = self.data_args.image_processor.crop_size
+                    crop_size = dict(height=224, width=224)
+                    data_dict['image'] = torch.zeros(3, crop_size['height'], crop_size['width'])
+                else:
+                    if hasattr(self.data_args, 'image_processor'):
+                        crop_size = self.data_args.image_processor.crop_size
+                        data_dict['image'] = torch.zeros(3, crop_size['height'], crop_size['width'])
+                    if hasattr(self.data_args, 'video_processor'):
+                        data_dict['video'] = torch.zeros(3, 8, 224, 224)
+            return data_dict
+        except Exception as e:
+            print(f'Error with {e}')
+            return self.__getitem__(random.randint(0, self.__len__()-1))
+
+@dataclass
+class DataCollatorForSupervisedDataset(object):
+    """Collate examples for supervised fine-tuning."""
+
+    tokenizer: transformers.PreTrainedTokenizer
+
+    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
+        input_ids, labels = tuple([instance[key] for instance in instances]
+                                  for key in ("input_ids", "labels"))
+        input_ids = torch.nn.utils.rnn.pad_sequence(
+            input_ids,
+            batch_first=True,
+            padding_value=self.tokenizer.pad_token_id)
+        labels = torch.nn.utils.rnn.pad_sequence(labels,
+                                                 batch_first=True,
+                                                 padding_value=IGNORE_INDEX)
+        input_ids = input_ids[:, :self.tokenizer.model_max_length]
+        labels = labels[:, :self.tokenizer.model_max_length]
+        batch = dict(
+            input_ids=input_ids,
+            labels=labels,
+            attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
+        )
+
+        # if 'image' in instances[0]:
+        #     images = [instance['image'] for instance in instances]
+        #     if all(x is not None and x.shape == images[0].shape for x in images):
+        #         batch['images'] = torch.stack(images)
+        #     else:
+        #         batch['images'] = images
+        '''
+        X_modalities [
+        [img_feature, img_feature, video_feature, audio_feature],
+        ['image', 'image', 'video', 'audio']
+        ]
+        '''
+        # X_modalities = []
+        Xs, keys = [], []
+        for instance in instances:
+            for x in DEFAULT_X_TOKEN.keys():
+                x = x.lower()
+                if x in instance:
+                    Xs.append(instance[x])
+                    keys.append(x)
+        batch['images'] = [Xs, keys]  # we do not change the key's name.
+        return batch
+
+
+def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer,
+                                data_args) -> Dict:
+    """Make dataset and collator for supervised fine-tuning."""
+    train_dataset = LazySupervisedDataset(tokenizer=tokenizer,
+                                data_path=data_args.train_data_path,
+                                data_args=data_args)
+    eval_dataset = LazySupervisedDataset(tokenizer=tokenizer,
+                                data_path=data_args.eval_data_path,
+                                data_args=data_args)
+    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
+    return dict(train_dataset=train_dataset,
+                eval_dataset=eval_dataset,
+                data_collator=data_collator)
+
+
+def train():
+    global local_rank
+    parser = transformers.HfArgumentParser(
+        (ModelArguments, DataArguments, TrainingArguments))
+    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+    local_rank = training_args.local_rank
+    compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
+
+    bnb_model_from_pretrained_args = {}
+    if training_args.bits in [4, 8]:
+        from transformers import BitsAndBytesConfig
+        bnb_model_from_pretrained_args.update(dict(
+            device_map={"": training_args.device},
+            load_in_4bit=training_args.bits == 4,
+            load_in_8bit=training_args.bits == 8,
+            quantization_config=BitsAndBytesConfig(
+                load_in_4bit=training_args.bits == 4,
+                load_in_8bit=training_args.bits == 8,
+                llm_int8_threshold=6.0,
+                llm_int8_has_fp16_weight=False,
+                bnb_4bit_compute_dtype=compute_dtype,
+                bnb_4bit_use_double_quant=training_args.double_quant,
+                bnb_4bit_quant_type=training_args.quant_type # {'fp4', 'nf4'}
+            )
+        ))
+
+    if model_args.image_tower is not None or model_args.video_tower is not None:  ####################################################
+        if 'mpt' in model_args.model_name_or_path:
+            config = transformers.AutoConfig.from_pretrained(model_args.model_name_or_path, trust_remote_code=True)
+            config.attn_config['attn_impl'] = training_args.mpt_attn_impl
+            model = LlavaMPTForCausalLM.from_pretrained(
+                model_args.model_name_or_path,
+                config=config,
+                cache_dir=training_args.cache_dir,
+                **bnb_model_from_pretrained_args
+            )
+        else:
+            model = LlavaLlamaForCausalLM.from_pretrained(
+                model_args.model_name_or_path,
+                cache_dir=training_args.cache_dir,
+                **bnb_model_from_pretrained_args
+            )
+    else:
+        model = transformers.LlamaForCausalLM.from_pretrained(
+            model_args.model_name_or_path,
+            cache_dir=training_args.cache_dir,
+            **bnb_model_from_pretrained_args
+        )
+    model.config.use_cache = False
+    model.config.X = model_args.X
+
+    if model_args.freeze_backbone:
+        model.model.requires_grad_(False)
+
+    if training_args.bits in [4, 8]:
+        from peft import prepare_model_for_kbit_training
+        model.config.torch_dtype=(torch.float32 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
+        model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=training_args.gradient_checkpointing)
+
+    if training_args.gradient_checkpointing:
+        if hasattr(model, "enable_input_require_grads"):
+            model.enable_input_require_grads()
+        else:
+            def make_inputs_require_grad(module, input, output):
+                output.requires_grad_(True)
+            model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+    if training_args.lora_enable:
+        from peft import LoraConfig, get_peft_model
+        lora_config = LoraConfig(
+            r=training_args.lora_r,
+            lora_alpha=training_args.lora_alpha,
+            target_modules=find_all_linear_names(model),
+            lora_dropout=training_args.lora_dropout,
+            bias=training_args.lora_bias,
+            task_type="CAUSAL_LM",
+        )
+        if training_args.bits == 16:
+            if training_args.bf16:
+                model.to(torch.bfloat16)
+            if training_args.fp16:
+                model.to(torch.float16)
+        rank0_print("Adding LoRA adapters...")
+        model = get_peft_model(model, lora_config)
+
+    if 'mpt' in model_args.model_name_or_path:
+        tokenizer = transformers.AutoTokenizer.from_pretrained(
+            model_args.model_name_or_path,
+            cache_dir=training_args.cache_dir,
+            model_max_length=training_args.model_max_length,
+            padding_side="right"
+        )
+    else:
+        tokenizer = transformers.AutoTokenizer.from_pretrained(
+            model_args.model_name_or_path,
+            cache_dir=training_args.cache_dir,
+            model_max_length=training_args.model_max_length,
+            padding_side="right",
+            use_fast=False,
+        )
+
+    if model_args.version == "v0":
+        if tokenizer.pad_token is None:
+            smart_tokenizer_and_embedding_resize(
+                special_tokens_dict=dict(pad_token="[PAD]"),
+                tokenizer=tokenizer,
+                model=model,
+            )
+    elif model_args.version == "v0.5":
+        tokenizer.pad_token = tokenizer.unk_token
+    # Execute Here!
+    else:
+        # Hack here to use Driving Specific Training Prompt Header
+        tokenizer.pad_token = tokenizer.unk_token
+        # if model_args.version in conversation_lib.conv_templates:
+            # conversation_lib.default_conversation = conversation_lib.conv_templates[model_args.version]
+        conversation_lib.default_conversation = conversation_lib.conv_templates["driving"]
+        # else:
+        #     conversation_lib.default_conversation = conversation_lib.conv_templates["vicuna_v1"]
+
+    if model_args.image_tower is not None or model_args.video_tower is not None:  #############################
+
+        if model_args.image_tower is not None:
+            model.get_model().initialize_image_modules(
+                model_args=model_args,
+                fsdp=training_args.fsdp
+            )
+            image_tower = model.get_image_tower()
+            image_tower.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device)
+
+            data_args.image_processor = image_tower.image_processor
+            data_args.is_multimodal = True
+
+            model.config.image_aspect_ratio = data_args.image_aspect_ratio
+            model.config.image_grid_pinpoints = data_args.image_grid_pinpoints
+
+        if model_args.video_tower is not None:
+            model.get_model().initialize_video_modules(
+                model_args=model_args,
+                fsdp=training_args.fsdp
+            )
+            video_tower = model.get_video_tower()
+            video_tower.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device)
+
+            data_args.video_processor = video_tower.video_processor
+            data_args.is_multimodal = True
+
+            # model.config.image_aspect_ratio = data_args.image_aspect_ratio
+            # model.config.image_grid_pinpoints = data_args.image_grid_pinpoints
+
+        model.config.tune_mm_mlp_adapter = training_args.tune_mm_mlp_adapter = model_args.tune_mm_mlp_adapter
+        if model_args.tune_mm_mlp_adapter:
+            model.requires_grad_(False)
+            for p in model.get_model().mm_projector.parameters():
+                p.requires_grad = True
+
+        model.config.freeze_mm_mlp_adapter = training_args.freeze_mm_mlp_adapter
+        if training_args.freeze_mm_mlp_adapter:
+            for p in model.get_model().mm_projector.parameters():
+                p.requires_grad = False
+
+        if training_args.bits in [4, 8]:
+            model.get_model().mm_projector.to(dtype=compute_dtype, device=training_args.device)
+
+        model.config.mm_use_x_start_end = data_args.mm_use_x_start_end = model_args.mm_use_x_start_end
+        training_args.use_x_start_end = model_args.mm_use_x_start_end
+        model.config.mm_use_x_patch_token = model_args.mm_use_x_patch_token
+        model.initialize_X_tokenizer(model_args, tokenizer=tokenizer)
+
+    if training_args.bits in [4, 8]:
+        from peft.tuners.lora import LoraLayer
+        for name, module in model.named_modules():
+            if isinstance(module, LoraLayer):
+                if training_args.bf16:
+                    module = module.to(torch.bfloat16)
+            if 'norm' in name:
+                module = module.to(torch.float32)
+            if 'lm_head' in name or 'embed_tokens' in name:
+                if hasattr(module, 'weight'):
+                    if training_args.bf16 and module.weight.dtype == torch.float32:
+                        module = module.to(torch.bfloat16)
+
+    data_module = make_supervised_data_module(tokenizer=tokenizer,
+                                              data_args=data_args)
+
+    #################
+    # print(f'{training_args}')
+    # print(f'{model_args}')
+    for n, p in model.named_parameters():
+        if p.requires_grad:
+            print(f'{n}==>{p.requires_grad}')
+    print(f'{model}')
+    print(model.device)
+    #################
+
+    trainer = LLaVATrainer(model=model,
+                    tokenizer=tokenizer,
+                    args=training_args,
+                    **data_module)
+
+    if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
+        trainer.train(resume_from_checkpoint=True)
+    else:
+        trainer.train()
+    trainer.save_state()
+
+    model.config.use_cache = True
+
+    if training_args.lora_enable:
+        state_dict = get_peft_state_maybe_zero_3(
+            model.named_parameters(), training_args.lora_bias
+        )
+        non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3(
+            model.named_parameters()
+        )
+        if training_args.local_rank == 0 or training_args.local_rank == -1:
+            model.config.save_pretrained(training_args.output_dir)
+            model.save_pretrained(training_args.output_dir, state_dict=state_dict)
+            torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin'))
+    else:
+        safe_save_model_for_hf_trainer(trainer=trainer,
+                                       output_dir=training_args.output_dir)
+
+
+if __name__ == "__main__":
+    train()
diff --git a/llava/train/train_mem.py b/llava/train/train_mem.py
new file mode 100644
index 0000000..55bcb20
--- /dev/null
+++ b/llava/train/train_mem.py
@@ -0,0 +1,18 @@
+# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:
+# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright:
+# Make it more memory efficient by monkey patching the LLaMA model with FlashAttn.
+# Need to call this before importing transformers.
+from llava.train.llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn
+
+replace_llama_attn_with_flash_attn()
+
+from llava.train.train import train
+
+import warnings
+
+# Filter out warnings with a specific keyword or phrase
+warnings.filterwarnings('ignore', 
+                        message='The default value of the antialias parameter')
+
+if __name__ == "__main__":
+    train()
diff --git a/llava/train/train_xformers.py b/llava/train/train_xformers.py
new file mode 100644
index 0000000..c0ef821
--- /dev/null
+++ b/llava/train/train_xformers.py
@@ -0,0 +1,13 @@
+# Make it more memory efficient by monkey patching the LLaMA model with xformers attention.
+
+# Need to call this before importing transformers.
+from llava.train.llama_xformers_attn_monkey_patch import (
+    replace_llama_attn_with_xformers_attn,
+)
+
+replace_llama_attn_with_xformers_attn()
+
+from llava.train.train import train
+
+if __name__ == "__main__":
+    train()
\ No newline at end of file
diff --git a/llava/utils.py b/llava/utils.py
new file mode 100644
index 0000000..8f7163c
--- /dev/null
+++ b/llava/utils.py
@@ -0,0 +1,126 @@
+import datetime
+import logging
+import logging.handlers
+import os
+import sys
+
+import requests
+
+from llava.constants import LOGDIR
+
+server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
+moderation_msg = "YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN."
+
+handler = None
+
+
+def build_logger(logger_name, logger_filename):
+    global handler
+
+    formatter = logging.Formatter(
+        fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+
+    # Set the format of root handlers
+    if not logging.getLogger().handlers:
+        logging.basicConfig(level=logging.INFO)
+    logging.getLogger().handlers[0].setFormatter(formatter)
+
+    # Redirect stdout and stderr to loggers
+    stdout_logger = logging.getLogger("stdout")
+    stdout_logger.setLevel(logging.INFO)
+    sl = StreamToLogger(stdout_logger, logging.INFO)
+    sys.stdout = sl
+
+    stderr_logger = logging.getLogger("stderr")
+    stderr_logger.setLevel(logging.ERROR)
+    sl = StreamToLogger(stderr_logger, logging.ERROR)
+    sys.stderr = sl
+
+    # Get logger
+    logger = logging.getLogger(logger_name)
+    logger.setLevel(logging.INFO)
+
+    # Add a file handler for all loggers
+    if handler is None:
+        os.makedirs(LOGDIR, exist_ok=True)
+        filename = os.path.join(LOGDIR, logger_filename)
+        handler = logging.handlers.TimedRotatingFileHandler(
+            filename, when='D', utc=True)
+        handler.setFormatter(formatter)
+
+        for name, item in logging.root.manager.loggerDict.items():
+            if isinstance(item, logging.Logger):
+                item.addHandler(handler)
+
+    return logger
+
+
+class StreamToLogger(object):
+    """
+    Fake file-like stream object that redirects writes to a logger instance.
+    """
+    def __init__(self, logger, log_level=logging.INFO):
+        self.terminal = sys.stdout
+        self.logger = logger
+        self.log_level = log_level
+        self.linebuf = ''
+
+    def __getattr__(self, attr):
+        return getattr(self.terminal, attr)
+
+    def write(self, buf):
+        temp_linebuf = self.linebuf + buf
+        self.linebuf = ''
+        for line in temp_linebuf.splitlines(True):
+            # From the io.TextIOWrapper docs:
+            #   On output, if newline is None, any '\n' characters written
+            #   are translated to the system default line separator.
+            # By default sys.stdout.write() expects '\n' newlines and then
+            # translates them so this is still cross platform.
+            if line[-1] == '\n':
+                self.logger.log(self.log_level, line.rstrip())
+            else:
+                self.linebuf += line
+
+    def flush(self):
+        if self.linebuf != '':
+            self.logger.log(self.log_level, self.linebuf.rstrip())
+        self.linebuf = ''
+
+
+def disable_torch_init():
+    """
+    Disable the redundant torch default initialization to accelerate model creation.
+    """
+    import torch
+    setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
+    setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
+
+
+def violates_moderation(text):
+    """
+    Check whether the text violates OpenAI moderation API.
+    """
+    url = "https://api.openai.com/v1/moderations"
+    headers = {"Content-Type": "application/json",
+               "Authorization": "Bearer " + os.environ["OPENAI_API_KEY"]}
+    text = text.replace("\n", "")
+    data = "{" + '"input": ' + f'"{text}"' + "}"
+    data = data.encode("utf-8")
+    try:
+        ret = requests.post(url, headers=headers, data=data, timeout=5)
+        flagged = ret.json()["results"][0]["flagged"]
+    except requests.exceptions.RequestException as e:
+        flagged = False
+    except KeyError as e:
+        flagged = False
+
+    return flagged
+
+
+def pretty_print_semaphore(semaphore):
+    if semaphore is None:
+        return "None"
+    return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..13d4e01
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,36 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "llava"
+version = "1.1.3"
+description = "Towards GPT-4 like large language and visual assistant."
+readme = "README.md"
+requires-python = ">=3.8"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: Apache Software License",
+]
+dependencies = [
+    "torch==2.0.1", "torchvision==0.15.2",
+    "transformers==4.31.0", "tokenizers>=0.12.1,<0.14", "sentencepiece==0.1.99", "shortuuid",
+    "accelerate==0.21.0", "peft==0.4.0", "bitsandbytes==0.41.0",
+    "pydantic<2,>=1", "markdown2[all]", "numpy", "scikit-learn==1.2.2",
+    "gradio==3.35.2", "gradio_client==0.2.9",
+    "requests", "httpx==0.24.0", "uvicorn", "fastapi",
+    "einops==0.6.1", "einops-exts==0.0.4", "timm==0.6.13",
+]
+
+[project.optional-dependencies]
+train = ["deepspeed==0.9.5", "ninja", "wandb"]
+
+[project.urls]
+"Homepage" = "https://llava-vl.github.io"
+"Bug Tracker" = "https://github.com/haotian-liu/LLaVA/issues"
+
+[tool.setuptools.packages.find]
+exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]
+
+[tool.wheel]
+exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]
diff --git a/retrieval/BDDX_RAG_hybird_vpmatch.json b/retrieval/BDDX_RAG_hybird_vpmatch.json
new file mode 100644
index 0000000..24872dc
--- /dev/null
+++ b/retrieval/BDDX_RAG_hybird_vpmatch.json
@@ -0,0 +1,22796 @@
+{
+    "BDDX_Processed/video/0c13b339-aa4c659e_09681.mp4": [
+        "BDDX_Processed/video/1d2e677c-cb0b0f6d_18419.mp4",
+        "BDDX_Processed/video/0d606092-6cb1ae91_10526.mp4"
+    ],
+    "BDDX_Processed/video/1caed018-666cb4bc_18173.mp4": [
+        "BDDX_Processed/video/06d501fd-a9ffc960_00004.mp4",
+        "BDDX_Processed/video/06727591-79344a7e_03034.mp4"
+    ],
+    "BDDX_Processed/video/0bf2ee26-bcfb308c_09612.mp4": [
+        "BDDX_Processed/video/03f6e688-415b77da_01784.mp4",
+        "BDDX_Processed/video/1e15eaab-f4b4838a_18914.mp4"
+    ],
+    "BDDX_Processed/video/0c83837e-73833b1a_09896.mp4": [
+        "BDDX_Processed/video/07797fc1-3f6aee10_07176.mp4",
+        "BDDX_Processed/video/022ff54c-3cb58ec1_05027.mp4"
+    ],
+    "BDDX_Processed/video/0ebdf257-7f2a2044_11208.mp4": [
+        "BDDX_Processed/video/17a5fe85-887713cb_15813.mp4",
+        "BDDX_Processed/video/20dd6b9c-290dc6e8_20266.mp4"
+    ],
+    "BDDX_Processed/video/1e957b3e-c9d0d292_19170.mp4": [
+        "BDDX_Processed/video/0ce59f85-4636c80c_10178.mp4",
+        "BDDX_Processed/video/0ce59f85-38ec2981_10172.mp4"
+    ],
+    "BDDX_Processed/video/0048f391-8eb40ca6_00943.mp4": [
+        "BDDX_Processed/video/00516b75-7ac91661_04396.mp4",
+        "BDDX_Processed/video/0caabbb0-795b4e67_10018.mp4"
+    ],
+    "BDDX_Processed/video/05f41c11-94e167fc_01867.mp4": [
+        "BDDX_Processed/video/06d065cc-bbf101e6_04156.mp4",
+        "BDDX_Processed/video/04ede4bd-c03f27b1_03871.mp4"
+    ],
+    "BDDX_Processed/video/014070bf-4de0af14_04709.mp4": [
+        "BDDX_Processed/video/0718ab33-bbd7e0ae_01440.mp4",
+        "BDDX_Processed/video/09952d94-b67d51e0_08222.mp4"
+    ],
+    "BDDX_Processed/video/11f16188-3c9f07af_12754.mp4": [
+        "BDDX_Processed/video/1af21ce1-97d4a34b_17498.mp4",
+        "BDDX_Processed/video/0ce5101a-9f7e7fd8_10167.mp4"
+    ],
+    "BDDX_Processed/video/0738f42e-734821e2_01877.mp4": [
+        "BDDX_Processed/video/0738f42e-734821e2_01878.mp4",
+        "BDDX_Processed/video/0634e4d9-cbdf926d_02415.mp4"
+    ],
+    "BDDX_Processed/video/061ae121-ba8c91c2_02158.mp4": [
+        "BDDX_Processed/video/00091078-875c1f73_04265.mp4",
+        "BDDX_Processed/video/0b01ca80-852faffa_09142.mp4"
+    ],
+    "BDDX_Processed/video/04a4137e-4b7d314e_06080.mp4": [
+        "BDDX_Processed/video/0289eba3-1c552656_05179.mp4",
+        "BDDX_Processed/video/04dd75bb-9507acc4_06109.mp4"
+    ],
+    "BDDX_Processed/video/0f6ccc0d-943bb2d8_11402.mp4": [
+        "BDDX_Processed/video/12ac7475-69905215_13030.mp4",
+        "BDDX_Processed/video/11bbe43a-8ff2ea7e_12680.mp4"
+    ],
+    "BDDX_Processed/video/0977bc8b-4dc8eea6_08194.mp4": [
+        "BDDX_Processed/video/192b951a-0d1e2069_16631.mp4",
+        "BDDX_Processed/video/08e3c891-9fc671ad_07945.mp4"
+    ],
+    "BDDX_Processed/video/07184fbb-bb4c67d2_06999.mp4": [
+        "BDDX_Processed/video/02b7b9ee-74fb981c_05272.mp4",
+        "BDDX_Processed/video/009215e2-e67ccfb7_04524.mp4"
+    ],
+    "BDDX_Processed/video/05584a52-730a88df_06287.mp4": [
+        "BDDX_Processed/video/07184fbb-2dc17016_06977.mp4",
+        "BDDX_Processed/video/02b7b9ee-066c81db_05262.mp4"
+    ],
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\ No newline at end of file
diff --git a/retrieval/BDDX_RAG_neg_3.json b/retrieval/BDDX_RAG_neg_3.json
new file mode 100644
index 0000000..49e7671
--- /dev/null
+++ b/retrieval/BDDX_RAG_neg_3.json
@@ -0,0 +1,17697 @@
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+        "BDDX_Processed/video/06cbfcda-32875f1e_06715.mp4",
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+        "BDDX_Processed/video/0315cc1f-d0c40c17_05470.mp4",
+        "BDDX_Processed/video/004071a4-ef4bf541_04366.mp4",
+        "BDDX_Processed/video/025b2ded-11363432_05097.mp4",
+        "BDDX_Processed/video/0714456d-2648a0c9_06942.mp4",
+        "BDDX_Processed/video/066112a8-cdf390dc_06557.mp4",
+        "BDDX_Processed/video/047e7f64-6df28989_06043.mp4",
+        "BDDX_Processed/video/05be3c16-e85868fd_06393.mp4",
+        "BDDX_Processed/video/05a45cbf-ffa8ddde_06373.mp4",
+        "BDDX_Processed/video/04056725-14e1b11d_05787.mp4",
+        "BDDX_Processed/video/05b457b9-6e326f39_06391.mp4",
+        "BDDX_Processed/video/039f729b-913a68c6_05626.mp4",
+        "BDDX_Processed/video/064bad4e-e26c64f8_06529.mp4",
+        "BDDX_Processed/video/02b7b9ee-4cd88e31_05266.mp4",
+        "BDDX_Processed/video/00825605-9dcec7d0_04502.mp4",
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+        "BDDX_Processed/video/06cb522f-f1ff4334_06703.mp4",
+        "BDDX_Processed/video/00810e80-37641274_04500.mp4",
+        "BDDX_Processed/video/05fa6494-51df9bd3_06454.mp4",
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+        "BDDX_Processed/video/04629c39-69d7c08c_06002.mp4",
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+        "BDDX_Processed/video/06ec79fd-7b557fdf_06805.mp4",
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+        "BDDX_Processed/video/07cacc74-5ce6b76a_07294.mp4",
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+        "BDDX_Processed/video/076399f2-fb3e745b_07164.mp4",
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+        "BDDX_Processed/video/07254f2e-32a98be9_07045.mp4",
+        "BDDX_Processed/video/026501c2-dfd80f2d_05146.mp4",
+        "BDDX_Processed/video/0714456d-f947f605_06958.mp4",
+        "BDDX_Processed/video/00810e80-37641274_04501.mp4",
+        "BDDX_Processed/video/0370eddd-81051014_05552.mp4",
+        "BDDX_Processed/video/07919504-3a57c8d3_07206.mp4",
+        "BDDX_Processed/video/0714456d-eb7f5c38_06955.mp4",
+        "BDDX_Processed/video/01440a35-e0b2f4a2_04713.mp4",
+        "BDDX_Processed/video/00d15d58-9197cde5_04581.mp4",
+        "BDDX_Processed/video/0672c229-e9497c41_06604.mp4",
+        "BDDX_Processed/video/04629c39-69d7c08c_06001.mp4",
+        "BDDX_Processed/video/01e9deb7-ed388859_04936.mp4",
+        "BDDX_Processed/video/030a6a6c-fca2979d_05458.mp4",
+        "BDDX_Processed/video/0104a3f0-9294818d_04625.mp4",
+        "BDDX_Processed/video/076399f2-6c2cdfd0_07146.mp4",
+        "BDDX_Processed/video/02a11306-ea3764fe_05243.mp4",
+        "BDDX_Processed/video/05acab14-b78b76c7_06381.mp4",
+        "BDDX_Processed/video/05acab14-b78b76c7_06380.mp4",
+        "BDDX_Test/video/2a011796-d2ea0ed0_25344.mp4",
+        "BDDX_Processed/video/1ddfceee-89178712_18787.mp4",
+        "BDDX_Test/video/2819281f-a885c233_24477.mp4",
+        "BDDX_Processed/video/014e76d7-a0c222bc_03225.mp4",
+        "BDDX_Processed/video/0e05e1d7-51f68c07_10841.mp4",
+        "BDDX_Processed/video/0332729f-b63cda04_04188.mp4",
+        "BDDX_Processed/video/14098efb-5c2b0351_13757.mp4",
+        "BDDX_Processed/video/06fce5b8-efc1a0dd_00442.mp4",
+        "BDDX_Processed/video/0723c4dd-7a1168dc_01621.mp4",
+        "BDDX_Processed/video/2038e4b0-4404bded_19995.mp4",
+        "BDDX_Processed/video/07482c65-a96de461_02129.mp4",
+        "BDDX_Processed/video/0d1fc580-6192f7bd_10366.mp4",
+        "BDDX_Processed/video/10bddcb9-4ee6f404_11936.mp4",
+        "BDDX_Processed/video/076399f2-4c68835b_07133.mp4",
+        "BDDX_Processed/video/10d86362-67be6116_12032.mp4",
+        "BDDX_Processed/video/1f082727-2faaa728_19301.mp4",
+        "BDDX_Processed/video/15e7be3e-64699f10_14755.mp4",
+        "BDDX_Processed/video/0d405525-3d6d563b_10485.mp4",
+        "BDDX_Processed/video/09ac7416-087779c5_08307.mp4",
+        "BDDX_Processed/video/185518c4-5d092d11_16146.mp4",
+        "BDDX_Processed/video/099db658-8aea6bf2_08268.mp4",
+        "BDDX_Processed/video/0d82a02d-60a4654b_10565.mp4",
+        "BDDX_Processed/video/01b4e4b9-fc7a5264_00029.mp4",
+        "BDDX_Processed/video/0798a3a8-c4501e0f_07233.mp4",
+        "BDDX_Processed/video/011a2aa0-677a45ae_02756.mp4",
+        "BDDX_Processed/video/1eb1e178-af806f8b_19210.mp4",
+        "BDDX_Processed/video/0d2ab64b-690f5192_10457.mp4",
+        "BDDX_Processed/video/20ceee16-817c575c_20247.mp4",
+        "BDDX_Processed/video/065992a2-5bef4d51_02679.mp4",
+        "BDDX_Processed/video/0051e391-d32a618e_04400.mp4",
+        "BDDX_Processed/video/055e339a-e98dd764_00722.mp4",
+        "BDDX_Processed/video/031637c4-655be58e_03998.mp4",
+        "BDDX_Processed/video/055e339a-e98dd764_00718.mp4",
+        "BDDX_Processed/video/055e339a-e98dd764_00720.mp4",
+        "BDDX_Processed/video/02b7b9ee-74fb981c_02650.mp4",
+        "BDDX_Processed/video/21efbb21-b31b9f50_20745.mp4",
+        "BDDX_Processed/video/11659c5d-2b5fe71d_12498.mp4",
+        "BDDX_Processed/video/0710389f-9edbc057_00866.mp4",
+        "BDDX_Test/video/29da0a66-be26abfa_25266.mp4",
+        "BDDX_Processed/video/21825b6e-177a1628_20597.mp4",
+        "BDDX_Processed/video/0bf2ee26-bcfb308c_09614.mp4",
+        "BDDX_Processed/video/07cacc74-c8b09a29_03358.mp4",
+        "BDDX_Processed/video/02d478d1-9ce5120d_03180.mp4",
+        "BDDX_Processed/video/1517f29b-bb9faef7_14249.mp4",
+        "BDDX_Processed/video/2250b7e5-07c52bd4_20976.mp4",
+        "BDDX_Processed/video/0193a01e-90028c79_04844.mp4",
+        "BDDX_Processed/video/0ac90ac0-c74d62d6_09062.mp4",
+        "BDDX_Processed/video/1b1e89a6-0e384ba5_17576.mp4",
+        "BDDX_Processed/video/036f3b5f-ee1521de_05550.mp4",
+        "BDDX_Test/video/2903b6c3-f7f5cd55_24851.mp4",
+        "BDDX_Processed/video/12e4568a-1a7a65af_13117.mp4",
+        "BDDX_Processed/video/1e7225d5-64710d9a_19102.mp4",
+        "BDDX_Processed/video/00e02d60-54df99d1_04601.mp4",
+        "BDDX_Processed/video/06b1932a-5757cfcf_03853.mp4",
+        "BDDX_Processed/video/0481e94d-a40bbffb_06053.mp4",
+        "BDDX_Processed/video/156dcd52-aecaa59c_14521.mp4",
+        "BDDX_Processed/video/0cd440c4-31cf6726_10091.mp4",
+        "BDDX_Test/video/281a8cc4-89b5ca4a_24480.mp4",
+        "BDDX_Processed/video/030a6a6c-fca2979d_05460.mp4",
+        "BDDX_Processed/video/0501cda3-5c939ca7_06154.mp4",
+        "BDDX_Processed/video/1a07eecb-5d4efd09_16921.mp4",
+        "BDDX_Processed/video/1d24537d-f291dab4_18385.mp4",
+        "BDDX_Processed/video/0d14d236-8433ea84_10321.mp4",
+        "BDDX_Processed/video/0ffb84f8-30d315be_11625.mp4",
+        "BDDX_Processed/video/03b5e695-11d43622_01291.mp4",
+        "BDDX_Processed/video/1f4b8e7d-a12884e9_19490.mp4",
+        "BDDX_Processed/video/0a248ce2-5111a890_08683.mp4",
+        "BDDX_Processed/video/1e3dcb86-5db183b9_19029.mp4",
+        "BDDX_Processed/video/002b562f-e0ac84fe_00593.mp4",
+        "BDDX_Processed/video/0c5ff0c6-b1853637_09807.mp4",
+        "BDDX_Processed/video/1e272076-566f6711_18947.mp4",
+        "BDDX_Processed/video/17662c87-9476da54_15719.mp4",
+        "BDDX_Processed/video/08a18849-8f2d5d07_07792.mp4",
+        "BDDX_Processed/video/21af7252-7ed16a45_20668.mp4",
+        "BDDX_Processed/video/06db23de-8789538f_06778.mp4",
+        "BDDX_Processed/video/15626dcf-0fb1ebe9_14481.mp4",
+        "BDDX_Processed/video/0786294e-634c696d_02573.mp4",
+        "BDDX_Processed/video/2168a252-c052da7a_20514.mp4",
+        "BDDX_Processed/video/1fe4fb48-53ee13f8_19910.mp4",
+        "BDDX_Processed/video/02a11306-ea3764fe_02360.mp4",
+        "BDDX_Processed/video/1d092676-d1d3ad6b_18329.mp4",
+        "BDDX_Processed/video/06257efc-9aba5724_06495.mp4",
+        "BDDX_Test/video/29a6a14c-aefc8638_25170.mp4",
+        "BDDX_Processed/video/0ee642f9-a93248b5_11260.mp4",
+        "BDDX_Test/video/2c3ba530-eb00441f_26463.mp4",
+        "BDDX_Processed/video/156dcd52-d023f184_14532.mp4",
+        "BDDX_Processed/video/021f503c-012d941f_00933.mp4",
+        "BDDX_Processed/video/1deff035-ab1c94cf_18839.mp4",
+        "BDDX_Test/video/29f8562d-b6a51c75_25319.mp4",
+        "BDDX_Processed/video/1d2552a6-622aa824_18391.mp4",
+        "BDDX_Processed/video/00268999-a4b8e39d_04311.mp4",
+        "BDDX_Processed/video/11ecaf4a-8586f43d_12745.mp4",
+        "BDDX_Test/video/2c50f406-61e19dc8_26496.mp4",
+        "BDDX_Processed/video/081a0da6-a7cdac9c_07446.mp4",
+        "BDDX_Processed/video/013aba02-3fbd03ff_03120.mp4",
+        "BDDX_Processed/video/14d42fac-0708b178_14092.mp4",
+        "BDDX_Processed/video/0ca8aa90-320f3a64_09997.mp4",
+        "BDDX_Processed/video/036f3b5f-ee1521de_00371.mp4",
+        "BDDX_Processed/video/1b05d962-ab15eec8_17541.mp4",
+        "BDDX_Test/video/29b6a36b-3ef7db23_25213.mp4",
+        "BDDX_Processed/video/1f668a20-f15712ac_19594.mp4",
+        "BDDX_Processed/video/06d1c4fe-4f416689_04182.mp4",
+        "BDDX_Processed/video/17fb67eb-ec0ce6f2_16005.mp4",
+        "BDDX_Processed/video/05a93853-0d9f37e7_01251.mp4",
+        "BDDX_Test/video/28e4c2b6-b705f7f3_24803.mp4",
+        "BDDX_Processed/video/0070bc56-8d8cfd82_04469.mp4",
+        "BDDX_Processed/video/17b88142-15616867_15831.mp4",
+        "BDDX_Processed/video/13796ade-6b89dc0d_13485.mp4",
+        "BDDX_Processed/video/160f6a0b-85d4230f_14897.mp4",
+        "BDDX_Test/video/28144f4a-b69dd83a_24470.mp4",
+        "BDDX_Processed/video/034ebec5-b2b76478_00216.mp4",
+        "BDDX_Processed/video/168a2c99-c74274d0_15212.mp4",
+        "BDDX_Processed/video/20b8607e-7550b48f_20231.mp4",
+        "BDDX_Processed/video/07184fbb-1bf770ba_06975.mp4",
+        "BDDX_Processed/video/0bf2ee26-bcfb308c_09612.mp4",
+        "BDDX_Processed/video/21fd3bf5-ef5a1870_20832.mp4",
+        "BDDX_Test/video/29ae5f56-fbf6aa17_25183.mp4",
+        "BDDX_Processed/video/026b9a11-f819c1b9_05150.mp4",
+        "BDDX_Processed/video/13ff2e36-2301a60e_13728.mp4",
+        "BDDX_Processed/video/13ded202-27de718f_13687.mp4",
+        "BDDX_Processed/video/14c2bf46-4c584358_14052.mp4",
+        "BDDX_Processed/video/04095551-efe2622a_05844.mp4",
+        "BDDX_Processed/video/01e9deb7-ed388859_00420.mp4",
+        "BDDX_Processed/video/137a0efc-9b64d089_13536.mp4",
+        "BDDX_Test/video/2a762aa1-f7147908_25686.mp4",
+        "BDDX_Test/video/26e95486-9b5788fa_24231.mp4",
+        "BDDX_Test/video/245ddac9-eaf7f6c3_24000.mp4",
+        "BDDX_Proc
\ No newline at end of file
diff --git a/retrieval/BDDX_RAG_pos_3.json b/retrieval/BDDX_RAG_pos_3.json
new file mode 100644
index 0000000..66c649e
--- /dev/null
+++ b/retrieval/BDDX_RAG_pos_3.json
@@ -0,0 +1,19893 @@
+{
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+        "BDDX_Processed/video/0d606092-6cb1ae91_10526.mp4",
+        "BDDX_Processed/video/0798a3a8-c4501e0f_07231.mp4",
+        "BDDX_Test/video/27b4ec10-4156e86e_24405.mp4",
+        "BDDX_Processed/video/125e39b2-2de3245f_12890.mp4",
+        "BDDX_Processed/video/1ba35b25-fd8f358c_17749.mp4"
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+        "BDDX_Processed/video/0718ab33-bbd7e0ae_01441.mp4",
+        "BDDX_Processed/video/06727591-79344a7e_03034.mp4",
+        "BDDX_Processed/video/06d501fd-a9ffc960_00004.mp4",
+        "BDDX_Processed/video/04488604-72f099d0_02709.mp4",
+        "BDDX_Processed/video/0277379d-ed078767_05160.mp4"
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+        "BDDX_Processed/video/0f89108b-3ab59119_11453.mp4",
+        "BDDX_Processed/video/07184fbb-bb4c67d2_01355.mp4",
+        "BDDX_Processed/video/13796ade-04e91bd7_13457.mp4",
+        "BDDX_Processed/video/0bf8851e-e3f5b31d_09652.mp4"
+    ],
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+        "BDDX_Processed/video/0d301da1-88d39c6e_10467.mp4",
+        "BDDX_Processed/video/017b02cb-477b66fa_04796.mp4",
+        "BDDX_Processed/video/022ff54c-3cb58ec1_05027.mp4",
+        "BDDX_Processed/video/004071a4-ef4bf541_00872.mp4",
+        "BDDX_Processed/video/09dd76ae-f764918c_08532.mp4",
+        "BDDX_Processed/video/09dd76ae-f764918c_08529.mp4"
+    ],
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+        "BDDX_Processed/video/20dd6b9c-290dc6e8_20266.mp4",
+        "BDDX_Processed/video/17a5fe85-887713cb_15813.mp4",
+        "BDDX_Processed/video/17a0445d-6debbef0_15806.mp4",
+        "BDDX_Processed/video/20ff37cb-5d6ab0dc_20325.mp4",
+        "BDDX_Processed/video/213f19cf-982ed08d_20438.mp4",
+        "BDDX_Test/video/28eec171-c33c9205_24822.mp4"
+    ],
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+        "BDDX_Processed/video/0ce59f85-4636c80c_10178.mp4",
+        "BDDX_Processed/video/20ed775f-8236b43e_20289.mp4",
+        "BDDX_Processed/video/0ea102ca-6b91d2d0_11161.mp4",
+        "BDDX_Processed/video/0ce59f85-38ec2981_10172.mp4",
+        "BDDX_Processed/video/0dce14f9-7de59bf3_10705.mp4",
+        "BDDX_Processed/video/0b88820f-96208d1e_09428.mp4"
+    ],
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+        "BDDX_Processed/video/0048f391-8eb40ca6_00941.mp4",
+        "BDDX_Processed/video/0b5c2365-35b41936_09309.mp4",
+        "BDDX_Processed/video/02d478d1-e6811391_03239.mp4",
+        "BDDX_Processed/video/0caabbb0-795b4e67_10018.mp4",
+        "BDDX_Processed/video/07482c65-a96de461_02131.mp4",
+        "BDDX_Processed/video/0f837def-10049e8d_11439.mp4"
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+        "BDDX_Processed/video/00e02d60-54df99d1_02376.mp4",
+        "BDDX_Test/video/25a0f845-ce9d1010_24053.mp4",
+        "BDDX_Processed/video/04ede4bd-c03f27b1_03871.mp4",
+        "BDDX_Processed/video/06d065cc-bbf101e6_04157.mp4",
+        "BDDX_Processed/video/047e7f64-6df28989_03408.mp4",
+        "BDDX_Processed/video/070383db-0363b1c7_00731.mp4"
+    ],
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+        "BDDX_Processed/video/17ebfeeb-52459a76_15935.mp4",
+        "BDDX_Processed/video/12cf075a-ff5e2b67_13095.mp4",
+        "BDDX_Processed/video/0718ab33-bbd7e0ae_01440.mp4",
+        "BDDX_Processed/video/1cc4238c-7b557288_18221.mp4",
+        "BDDX_Processed/video/1c73e4c3-3382a131_18114.mp4",
+        "BDDX_Processed/video/115d9e12-17808939_12461.mp4"
+    ],
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+        "BDDX_Processed/video/1af21ce1-97d4a34b_17498.mp4",
+        "BDDX_Processed/video/0dd60257-0465cd3a_10707.mp4",
+        "BDDX_Processed/video/1ab50992-a88d0328_17313.mp4",
+        "BDDX_Processed/video/0ba933b2-3308d259_09522.mp4",
+        "BDDX_Processed/video/1ab50992-a88d0328_17311.mp4",
+        "BDDX_Processed/video/0ea102ca-b8e8cb10_11164.mp4"
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+        "BDDX_Processed/video/0738f42e-734821e2_01878.mp4",
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+        "BDDX_Processed/video/0d9ca9fa-0b4a0ddf_10602.mp4",
+        "BDDX_Test/video/289af74b-f2c8a214_24700.mp4"
+    ],
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+        "BDDX_Processed/video/067873fa-af75840e_03187.mp4",
+        "BDDX_Processed/video/04dc483a-f595d9f1_03750.mp4",
+        "BDDX_Processed/video/0725b3e1-e2d1e6af_01741.mp4"
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+        "BDDX_Processed/video/1af2e457-53e4b09f_17518.mp4",
+        "BDDX_Processed/video/222a5cd9-ef880b17_20897.mp4",
+        "BDDX_Test/video/2912bfbc-67db449e_24903.mp4",
+        "BDDX_Processed/video/031637c4-9b8207b3_04025.mp4",
+        "BDDX_Processed/video/1f156136-4ae86750_19357.mp4"
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+        "BDDX_Processed/video/176c2785-ca2572b3_15765.mp4",
+        "BDDX_Processed/video/0bb3f3d3-12b80b0b_09538.mp4",
+        "BDDX_Processed/video/1f4b8e7d-82f7e836_19480.mp4",
+        "BDDX_Processed/video/19829784-7e4ee4e9_16736.mp4",
+        "BDDX_Processed/video/143b8968-550cf7aa_13857.mp4",
+        "BDDX_Processed/video/11035230-011a7fb1_12168.mp4"
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+        "BDDX_Processed/video/0be60f15-4bd4b9d4_09589.mp4",
+        "BDDX_Processed/video/15bb4509-6348e545_14666.mp4",
+        "BDDX_Processed/video/03a5a702-81bc7402_0564
\ No newline at end of file
diff --git a/retrieval/BDDX_RAG_tuned_vpmatch_t13.json b/retrieval/BDDX_RAG_tuned_vpmatch_t13.json
new file mode 100644
index 0000000..a0487e9
--- /dev/null
+++ b/retrieval/BDDX_RAG_tuned_vpmatch_t13.json
@@ -0,0 +1,22928 @@
+{
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+        "BDDX_Test/video/2c4fb28e-091459b9_26488.mp4"
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+        "BDDX_Processed/video/084d79a9-60652407_07577.mp4"
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+        "BDDX_Processed/video/1710645f-58d4def6_15567.mp4"
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+        "BDDX_Processed/video/1698c1ee-7a532c1a_15292.mp4"
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+        "BDDX_Processed/video/02ac138c-6bc72d56_02445.mp4"
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+        "BDDX_Processed/video/0070bc56-49cf077c_01381.mp4",
+        "BDDX_Processed/video/06ec79fd-7b557fdf_00182.mp4"
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+        "BDDX_Processed/video/12503fc4-ad0a4d0d_12875.mp4",
+        "BDDX_Processed/video/1d64afb3-30a44ddd_18548.mp4"
+    ],
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+        "BDDX_Processed/video/02d478d1-0328a5bf_05306.mp4",
+        "BDDX_Processed/video/02c2a4d8-94d32b2f_05290.mp4"
+    ],
+    "BDDX_Processed/video/0451ce55-c7cdc6f2_02840.mp4": [
+        "BDDX_Processed/video/0573f031-8ef23cf6_06339.mp4",
+        "BDDX_Processed/video/0573f031-8ef23cf6_00902.mp4"
+    ],
+    "BDDX_Processed/video/05e4d323-5e76c428_01586.mp4": [
+        "BDDX_Processed/video/065992a2-5bef4d51_02678.mp4",
+        "BDDX_Processed/video/0cbdd20d-6ce7dfa3_10060.mp4
\ No newline at end of file
diff --git a/retrieval/BDDX_RAG_visual_vpmatch.json b/retrieval/BDDX_RAG_visual_vpmatch.json
new file mode 100644
index 0000000..90f442e
--- /dev/null
+++ b/retrieval/BDDX_RAG_visual_vpmatch.json
@@ -0,0 +1,22796 @@
+{
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+        "BDDX_Processed/video/0ecc89ce-a5dc232e_11221.mp4"
+    ],
+    "BDDX_Processed/video/04095551-ef1928db_05831.mp4": [
+        "BDDX_Processed/video/04095551-ef1928db_02185.mp4",
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+    ],
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+        "BDDX_Processed/video/1d092676-d1d3ad6b_18329.mp4"
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+    "BDDX_Processed/video/1726919b-9157ae86_15638.mp4": [
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+    ],
+    "BDDX_Processed/video/03f65112-049cca26_01765.mp4": [
+        "BDDX_Processed/video/03f65112-049cca26_05757.mp4",
+        "BDDX_Processed/video/03f65112-049cca26_05756.mp4"
+    ],
+    "BDDX_Processed/video/0d6dc44c-c4ec8d75_10538.mp4": [
+        "BDDX_Processed/video/0d6dc44c-c4ec8d75_10537.mp4",
+        "BDDX_Processed/video/1332ff24-cddc88c7_13306.mp4"
+    ],
+    "BDDX_Processed/video/2295f926-6f3cc5db_21095.mp4": [
+        "BDDX_Processed/video/2295f926-6f3cc5db_21094.mp4",
+        "BDDX_Processed/video/2295f926-6f3cc5db_21096.mp4"
+    ],
+    "BDDX_Processed/video/04076a65-052f6027_01991.mp4": [
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+        "BDDX_Processed/video/16f49e9b-3811c208_15524.mp4"
+    ],
+    "BDDX_Processed/video/17a3ca1c-96973081_15809.mp4": [
+        "BDDX_Processed/video/17a3ca1c-96973081_15808.mp4",
+        "BDDX_Processed/video/17a3ca1c-96973081_15811.mp4"
+    ],
+    "BDDX_Processed/video/014070bf-4de0af14_03132.mp4": [
+        "BDDX_Processed/video/014070bf-4de0af14_04706.mp4",
+        "BDDX_Processed/video/014070bf-4de0af14_04709.mp4"
+    ],
+    "BDDX_Processed/video/1db28ff3-21fdfde6_18691.mp4": [
+        "BDDX_Processed/video/1db28ff3-21fdfde6_18685.mp4",
+        "BDDX_Processed/video/043026cb-cf576c90_02517.mp4"
+    ],
+    "BDDX_Processed/video/14ebe2e9-d83e17d0_14149.mp4": [
+        "BDDX_Processed/video/20e931c5-f21a5238_20274.mp4",
+        "BDDX_Processed/video/20e931c5-f21a5238_20273.mp4"
+    ],
+    "BDDX_Processed/video/047e715f-81e81a28_06037.mp4": [
+        "BDDX_Processed/video/047e715f-81e81a28_03372.mp4",
+        "BDDX_Processed/video/047e715f-81e81a28_03373.mp4"
+    ],
+    "BDDX_Processed/video/19906a63-81e81d0b_16776.mp4": [
+        "BDDX_Processed/video/19906a63-81e81d0b_16775.mp4",
+        "BDDX_Processed/video/090fd6b0-9581c55c_08020.mp4"
+    ],
+    "BDDX_Processed/video/13659c88-36fcca78_13415.mp4": [
+        "BDDX_Processed/video/22245230-0d0fc024_20896.mp4",
+        "BDDX_Processed/video/02f101f6-56ba9106_05387.mp4"
+    ],
+    "BDDX_Processed/video/14540082-1c04c004_13878.mp4": [
+        "BDDX_Processed/video/14540082-1c04c004_13879.mp4",
+        "BDDX_Processed/video/16c045e2-9e88bd5d_15399.mp4"
+    ],
+    "BDDX_Processed/video/11589021-eb71270b_12441.mp4": [
+        "BDDX_Processed/video/11589021-eb71270b_12439.mp4",
+        "BDDX_Processed/video/11589021-ab3116a2_12430.mp4"
+    ],
+    "BDDX_Processed/video/0bf38594-f286ef43_09624.mp4": [
+        "BDDX_Processed/video/0f714f70-ea1a1499_11413.mp4",
+        "BDDX_Processed/video/116d445b-a0ddd4cb_12515.mp4"
+    ],
+    "BDDX_Processed/video/03f0f5e6-ab7a74ed_01755.mp4": [
+        "BDDX_Processed/video/03f0f5e6-ab7a74ed_05755.mp4",
+        "BDDX_Processed/video/1d2090ea-98b3b88d_18356.mp4"
+    ],
+    "BDDX_Processed/video/0cbdd20d-6ce7dfa3_10062.mp4": [
+        "BDDX_Processed/video/0cbdd20d-6ce7dfa3_10061.mp4",
+        "BDDX_Processed/video/107bc4d0-a430526f_11847.mp4"
+    ],
+    "BDDX_Processed/video/0569fe1c-ffe5f96c_06327.mp4": [
+        "BDDX_Processed/video/0569fe1c-ffe5f96c_00838.mp4",
+        "BDDX_Processed/video/01128d0a-031e0a61_04645.mp4"
+    ],
+    "BDDX_Processed/video/131d44d3-336c939d_13251.mp4": [
+        "BDDX_Processed/video/131d44d3-336c939d_13252.mp4",
+        "BDDX_Processed/video/131d44d3-336c939d_13250.mp4"
+    ],
+    "BDDX_Processed/video/06595ed1-6aa7c911_06535.mp4": [
+        "BDDX_Processed/video/06595ed1-6aa7c911_02627.mp4",
+        "BDDX_Processed/video/002ab96a-a96704f9_04317.mp4"
+    ],
+    "BDDX_Processed/video/0e4acdbb-eccd8c04_10932.mp4": [
+        "BDDX_Processed/video/0e4acdbb-eccd8c04_10933.mp4",
+        "BDDX_Processed/video/1e6a6965-d2c44395_19091.mp4"
+    ],
+    "BDDX_Processed/video/11e60919-a44a764e_12733.mp4": [
+        "BDDX_Processed/video/11e60919-a44a764e_12732.mp4",
+        "BDDX_Processed/video/07184fbb-2e67b768_06978.mp4"
+    ],
+    "BDDX_Processed/video/039f729b-17136fb4_00740.mp4": [
+        "BDDX_Processed/video/039f729b-17136fb4_00740.mp4",
+        "BDDX_Processed/video/039f729b-17136fb4_00745.mp4"
+    ],
+    "BDDX_Processed/video/0a350edb-6f5c7ef8_08695.mp4": [
+        "BDDX_Processed/video/0a350edb-6f5c7ef8_08694.mp4",
+        "BDDX_Processed/video/0a350edb-6f5c7ef8_08697.mp4"
+    ],
+    "BDDX_Processed/video/02346f91-aaa4e437_05047.mp4": [
+        "BDDX_Processed/video/02346f91-aaa4e437_01179.mp4",
+        "BDDX_Processed/video/02346f91-aaa4e437_05048.mp4"
+    ],
+    "BDDX_Processed/video/101002a3-af1f7874_11697.mp4": [
+        "BDDX_Processed/video/101002a3-af1f7874_11695.mp4",
+        "BDDX_Processed/video/101002a3-af1f7874_11696.mp4"
+    ],
+    "BDDX_Processed/video/0f442be6-db4257a7_11370.mp4": [
+        "BDDX_Processed/video/0f442be6-db4257a7_11371.mp4",
+        "BDDX_Processed/video/0d207cff-4d922795_10391.mp4"
+    ],
+    "BDDX_Processed/video/0711ab13-e486593d_00949.mp4": [
+        "BDDX_Processed/video/0711ab13-e486593d_06935.mp4",
+        "BDDX_Processed/video/0c91b387-10ef9e01_09926.mp4"
+    ],
+    "BDDX_Processed/video/0738f42e-fe0d895e_07075.mp4": [
+        "BDDX_Processed/video/0738f42e-fe0d895e_01886.mp4",
+        "BDDX_Processed/video/0738f42e-fe0d895e_07079.mp4"
+    ],
+    "BDDX_Processed/video/1edd9c50-b4b5ad61_19265.mp4": [
+        "BDDX_Processed/video/1edd9c50-b4b5ad61_19266.mp4",
+        "BDDX_Processed/video/031637c4-b28f871a_05486.mp4"
+    ],
+    "BDDX_Processed/video/1cd33ceb-0c159ee7_18240.mp4": [
+        "BDDX_Processed/video/21de859a-202af88a_20695.mp4",
+        "BDDX_Processed/video/1cd33ceb-0c159ee7_18241.mp4"
+    ],
+    "BDDX_Processed/video/0b669851-ebc3d3e9_09355.mp4": [
+        "BDDX_Processed/video/0b669851-ebc3d3e9_09356.mp4",
+        "BDDX_Processed/video/04488604-7db829ee_05929.mp4"
+    ],
+    "BDDX_Processed/video/066557f7-f2fe2ec0_02864.mp4": [
+        "BDDX_Processed/video/066557f7-f2fe2ec0_06566.mp4",
+        "BDDX_Processed/video/066557f7-f2fe2ec0_02863.mp4"
+    ],
+    "BDDX_Processed/video/21fba933-046e68f3_20805.mp4": [
+        "BDDX_Processed/video/13620abf-62b6685f_13404.mp4",
+        "BDDX_Processed/video/13ded202-25aa2c44_13684.mp4"
+    ],
+    "BDDX_Processed/video/1520a162-0716ac18_14279.mp4": [
+        "BDDX_Processed/video/1520a162-0716ac18_14278.mp4",
+        "BDDX_Processed/video/190aeadf-6966166d_16525.mp4"
+    ],
+    "BDDX_Processed/video/00091078-c1d32eea_00200.mp4": [
+        "BDDX_Processed/video/00091078-c1d32eea_04275.mp4",
+        "BDDX_Processed/video/00091078-c1d32eea_04276.mp4"
+    ],
+    "BDDX_Processed/video/132f36a5-3e7891ae_13294.mp4": [
+        "BDDX_Processed/video/132f36a5-3e7891ae_13295.mp4",
+        "BDDX_Processed/video/02d478d1-42ca3cfb_02999.mp4"
+    ],
+    "BDDX_Processed/video/0a75801e-05155014_08788.mp4": [
+        "BDDX_Processed/video/0a75801e-05155014_08787.mp4",
+        "BDDX_Processed/video/18fc7b57-a2e51713_16500.mp4"
+    ],
+    "BDDX_Processed/video/1008a17e-5dfe3438_11645.mp4": [
+        "BDDX_Processed/video/1008a17e-5dfe3438_11647.mp4",
+        "BDDX_Processed/video/1008a17e-5dfe3438_11646.mp4"
+    ],
+    "BDDX_Processed/video/014b8fc3-532a0660_04722.mp4": [
+        "BDDX_Processed/video/014b8fc3-532a0660_03200.mp4",
+        "BDDX_Processed/video/014b8fc3-532a0660_03199.mp4"
+    ],
+    "BDDX_Processed/video/09b80f74-c763ee9b_08471.mp4": [
+        "BDDX_Processed/video/09b80f74-c763ee9b_08470.mp4",
+        "BDDX_Processed/video/09b80f74-c763ee9b_08477.mp4"
+    ],
+    "BDDX_Processed/video/18b6d1e2-caa13b16_16323.mp4": [
+        "BDDX_Processed/video/18b6d1e2-caa13b16_16324.mp4",
+        "BDDX_Processed/video/1b892748-85c80a37_17732.mp4"
+    ],
+    "BDDX_Processed/video/0ea059ef-ffb43396_11147.mp4": [
+        "BDDX_Processed/video/0ea059ef-ffb43396_11146.mp4",
+        "BDDX_Processed/
\ No newline at end of file
diff --git a/retrieval/bddx_vpath_info_match.json b/retrieval/bddx_vpath_info_match.json
new file mode 100644
index 0000000..f7f3aef
--- /dev/null
+++ b/retrieval/bddx_vpath_info_match.json
@@ -0,0 +1,1878 @@
+{
+    "BDDX_Processed/video/0c13b339-aa4c659e_09681.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.57, 9.59, 9.6, 9.35, 8.72, 8.09, 7.07]\n Curvature: [0.0, 0.13, 0.25, 0.22, -0.05, -0.31, -0.24]\n Acceleration: [0.43, -0.06, -0.51, -1.04, -1.63, -2.23, -2.29]\n Course: [0.0, 0.0, 0.0, 0.61, 0.61, 0.61, -0.14]\nWhat is the action of ego car?\nAssistant: The car pulls over to the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: to go around a stopped van.\n\n",
+    "BDDX_Processed/video/1caed018-666cb4bc_18173.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.33, 8.38, 8.34, 8.2, 7.43, 6.37, 4.89]\n Curvature: [-0.04, -0.13, -0.09, 0.11, 0.06, -0.0, -0.0]\n Acceleration: [0.75, 0.22, -0.28, -0.75, -1.35, -1.86, -2.05]\n Course: [0.0, -0.51, 0.21, 0.21, 0.0, -5.14, -5.14]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear.\n\n",
+    "BDDX_Processed/video/0bf2ee26-bcfb308c_09612.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.82, 9.21, 9.87, 10.88, 11.13, 11.33, 9.78]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.32, 0.49, 0.29, 0.2, 0.14, -0.46, -0.62]\n Course: [0.0, 0.0, 0.13, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving forward at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady pace.\n\n",
+    "BDDX_Processed/video/0c83837e-73833b1a_09896.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.7, 4.76, 5.54, 5.26, 5.78, 6.92, 6.44]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.79, 0.61, -0.18, 0.04, 0.2, 0.05, -0.32]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.1]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because its lane is free of nearby traffic.\n\n",
+    "BDDX_Processed/video/0ebdf257-7f2a2044_11208.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: at a red light behind other cars.\n\n",
+    "BDDX_Processed/video/1e957b3e-c9d0d292_19170.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.58, 8.06, 6.73, 5.87, 4.57, 2.84, 1.25]\n Curvature: [0.02, -0.04, -0.0, 0.0, -0.01, 0.02, 0.04]\n Acceleration: [-2.33, -2.06, -1.25, -1.25, -1.63, -1.81, -1.44]\n Course: [0.0, 0.0, -0.25, 0.4, 0.0, -0.16, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light at the upcoming intersection is red.\n\n",
+    "BDDX_Processed/video/0048f391-8eb40ca6_00943.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.06, 5.36, 5.66, 5.96, 6.21, 6.34, 6.46]\n Curvature: [0.01, -0.0, -0.01, -0.01, -0.02, -0.01, -0.01]\n Acceleration: [1.42, 1.26, 1.1, 0.94, 0.79, 0.57, 0.35]\n Course: [0.0, 0.02, 0.02, 0.02, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear ahead of it.\n\n",
+    "BDDX_Processed/video/05f41c11-94e167fc_01867.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.38, 4.19, 3.99, 3.8, 3.67, 3.98, 4.3]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.83, -1.29, -0.76, -0.23, 0.27, 0.57, 0.87]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: The car is moving through an intersection\n\n",
+    "BDDX_Processed/video/014070bf-4de0af14_04709.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [23.07, 23.36, 23.57, 23.67, 23.67, 23.58, 23.6]\n Curvature: [-0.01, -0.0, 0.01, 0.02, 0.02, 0.01, 0.01]\n Acceleration: [0.19, 0.27, 0.2, 0.06, -0.05, -0.03, 0.13]\n Course: [0.0, -0.14, -0.27, 0.0, -0.27, -0.54, -0.27]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's moving to the right lane.\n\n",
+    "BDDX_Processed/video/11f16188-3c9f07af_12754.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [25.18, 23.95, 24.15, 24.22, 24.43, 25.49, 26.59]\n Curvature: [0.0, -0.02, 0.0, -0.0, 0.01, 0.03, -0.0]\n Acceleration: [0.08, -0.23, 0.18, -0.26, 0.12, 0.13, 0.26]\n Course: [0.0, 4.52, 0.41, -1.73, -2.04, -4.26, -0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no cars in its lane ahead and the light is green.\n\n",
+    "BDDX_Processed/video/0738f42e-734821e2_01877.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.23, 3.24, 3.24, 3.25, 3.3, 3.61, 3.88]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-0.67, -0.33, -0.04, 0.3, 0.6, 0.64, 0.68]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing abruptly\n\nHuman: Why does the ego car doing this?\nAssistant: The car is attempting to avoid a collision with a taxi merging into the lane.\n\n",
+    "BDDX_Processed/video/061ae121-ba8c91c2_02158.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.04, 0.12, 0.2, 0.24]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.04, 0.08, 0.11, 0.13, 0.15, 0.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic in front of it is stopped.\n\n",
+    "BDDX_Processed/video/04a4137e-4b7d314e_06080.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.36, 11.62, 12.16, 15.55, 16.23, 16.25, 13.14]\n Curvature: [-0.01, 0.0, 0.01, -0.0, -0.01, 0.01, -0.0]\n Acceleration: [1.31, 0.41, 1.97, 1.61, 0.3, -1.32, -4.45]\n Course: [0.0, -0.09, -0.11, 0.1, 0.0, 0.0, 0.2]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0f6ccc0d-943bb2d8_11402.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.01, 0.07, 0.03]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a car in front stopped at a red light.\n\n",
+    "BDDX_Processed/video/0977bc8b-4dc8eea6_08194.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.35, 0.36, 0.07, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.08, -0.08, -0.08, -0.08, -0.08, -0.08, -0.08]\n Acceleration: [0.01, -0.16, -0.18, -0.05, 0.0, 0.0, 0.06]\n Course: [0.0, 0.08, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is waiting\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/07184fbb-bb4c67d2_06999.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.11, 2.57, 4.08, 5.47, 7.1, 8.46, 9.55]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.6, 2.01, 2.28, 2.32, 2.16, 1.91, 1.51]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins driving down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/05584a52-730a88df_06287.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.09, 10.98, 10.89, 11.11, 11.14, 9.61, 6.07]\n Curvature: [-0.0, -0.01, -0.02, -0.03, -0.04, -0.03, -0.01]\n Acceleration: [-0.46, -0.07, 0.02, 0.17, -0.31, -2.12, -2.92]\n Course: [0.0, 0.41, 0.89, 1.12, 1.37, 1.82, 0.44]\nWhat is the action of ego car?\nAssistant: The car merges right then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/007c01ea-63aa326c_01532.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.38, 18.58, 18.73, 18.88, 18.05, 16.8, 17.08]\n Curvature: [0.0, -0.0, -0.01, -0.01, -0.01, -0.01, -0.01]\n Acceleration: [-0.04, -0.25, -0.09, -0.02, -0.13, 0.09, 0.18]\n Course: [0.0, -0.09, 0.46, 2.1, 0.73, 2.81, 5.83]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/02a11306-ea3764fe_02362.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.15, 5.76, 5.51, 5.52, 5.32, 5.08, 4.78]\n Curvature: [-0.0, 0.05, 0.07, 0.01, -0.03, -0.04, 0.02]\n Acceleration: [-0.63, -0.44, -0.28, -0.18, -0.3, -0.13, 0.78]\n Course: [0.0, 0.05, -0.23, -0.19, 0.0, 0.47, 0.59]\nWhat is the action of ego car?\nAssistant: The car continues to drive slowly\n\nHuman: Why does the ego car doing this?\nAssistant: since traffic is still moving slowly.\n\n",
+    "BDDX_Processed/video/1b883059-913ecef4_17702.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.01, 12.99, 12.97, 12.95, 12.92, 12.85, 12.78]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.16, -0.17, -0.17, -0.18, -0.18, -0.14, -0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car passes through an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green\n\n",
+    "BDDX_Processed/video/11060687-27c191a6_12187.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 1.0, 1.93, 2.8, 3.65, 5.01, 6.29]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.96, 1.39, 1.78, 1.95, 2.09, 2.14, 2.14]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned green.\n\n",
+    "BDDX_Processed/video/041fb602-19f8495d_05865.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.63, 14.15, 15.12, 15.02, 14.57, 14.24, 13.95]\n Curvature: [0.0, -0.01, -0.01, -0.14, -0.01, 0.0, 0.0]\n Acceleration: [-0.14, 0.05, 0.3, -0.04, -0.08, -0.49, 0.18]\n Course: [0.0, 0.0, 0.89, 1.06, 0.78, 0.26, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped in front.\n\n",
+    "BDDX_Processed/video/2173474d-f99d23ef_20534.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.12, 1.76, 2.5, 3.21, 3.77, 3.92, 5.27]\n Curvature: [-0.17, 0.01, 0.05, 0.03, 0.0, 0.0, -0.0]\n Acceleration: [0.75, 0.88, 0.91, 0.77, 0.56, 1.04, 1.52]\n Course: [0.0, 2.51, 0.0, -0.97, -0.48, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates down the street\n\nHuman: Why does the ego car doing this?\nAssistant: since the light turns green.\n\n",
+    "BDDX_Processed/video/1cd5d614-35d63896_18248.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.28, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped.\n\n",
+    "BDDX_Processed/video/0bfc0ff9-1fdb0036_09660.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.58, 12.23, 11.0, 9.66, 8.33, 7.0, 5.92]\n Curvature: [-0.1, -0.02, 0.05, 0.08, 0.03, -0.02, -0.08]\n Acceleration: [-2.16, -2.66, -3.11, -3.36, -3.26, -3.17, -2.87]\n Course: [0.0, -0.18, -0.12, 0.0, 0.0, 0.0, -0.02]\nWhat is the action of ego car?\nAssistant: The car brakes\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign ahead.\n\n",
+    "BDDX_Processed/video/09ac7416-7302f662_08343.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.96, 9.92, 11.09, 11.95, 12.43, 12.21, 10.99]\n Curvature: [-0.21, -0.02, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.3, 1.33, 0.75, 0.53, 0.04, -0.34, -0.97]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no nearby vehicles in front of it.\n\n",
+    "BDDX_Processed/video/07919504-00f3c313_02696.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.6, 15.74, 14.96, 14.06, 13.08, 12.11, 11.14]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-1.92, -2.08, -2.23, -2.37, -2.48, -2.58, -2.49]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a car doing a u-turn ahead\n\n",
+    "BDDX_Processed/video/0573e933-a8b4cf7d_00881.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.83, 15.74, 15.51, 14.72, 14.21, 12.95, 12.14]\n Curvature: [0.0, 0.0, -0.01, 0.0, 0.0, -0.0, -0.01]\n Acceleration: [-0.08, -0.04, -0.24, -0.31, -0.14, -0.59, -0.08]\n Course: [0.0, -0.38, 0.0, 0.08, -0.21, 0.0, 0.42]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front of it is driving at a normal speed.\n\n",
+    "BDDX_Processed/video/01e6281b-5be21c14_00333.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.63, 6.9, 8.11, 8.88, 10.51, 11.5, 11.52]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.8, 0.82, 0.71, 0.77, 1.05, 0.28, -0.17]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.04, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving at a normal rate of speed\n\nHuman: Why does the ego car doing this?\nAssistant: as there is no traffic.\n\n",
+    "BDDX_Processed/video/21efbb21-7a7245da_20738.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.85, 4.25, 4.21, 4.85, 4.31, 3.52, 2.63]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.51, -0.16, 0.02, -0.03, -0.72, -1.31, -1.49]\n Course: [0.0, 0.0, -0.06, -0.06, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The vehicle moved into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it moved around the braking vehicle.\n\n",
+    "BDDX_Processed/video/1c5c6a4f-65279a68_18036.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.35, 3.62, 3.87, 4.31, 4.93, 5.6, 6.22]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.68, 0.88, 1.06, 1.25, 1.41, 1.58, 1.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green\n\n",
+    "BDDX_Processed/video/17f3b81c-f82d47da_15993.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.76, 4.74, 4.73, 4.87, 5.16, 5.25, 5.28]\n Curvature: [-0.0, 0.0, 0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [-0.13, -0.04, 0.11, 0.23, 0.25, 0.09, -0.06]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving forward steadily\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is continuing to slowly move forward\n\n",
+    "BDDX_Processed/video/149c3357-7fda9297_14010.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.82, 10.73, 10.64, 10.67, 10.7, 10.68, 10.65]\n Curvature: [0.0, 0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.63, 0.29, -0.06, -0.02, 0.0, -0.07, -0.19]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving steadily.\n\n",
+    "BDDX_Processed/video/1b892748-85c80a37_17731.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.54, 7.92, 7.62, 7.17, 7.31, 7.92, 7.83]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.55, 0.04, -0.29, -0.16, 0.35, 0.13, -0.03]\n Course: [0.0, 0.0, -0.03, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car proceeds down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green and traffic is moving normally.\n\n",
+    "BDDX_Processed/video/08b77e05-932ca5cf_07857.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.04, 9.34, 9.6, 9.9, 10.16, 10.23, 10.31]\n Curvature: [-0.02, -0.01, -0.01, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.84, 0.8, 0.77, 0.73, 0.7, 0.62, 0.55]\n Course: [0.0, 0.17, 0.17, 0.17, 0.08, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in front of it speeds up.\n\n",
+    "BDDX_Processed/video/1e8bcf70-c2275084_19162.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.76, 0.33, 0.65, 0.07, 0.47, 0.32, 0.52]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [-0.36, 0.05, -0.07, -0.12, 0.23, -0.16, 0.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is reversing on the narrow road\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a car blocking the path forward.\n\n",
+    "BDDX_Processed/video/17fb67eb-28a14e3e_15997.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 2.93, 6.24, 8.93, 9.88, 8.87, 6.58]\n Curvature: [0.0, 0.0, 0.02, 0.01, 0.0, 0.04, 0.02]\n Acceleration: [-0.27, 1.87, 1.2, 1.17, -0.45, -0.91, -1.86]\n Course: [0.0, 0.0, -1.69, -2.01, -0.42, -2.89, -0.64]\nWhat is the action of ego car?\nAssistant: The car continues forward toward the exit\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear ahead.\n\n",
+    "BDDX_Processed/video/030a6a6c-fca2979d_05459.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.04, 3.33, 3.86, 4.7, 5.22, 5.67, 6.04]\n Curvature: [-0.08, -0.07, -0.08, -0.11, -0.12, -0.12, -0.12]\n Acceleration: [0.04, 0.58, 0.92, 1.01, 0.82, 0.7, 0.81]\n Course: [0.0, 2.1, 2.27, 2.27, 2.84, 3.01, 3.01]\nWhat is the action of ego car?\nAssistant: The car turns right then merges left\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is seen and the car can safely move onward.\n\n",
+    "BDDX_Processed/video/14ac7c59-d904d83c_14024.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.15, 0.11, 0.07, 0.03, 0.0, 0.0, 0.0]\n Curvature: [-0.15, -0.15, -0.15, -0.15, -0.15, -0.15, -0.15]\n Acceleration: [-0.15, -0.13, -0.11, -0.09, -0.07, -0.05, -0.03]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is rolling forward a couple of feet then comes to a complete stop.\n\nHuman: Why does the ego car doing this?\nAssistant: Because traffic in front of it and around it is at a complete stop.\n\n",
+    "BDDX_Processed/video/167f4689-bd993700_15194.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 3.17, 5.12, 6.84, 8.68, 9.75, 10.73]\n Curvature: [0.05, -0.01, 0.03, -0.01, -0.01, -0.01, -0.0]\n Acceleration: [0.81, 0.89, 0.85, 0.72, 0.52, 0.4, 0.33]\n Course: [0.0, -0.18, 0.0, -0.05, 0.67, 0.35, -0.22]\nWhat is the action of ego car?\nAssistant: The car starts driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is green\n\n",
+    "BDDX_Processed/video/1dcbf95d-5b276cdc_18726.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.15, 19.84, 20.01, 20.05, 20.14, 20.11, 19.94]\n Curvature: [-0.0, -0.0, -0.02, -0.04, -0.04, -0.01, -0.0]\n Acceleration: [0.84, 0.51, 0.22, 0.06, 0.04, -0.12, -0.16]\n Course: [0.0, -0.0, 0.0, 2.42, 0.81, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is merging into the lane to its left\n\nHuman: Why does the ego car doing this?\nAssistant: because its lane is ending and there is no nearby traffic in the lane to its left.\n\n",
+    "BDDX_Processed/video/0e84b431-75df27b5_11067.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.01, 2.33, 2.66, 2.98, 3.26, 3.1, 2.94]\n Curvature: [-0.0, -0.01, -0.01, -0.01, -0.02, -0.03, -0.04]\n Acceleration: [0.77, 0.66, 0.54, 0.43, 0.33, -0.1, -0.52]\n Course: [0.0, -0.07, -0.07, -0.07, -0.03, -0.12, -0.12]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as the car in front of it begins moving more quickly.\n\n",
+    "BDDX_Processed/video/0aa4bc86-1e9bb591_08875.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03]\n Course: [0.0, -0.07, 0.0, 0.0, 0.07, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the vehicle in front of it is stopped.\n\n",
+    "BDDX_Processed/video/11318349-98f9b811_12325.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.13, 7.38, 7.56, 7.79, 8.05, 8.23, 8.39]\n Curvature: [0.0, 0.03, 0.03, 0.0, -0.02, -0.02, 0.0]\n Acceleration: [0.31, 0.27, 0.28, 0.3, 0.29, 0.23, 0.08]\n Course: [0.0, 0.36, 0.07, 0.0, -0.04, -0.07, 0.42]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/0229e497-f4eed706_00963.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.49, 12.48, 13.16, 13.71, 13.32, 13.03, 12.88]\n Curvature: [0.0, -0.02, -0.0, -0.01, 0.0, -0.01, -0.01]\n Acceleration: [0.38, 0.35, 0.29, 0.07, -0.23, -0.07, -0.08]\n Course: [0.0, -0.4, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car remains in the same lane and goes the same speed.\n\nHuman: Why does the ego car doing this?\nAssistant: Because the car is merging onto the highway with no traffic ahead of it.\n\n",
+    "BDDX_Processed/video/05266828-79a6ed22_00083.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.41, 0.58, 1.0, 1.44, 1.59, 1.29, 0.91]\n Curvature: [0.0, 0.2, 0.14, 0.01, -0.18, -0.09, 0.11]\n Acceleration: [0.19, 0.32, 0.46, 0.32, -0.07, -0.35, -0.24]\n Course: [0.0, 0.21, -0.16, -0.16, -0.14, 0.16, -0.33]\nWhat is the action of ego car?\nAssistant: The car moves slowly forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is going slowly.\n\n",
+    "BDDX_Processed/video/1f7ba5ab-fc660dc7_19667.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.21, 1.47, 5.32, 8.95, 10.72, 11.18, 11.46]\n Curvature: [-0.97, 0.16, 0.04, -0.01, -0.0, -0.0, -0.04]\n Acceleration: [0.19, 1.34, 1.71, 1.29, 0.45, 0.06, 0.13]\n Course: [0.0, -0.73, 0.73, -0.2, 0.6, 0.96, -1.08]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: Because the light turned green and the traffic ahead is now moving.\n\n",
+    "BDDX_Processed/video/22a5625a-e1af86c8_21108.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.91, 3.92, 3.92, 3.77, 3.61, 3.38, 3.17]\n Curvature: [-0.03, -0.03, -0.04, -0.02, -0.01, -0.01, -0.01]\n Acceleration: [0.33, 0.09, -0.14, -0.26, -0.37, -0.37, -0.37]\n Course: [0.0, 0.26, 0.21, 0.11, 0.08, 0.05, 0.07]\nWhat is the action of ego car?\nAssistant: The car accelerates slowly before slowing down to a gradually stop.\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic ahead is heavy and moving very slowly.\n\n",
+    "BDDX_Processed/video/01b0505f-5f564e84_00005.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.6, 0.65, 0.51, 0.5, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.01, 0.01, -0.1, -0.16, -0.06, 0.0, 0.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is at an intersection with a red light.\n\n",
+    "BDDX_Processed/video/010b0600-47a9e3e0_04638.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.93, 1.59, 1.06, 1.95, 3.53, 4.52, 6.12]\n Curvature: [1.67, 0.35, -0.09, -0.09, -0.23, 0.3, -0.05]\n Acceleration: [-0.67, -1.4, 0.13, 1.2, 1.23, 1.28, 0.88]\n Course: [0.0, -18.62, -44.08, 15.13, 22.49, 0.83, 2.19]\nWhat is the action of ego car?\nAssistant: The car is doing a u-turn in the oncoming lane\n\nHuman: Why does the ego car doing this?\nAssistant: to change directions while traffic is clear\n\n",
+    "BDDX_Processed/video/0723dfcb-b54e0783_07044.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.06, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked on the side of the road.\n\n",
+    "BDDX_Processed/video/18f392ee-eeb8397f_16451.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.55, 4.11, 4.66, 5.22, 5.76, 6.2, 6.64]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.59, 1.67, 1.76, 1.85, 1.75, 0.39, -0.97]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car partially moves into the turn lane\n\nHuman: Why does the ego car doing this?\nAssistant: to go around the turning car.\n\n",
+    "BDDX_Processed/video/04629c39-faefdb18_06006.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.05, 6.24, 7.11, 7.51, 7.37, 7.05, 6.06]\n Curvature: [-0.06, 0.04, 0.01, -0.01, 0.0, 0.02, -0.01]\n Acceleration: [2.03, 1.1, 0.36, 0.19, -0.25, -0.41, -0.59]\n Course: [0.0, 0.45, -0.39, 0.53, 0.12, -0.42, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates and drives down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the street is clear.\n\n",
+    "BDDX_Processed/video/02f208e7-904032da_05399.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.38, 2.73, 5.64, 6.48, 0.26, 2.73]\n Curvature: [-1.1, -0.35, 0.01, -0.0, -0.0, -0.92, -0.01]\n Acceleration: [0.0, 0.58, 1.2, 1.02, -1.75, -0.84, 2.11]\n Course: [0.0, 0.0, 0.0, 0.0, 0.68, -1.32, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then merges right\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/028b5d16-be6486a6_05198.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [25.69, 25.89, 25.78, 25.54, 25.11, 23.96, 25.25]\n Curvature: [0.0, -0.03, -0.0, -0.01, -0.01, -0.02, 0.01]\n Acceleration: [0.03, -0.0, -0.03, -0.14, -0.2, 0.2, -0.56]\n Course: [0.0, 0.98, 0.68, 1.03, 0.84, 4.21, -1.89]\nWhat is the action of ego car?\nAssistant: The car moves forward then merges right\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/02fc82b3-279906a9_05416.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.99, 8.95, 8.9, 8.86, 8.8, 8.65, 8.5]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.07, -0.04, -0.16, -0.27, -0.41, -0.73, -1.05]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating and drifting to the left onto the opposing side of the street\n\nHuman: Why does the ego car doing this?\nAssistant: Because there is a van parked in the middle of the street and partially blocking the lane going straight.\n\n",
+    "BDDX_Processed/video/100a3dd7-d21b91d2_11674.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.17, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [1.87, 1.87, 1.87, 1.88, 1.88, 1.89, 1.89]\n Acceleration: [-0.17, -0.06, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as the light ahead is red.\n\n",
+    "BDDX_Processed/video/00d15d58-9197cde5_02254.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.97, 7.7, 6.58, 5.31, 4.14, 3.71, 3.34]\n Curvature: [-0.09, -0.07, -0.04, -0.02, 0.0, 0.0, 0.0]\n Acceleration: [-2.57, -2.73, -2.88, -3.04, -3.12, -2.56, -2.08]\n Course: [0.0, -0.01, -0.0, -0.01, -0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The vehicle is braking\n\nHuman: Why does the ego car doing this?\nAssistant: because cars in front of it are stopped.\n\n",
+    "BDDX_Processed/video/099ea5ca-477562fd_08275.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: while waiting for a man on a bicycle to ride past\n\n",
+    "BDDX_Processed/video/064bad4e-e26c64f8_06530.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.33, 0.53, 0.55, 0.24, 0.0, 0.0]\n Curvature: [-0.69, -1.2, -1.97, -2.98, -3.14, -3.15, -3.15]\n Acceleration: [0.26, 0.27, 0.12, -0.21, -0.27, -0.22, -0.04]\n Course: [0.0, 0.09, -0.06, -0.05, 0.0, 0.05, 0.06]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped in front.\n\n",
+    "BDDX_Processed/video/03fd9aa1-4be42e2e_05776.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.42, 0.2, 0.01, 0.1, 0.18, 0.08, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, -0.06, -0.11, -0.05, -0.01, -0.06, -0.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the road conditions are slick\n\n",
+    "BDDX_Processed/video/038db692-9c25743b_05589.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.4, 4.91, 5.37, 5.81, 6.12, 6.43, 6.67]\n Curvature: [0.08, 0.09, 0.1, 0.13, 0.21, 0.29, 0.2]\n Acceleration: [1.36, 1.24, 1.12, 1.0, 0.86, 0.73, 0.71]\n Course: [0.0, -0.71, -0.48, -1.9, -1.9, -1.9, -2.46]\nWhat is the action of ego car?\nAssistant: The car begins to turn left\n\nHuman: Why does the ego car doing this?\nAssistant: while in the intersection\n\n",
+    "BDDX_Processed/video/160a76cc-06a8b72c_14876.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.2, 13.57, 13.86, 13.08, 12.31, 11.56, 10.66]\n Curvature: [0.0, 0.0, 0.0, 0.01, 0.02, 0.01, -0.0]\n Acceleration: [0.97, 0.26, -0.43, -0.99, -1.53, -1.91, -2.26]\n Course: [0.0, 0.0, -0.08, -0.33, -0.16, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it's getting ready to turn.\n\n",
+    "BDDX_Processed/video/04ff2dcc-d4f48c8a_03945.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.6, 2.77, 2.05, 1.22, 0.48, 0.43, 0.38]\n Curvature: [0.01, -0.03, -0.06, -0.1, -0.13, -0.14, -0.15]\n Acceleration: [-3.55, -3.04, -2.6, -2.1, -1.61, -1.24, -0.91]\n Course: [0.0, -0.02, -0.01, -0.02, -0.02, -0.02, -0.01]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is approaching an intersection with a red light.\n\n",
+    "BDDX_Processed/video/0798a3a8-c4501e0f_07230.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.45, 2.82, 3.2, 3.57, 3.93, 4.2, 4.46]\n Curvature: [0.11, 0.07, 0.04, 0.0, -0.03, -0.01, -0.0]\n Acceleration: [0.87, 0.96, 1.06, 1.16, 1.24, 1.24, 1.25]\n Course: [0.0, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car slowly accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: since the car in front of it has started moving again\n\n",
+    "BDDX_Processed/video/09b80f74-c763ee9b_08474.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.71, 6.17, 5.58, 4.95, 3.98, 3.44, 3.65]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.03, 0.18, 0.51]\n Acceleration: [-0.91, -0.9, -1.01, -1.25, -0.88, -0.45, 0.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -13.0, -13.0]\nWhat is the action of ego car?\nAssistant: The car turns slightly to the right then forward and to the left then comes to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: As it tries to make a wide enough turn to make the U-Turn but the street is not wide enough to complete it in one maneuver so it has to stop before going onto the sidewalk.\n\n",
+    "BDDX_Processed/video/1f621be0-58c08371_19589.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.35, 5.96, 7.13, 7.83, 8.3, 8.39, 7.75]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.75, 1.77, 1.54, 0.94, 0.31, -0.12, -0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car emerged into a left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it passed stopped vehicles on the right side waiting to turn right.\n\n",
+    "BDDX_Processed/video/14cd7eab-8dbe20e8_14070.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [25.84, 22.59, 19.06, 15.53, 11.9, 8.09, 6.03]\n Curvature: [-0.0, 0.01, 0.01, 0.05, 0.13, 0.14, 0.23]\n Acceleration: [-2.54, -3.94, -4.55, -4.73, -4.74, -3.69, -1.51]\n Course: [0.0, -0.12, -0.14, -0.48, -1.18, -2.81, -5.91]\nWhat is the action of ego car?\nAssistant: The car slows down and turns left\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green and the intersection is clear.\n\n",
+    "BDDX_Processed/video/09b5ab15-d01184c6_08460.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.77, 3.57, 4.36, 5.36, 6.62, 7.89, 9.28]\n Curvature: [-0.0, -0.21, -0.42, -0.46, -0.24, -0.02, 0.01]\n Acceleration: [0.95, 1.61, 2.28, 2.79, 3.11, 3.42, 3.22]\n Course: [0.0, 0.0, 0.0, 6.93, 6.93, 6.93, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning to the right\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may enter another road.\n\n",
+    "BDDX_Processed/video/02b7b9ee-a76d996e_05279.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.87, 1.65, 0.74, 0.35, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.12, -0.98, -0.52, -0.36, -0.11, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/1f77fb7b-b2957aa2_19612.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.37, 9.76, 10.61, 11.49, 12.29, 12.22, 12.58]\n Curvature: [0.0, -0.01, -0.0, 0.0, 0.01, -0.01, -0.0]\n Acceleration: [0.2, 0.89, 0.29, 0.6, 0.05, 0.21, 0.12]\n Course: [0.0, 0.15, 0.16, 0.29, -0.25, -0.25, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates down the road\n\nHuman: Why does the ego car doing this?\nAssistant: since there is more room to do so in this lane.\n\n",
+    "BDDX_Processed/video/11577eed-f5f58ffe_12420.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.6, 14.42, 14.25, 13.88, 13.32, 12.7, 11.85]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.09, -0.46, -0.79, -1.15, -1.47, -1.83, -1.82]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls over to the middle lane.\n\nHuman: Why does the ego car doing this?\nAssistant: To try and get ahead of the guy in the left lane.\n\n",
+    "BDDX_Processed/video/083bc808-b649e045_07528.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.19, 0.25, 0.31, 0.37, 0.4, 0.36, 0.32]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.2, 0.16, 0.11, 0.06, 0.02, -0.03, -0.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins to pull forward and right\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane to the right is clear.\n\n",
+    "BDDX_Processed/video/0a7d8340-c8520b7f_08825.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.26, 0.19, 0.13, 0.06, 0.0, 0.0, 0.0]\n Curvature: [0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22]\n Acceleration: [-0.49, -0.39, -0.31, -0.21, -0.13, -0.09, -0.06]\n Course: [0.0, 0.02, 0.02, 0.02, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic in front of it is stopped.\n\n",
+    "BDDX_Processed/video/073dc97d-bad8ed1d_07109.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.03, 4.98, 5.3, 5.6, 5.82, 5.7, 5.67]\n Curvature: [0.01, -0.09, -0.07, -0.05, -0.06, 0.03, 0.37]\n Acceleration: [0.35, 0.54, 0.04, 0.19, 0.08, -0.13, 0.07]\n Course: [0.0, -1.27, -0.16, -0.28, 0.0, 0.0, 0.11]\nWhat is the action of ego car?\nAssistant: The car is going down the street at a steady speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady pace.\n\n",
+    "BDDX_Processed/video/19defcb1-5d0964bb_16865.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.62, 4.95, 5.28, 5.29, 5.01, 4.7, 4.47]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.45, 0.28, 0.1, -0.12, -0.36, -0.63, -0.63]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.27]\nWhat is the action of ego car?\nAssistant: The car slows and makes another right turn.\n\nHuman: Why does the ego car doing this?\nAssistant: Because the car is going onto another lane.\n\n",
+    "BDDX_Processed/video/1726919b-18c3e190_15618.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.34, 2.47, 4.18, 5.65, 7.02, 7.9, 7.87]\n Curvature: [-0.02, -0.01, 0.04, -0.04, 0.01, -0.0, 0.01]\n Acceleration: [0.89, 1.38, 1.54, 1.38, 1.1, 0.41, -0.19]\n Course: [0.0, 0.07, -0.14, -0.14, -0.05, -0.01, -0.05]\nWhat is the action of ego car?\nAssistant: The car heads down the road at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady pace.\n\n",
+    "BDDX_Processed/video/12790d81-a785432d_12932.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.94, 5.37, 5.77, 6.17, 6.23, 6.38, 6.76]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.46, 0.57, 0.55, 0.34, 0.32, 0.55, 1.29]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car veers right\n\nHuman: Why does the ego car doing this?\nAssistant: to merge into the center lane.\n\n",
+    "BDDX_Processed/video/0b2bed67-0c752272_09210.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.24, 4.38, 3.52, 2.65, 1.85, 1.48, 1.12]\n Curvature: [-0.01, 0.04, 0.09, 0.14, 0.2, 0.3, 0.4]\n Acceleration: [-3.67, -3.34, -3.01, -2.68, -2.34, -1.97, -1.6]\n Course: [0.0, -0.07, -0.07, -0.07, -0.05, -0.02, -0.02]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: after the traffic light turned red.\n\n",
+    "BDDX_Processed/video/03fd9aa1-4be42e2e_05775.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.51, 0.5, 0.49, 0.47, 0.45, 0.39, 0.33]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.05, -0.08, -0.1, -0.12, -0.15, -0.17, -0.19]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the road conditions are difficult.\n\n",
+    "BDDX_Processed/video/07e9e760-687a2c40_07348.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.02, -0.02, -0.02, -0.01, -0.01, -0.0, -0.0]\n Acceleration: [-0.09, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red\n\n",
+    "BDDX_Processed/video/1fdf9217-47d61d91_19879.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.21, 1.89, 2.55, 2.83, 3.04, 2.76, 2.43]\n Curvature: [0.08, 0.04, 0.01, 0.01, -0.0, -0.0, -0.0]\n Acceleration: [1.26, 1.09, 0.9, 0.42, -0.05, -0.49, -0.88]\n Course: [0.0, 0.09, 0.03, -0.14, -0.14, -0.14, -0.03]\nWhat is the action of ego car?\nAssistant: The car slowly moves up\n\nHuman: Why does the ego car doing this?\nAssistant: as the line of car has also moved up.\n\n",
+    "BDDX_Processed/video/1980add1-b45ab642_16727.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.87, 11.45, 10.78, 10.14, 10.57, 10.19, 9.1]\n Curvature: [0.02, -0.01, 0.0, 0.0, 0.01, -0.0, -0.01]\n Acceleration: [-0.28, -0.33, -0.6, -0.04, 0.06, -0.55, -0.82]\n Course: [0.0, -0.13, 0.0, -0.09, -0.13, -0.09, 0.0]\nWhat is the action of ego car?\nAssistant: The car proceeds down the street\n\nHuman: Why does the ego car doing this?\nAssistant: since the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/0415cf54-5f2f44fc_05857.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.12, 8.13, 8.14, 8.15, 8.21, 8.57, 8.98]\n Curvature: [0.0, -0.03, -0.05, -0.08, -0.1, -0.06, -0.02]\n Acceleration: [0.09, 0.25, 0.44, 0.6, 0.76, 0.81, 0.86]\n Course: [0.0, -0.0, -0.0, -0.0, 0.11, 0.23, 0.34]\nWhat is the action of ego car?\nAssistant: The car moves right\n\nHuman: Why does the ego car doing this?\nAssistant: as to merge back onto the the correct side of the road.\n\n",
+    "BDDX_Processed/video/0d1fc580-2bbf8587_10338.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.29, 19.21, 19.14, 19.06, 18.99, 18.95, 18.91]\n Curvature: [-0.09, -0.05, -0.0, 0.04, 0.07, 0.05, 0.04]\n Acceleration: [-0.11, -0.14, -0.17, -0.21, -0.24, -0.36, -0.48]\n Course: [0.0, 0.05, 0.05, 0.05, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car passes under the Fourth Street exit sign\n\nHuman: Why does the ego car doing this?\nAssistant: because it is leaving the highway.\n\n",
+    "BDDX_Processed/video/03d34d05-4a61174c_01559.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [21.58, 21.72, 22.83, 22.98, 22.57, 22.64, 22.45]\n Curvature: [0.0, 0.02, 0.04, 0.01, 0.01, 0.01, -0.01]\n Acceleration: [0.11, 0.32, 0.83, -0.36, 0.04, -0.08, -0.09]\n Course: [0.0, 0.13, -0.14, 1.42, -0.77, -1.42, -0.21]\nWhat is the action of ego car?\nAssistant: The car maintains speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/2038e4b0-4404bded_19997.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.65, 3.69, 3.73, 3.77, 3.83, 4.0, 4.17]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [-0.05, 0.07, 0.18, 0.3, 0.41, 0.46, 0.5]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows a bit\n\nHuman: Why does the ego car doing this?\nAssistant: because a car up ahead slows to turn right.\n\n",
+    "BDDX_Processed/video/06ebf6cd-79235c8d_06802.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.38, 3.86, 8.2, 10.07, 10.89, 10.51, 2.58]\n Curvature: [0.04, 0.16, 0.05, -0.02, 0.02, -0.01, 0.06]\n Acceleration: [0.17, 1.84, 1.71, -1.46, 1.23, -2.14, -1.19]\n Course: [0.0, -9.28, -5.66, 0.52, 1.3, 0.0, -0.81]\nWhat is the action of ego car?\nAssistant: The car turns left then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red and pedestrians are crossing.\n\n",
+    "BDDX_Processed/video/10b038d4-a0efb156_11919.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [31.88, 30.89, 28.5, 24.65, 23.08, 22.31, 20.69]\n Curvature: [0.0, 0.01, -0.0, -0.0, -0.01, -0.0, -0.02]\n Acceleration: [-0.71, -0.32, -1.89, -1.02, -0.2, -0.42, -0.72]\n Course: [0.0, -0.39, 0.0, 0.12, 0.21, 0.4, 1.11]\nWhat is the action of ego car?\nAssistant: The car driving down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/142b9c5c-c4ca118e_13812.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.73, 5.58, 5.42, 5.35, 5.42, 5.5, 5.9]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.77, -0.53, -0.26, -0.0, 0.33, 0.63, 0.85]\n Course: [0.0, -0.04, -0.06, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is slowing down.\n\n",
+    "BDDX_Processed/video/04076a65-55eb2157_05807.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.11, 4.21, 4.3, 4.35, 4.41, 4.55, 4.73]\n Curvature: [0.17, 0.13, 0.1, 0.09, 0.08, 0.12, 0.13]\n Acceleration: [0.21, 0.17, 0.14, 0.16, 0.2, 0.35, 0.48]\n Course: [0.0, -2.54, -2.52, -2.45, -2.02, -1.59, -1.42]\nWhat is the action of ego car?\nAssistant: The car turns left\n\nHuman: Why does the ego car doing this?\nAssistant: as it is pulling into a parking lot\n\n",
+    "BDDX_Processed/video/120f96e5-7fdb83ae_12790.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.19, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/2094d2bf-3e79988b_20187.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.03, 0.25, 0.44]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.11, -1.0, -1.56]\n Acceleration: [0.0, 0.0, 0.01, 0.12, 0.22, 0.29, 0.33]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: at a red light.\n\n",
+    "BDDX_Processed/video/1659f092-0a9ea83c_15086.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.4, 15.45, 14.62, 13.67, 12.81, 12.56, 12.33]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.4, -2.36, -2.33, -2.29, -2.36, -3.18, -3.9]\n Course: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/03d34d05-4a61174c_05708.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [22.62, 22.6, 22.58, 22.55, 22.54, 22.58, 22.63]\n Curvature: [0.12, 0.09, 0.07, 0.04, 0.02, 0.01, 0.01]\n Acceleration: [-0.53, -0.38, -0.25, -0.09, 0.04, 0.04, 0.04]\n Course: [0.0, -0.12, -0.06, -0.12, -0.13, -0.14, -0.07]\nWhat is the action of ego car?\nAssistant: The car starts driving straight forward again\n\nHuman: Why does the ego car doing this?\nAssistant: because the road has stopped curving\n\n",
+    "BDDX_Processed/video/1f4b8e7d-00a8dd02_19467.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.0, 10.52, 10.99, 11.25, 11.16, 11.06, 10.66]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.29, 0.98, 0.7, 0.32, -0.13, -0.61, -0.98]\n Course: [0.0, 0.0, 0.0, 0.02, 0.02, 0.02, 0.0]\nWhat is the action of ego car?\nAssistant: The car goes to the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is attempting to turn right.\n\n",
+    "BDDX_Processed/video/076399f2-3cc0e470_02222.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.22, 0.86, 1.66, 1.36, 0.65, 1.28, 1.24]\n Curvature: [0.01, 0.0, -0.02, 0.08, 1.11, 1.48, 0.76]\n Acceleration: [0.3, 0.38, 0.19, -0.36, 0.0, 0.2, -0.31]\n Course: [0.0, -1.07, 0.0, 2.7, 0.0, 0.0, -3.24]\nWhat is the action of ego car?\nAssistant: The car moves forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because other traffic is turning and there are pedestrians in the roadway.\n\n",
+    "BDDX_Processed/video/01e9deb7-ed388859_00418.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.41, 8.66, 8.88, 9.13, 9.31, 9.02, 8.76]\n Curvature: [0.01, 0.0, -0.0, -0.0, -0.01, 0.0, 0.01]\n Acceleration: [0.48, 0.33, 0.2, 0.05, -0.18, -0.92, -1.58]\n Course: [0.0, -0.14, -0.07, -0.14, -0.14, -0.14, -0.07]\nWhat is the action of ego car?\nAssistant: The car slows quickly\n\nHuman: Why does the ego car doing this?\nAssistant: because the silver car was cutting it off.\n\n",
+    "BDDX_Processed/video/0062f18d-f8cd3a65_04412.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.21, 7.99, 7.93, 8.03, 7.99, 7.93, 7.83]\n Curvature: [0.0, 0.0, -0.01, -0.03, -0.04, -0.03, -0.0]\n Acceleration: [-0.33, -0.17, -0.05, 0.04, -0.05, -0.14, -0.21]\n Course: [0.0, 0.14, 0.27, 0.33, 0.55, 0.59, 0.49]\nWhat is the action of ego car?\nAssistant: The car drives down a street\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/12a6dd7e-cc056c30_13020.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.06, 4.3, 4.45, 4.55, 4.62, 4.65, 4.58]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.35, 0.28, 0.21, 0.13, 0.06, 0.01, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car approaches an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/1458e4ad-24fdf5c9_13902.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.49, 0.31, 0.48, 0.26, 0.05, 0.0, 0.0]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [-1.42, 0.24, -0.15, -0.02, -0.11, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is waiting for the light to change\n\nHuman: Why does the ego car doing this?\nAssistant: since there is traffic in the intersection.\n\n",
+    "BDDX_Processed/video/0b4892bd-444faa63_09241.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.03, 0.83, 0.65, 0.52, 0.47, 0.41, 0.41]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.49, -0.42, -0.36, -0.27, -0.18, -0.07, -0.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it's waiting for a chance to turn.\n\n",
+    "BDDX_Processed/video/0f7569a1-afced45a_11418.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.87, 3.34, 3.31, 7.36, 8.27, 8.73, 8.84]\n Curvature: [0.0, 0.1, -0.07, 0.02, -0.02, 0.04, -0.09]\n Acceleration: [1.5, 0.6, 2.0, 2.41, 0.68, 0.28, 0.14]\n Course: [0.0, 0.0, 0.0, -0.14, -0.06, 0.09, 0.38]\nWhat is the action of ego car?\nAssistant: The car maintains speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/0f062b89-9780bfa6_11291.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.17, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.08, -0.02, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car remains at a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light remains red.\n\n",
+    "BDDX_Processed/video/05584a52-590d4e59_06271.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.21, 8.64, 8.14, 7.56, 6.94, 6.01, 5.19]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-2.51, -2.59, -2.67, -2.75, -2.79, -2.53, -2.29]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/1d24537d-f291dab4_18386.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.2, 1.14, 3.37, 4.92, 6.19, 7.13, 8.19]\n Curvature: [0.22, 0.1, 0.01, -0.01, 0.01, -0.0, 0.0]\n Acceleration: [0.22, 1.11, 1.34, 0.87, 0.7, 0.68, 0.59]\n Course: [0.0, -0.15, -0.07, -0.13, 0.06, -0.13, 0.07]\nWhat is the action of ego car?\nAssistant: The car accelerates forward through the intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned green and traffic is flowing again.\n\n",
+    "BDDX_Processed/video/0308ce2b-8fc462fd_05442.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.7, 6.93, 7.87, 8.78, 9.08, 9.01, 8.96]\n Curvature: [-0.02, -0.02, -0.09, -0.11, -0.06, -0.06, -0.05]\n Acceleration: [1.5, 1.05, 0.9, 0.58, 0.12, -0.07, 0.23]\n Course: [0.0, 0.6, 1.68, 3.36, -46.75, -33.72, 2.13]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no impeding traffic.\n\n",
+    "BDDX_Processed/video/13d47839-d6add655_13674.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.31, 5.2, 3.7, 2.64, 1.68, 0.62, 0.15]\n Curvature: [0.0, -0.0, -0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.65, -2.27, -1.73, -1.34, -1.24, -0.96, -0.45]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is halted\n\n",
+    "BDDX_Processed/video/0a350edb-6f5c7ef8_08701.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [35.09, 34.89, 34.72, 34.51, 34.33, 34.26, 34.2]\n Curvature: [0.0, 0.01, 0.02, 0.02, 0.03, 0.02, 0.01]\n Acceleration: [-0.39, -0.42, -0.45, -0.48, -0.48, -0.31, -0.16]\n Course: [0.0, -0.07, -0.04, -0.07, -0.11, -0.14, -0.07]\nWhat is the action of ego car?\nAssistant: The car merges into the lane to its right\n\nHuman: Why does the ego car doing this?\nAssistant: because there is traffic ahead of it in its current lane and the right lane has no nearby traffic.\n\n",
+    "BDDX_Processed/video/0b8d1c37-c3fa8173_09475.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.3, 10.57, 8.72, 7.02, 5.33, 4.6, 3.59]\n Curvature: [0.02, 0.0, -0.02, -0.01, -0.29, -2.39, -3.78]\n Acceleration: [-3.2, -3.32, -3.4, -2.9, -2.44, -2.51, -2.51]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a red light.\n\n",
+    "BDDX_Processed/video/1b883059-913ecef4_17708.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.77, 13.66, 13.46, 12.04, 10.62, 9.22, 7.59]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-0.14, -0.83, -1.53, -2.17, -2.81, -3.41, -3.83]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down and comes to a stop at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red\n\n",
+    "BDDX_Processed/video/0cfcbb80-015af547_10240.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.62, 0.5, 0.39, 0.27, 0.17, 0.18, 0.19]\n Curvature: [0.06, 0.08, 0.1, 0.12, 0.14, 0.14, 0.15]\n Acceleration: [-2.18, -1.67, -1.16, -0.65, -0.16, 0.18, 0.53]\n Course: [0.0, 0.02, 0.02, 0.02, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as a vehicle pulls out in front of it.\n\n",
+    "BDDX_Processed/video/0c6482e0-75260514_09840.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.37, 1.0, 0.69, 0.32, 0.07, 0.61, 1.09]\n Curvature: [0.03, 0.03, 0.03, 0.03, 0.03, 0.01, -0.01]\n Acceleration: [-2.29, -1.59, -0.98, -0.28, 0.39, 0.85, 1.24]\n Course: [0.0, 0.0, 0.0, 0.0, -0.01, -0.02, -0.01]\nWhat is the action of ego car?\nAssistant: The car accelerates down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the intersection is clear of traffic.\n\n",
+    "BDDX_Processed/video/13796ade-04e91bd7_13453.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.22, 8.45, 7.68, 6.91, 6.24, 5.55, 4.87]\n Curvature: [-0.0, 0.0, 0.0, 0.01, 0.01, -0.03, -0.06]\n Acceleration: [-2.16, -2.32, -2.49, -2.66, -2.76, -2.56, -2.36]\n Course: [0.0, -0.02, -0.02, -0.02, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped.\n\n",
+    "BDDX_Processed/video/0d0b3605-9162b343_10290.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.16, 10.59, 11.44, 11.87, 12.58, 12.24, 12.54]\n Curvature: [0.0, 0.0, 0.0, 0.01, -0.0, 0.0, -0.0]\n Acceleration: [-0.07, -0.19, 0.41, 0.06, 0.25, -0.09, 0.05]\n Course: [0.0, 0.71, -0.15, 0.1, 0.25, 0.0, -0.17]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/0104eb19-162abfea_02555.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.92, 11.5, 11.08, 10.84, 10.79, 10.74, 10.79]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.99, -0.84, -0.68, -0.48, -0.26, -0.01, 0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stays right\n\nHuman: Why does the ego car doing this?\nAssistant: to avoid a car turning left.\n\n",
+    "BDDX_Processed/video/08e4f0af-3b719b39_07948.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: at the red light.\n\n",
+    "BDDX_Processed/video/15b8be6c-ab5d258f_14653.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.28, 16.92, 16.57, 16.32, 16.27, 16.64, 17.18]\n Curvature: [0.0, 0.01, 0.0, -0.0, -0.0, 0.0, 0.01]\n Acceleration: [-0.4, -0.44, -0.37, -0.15, 0.21, 0.56, 0.71]\n Course: [0.0, 0.0, 0.0, 0.05, 0.03, 0.0, 0.04]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front of it is slowing down.\n\n",
+    "BDDX_Processed/video/06ff7d61-45086936_00566.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.46, 9.73, 9.03, 7.94, 7.01, 6.69, 6.43]\n Curvature: [0.01, -0.05, -0.1, -0.12, -0.14, -0.15, -0.16]\n Acceleration: [-0.93, -1.34, -1.69, -1.49, -1.26, -0.77, -0.33]\n Course: [0.0, 0.19, 0.66, 2.06, 2.02, 3.0, 2.22]\nWhat is the action of ego car?\nAssistant: The car is slowing slightly and turning right\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is green.\n\n",
+    "BDDX_Processed/video/0fc0c815-8ba80d60_11540.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.52, 11.71, 11.87, 12.06, 12.21, 12.09, 12.0]\n Curvature: [0.02, 0.01, 0.0, -0.01, -0.02, -0.02, -0.02]\n Acceleration: [0.95, 0.73, 0.55, 0.33, 0.12, -0.03, -0.17]\n Course: [0.0, -0.07, -0.04, -0.07, -0.04, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car steers to the right\n\nHuman: Why does the ego car doing this?\nAssistant: to avoid road hazards to its left.\n\n",
+    "BDDX_Processed/video/0502de3f-c9529246_04111.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.56, 4.6, 3.64, 2.68, 1.9, 1.44, 0.98]\n Curvature: [0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.04]\n Acceleration: [-3.74, -3.46, -3.18, -2.9, -2.64, -2.2, -1.75]\n Course: [0.0, -0.05, -0.05, -0.05, -0.01, -0.02, -0.02]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: due to the light turning red.\n\n",
+    "BDDX_Processed/video/1004b27a-c30299b5_11634.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.08, 3.64, 2.33, 1.45, 0.68, 0.29, 0.0]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-3.22, -2.66, -2.14, -1.62, -1.14, -0.7, -0.32]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as the light ahead turns red.\n\n",
+    "BDDX_Processed/video/1558cb8a-147e2c9e_14465.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.76, 9.24, 8.04, 6.2, 4.33, 3.13, 2.04]\n Curvature: [0.0, 0.01, -0.0, -0.03, 0.02, 0.0, -0.01]\n Acceleration: [-0.42, -0.72, -1.28, -1.63, -1.3, -0.95, -0.68]\n Course: [0.0, 0.05, 0.0, 0.21, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because it is approaching an intersection where the traffic light is red and there is a car stopped in front of it.\n\n",
+    "BDDX_Processed/video/07184fbb-bb4c67d2_01357.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.24, 0.18, 0.03, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.09, -0.1, -0.09, -0.02, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is now red.\n\n",
+    "BDDX_Processed/video/1b71edc8-9793def5_17677.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.12, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.09, -0.05, -0.05, -0.05, -0.05, -0.05, -0.05]\n Acceleration: [-2.28, -0.2, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.06, 0.21, 0.09, 0.0, 0.0, 0.21]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/19906a63-e2ba347a_16785.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.92, 6.82, 6.72, 6.63, 6.54, 6.17, 5.81]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.45, -0.56, -0.67, -0.78, -0.88, -1.03, -1.19]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves back to the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: since it has a clear lane\n\n",
+    "BDDX_Processed/video/1f44d8f2-6feb2c27_19440.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.14, 5.86, 6.58, 6.72, 6.86, 6.92, 7.13]\n Curvature: [0.0, 0.05, 0.1, 0.02, -0.05, -0.03, -0.01]\n Acceleration: [1.43, 1.14, 0.83, 0.51, 0.25, 0.55, 0.89]\n Course: [0.0, 0.34, 0.19, -0.44, -0.25, 0.07, 0.2]\nWhat is the action of ego car?\nAssistant: The car slowly speeds up\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is moving slowly.\n\n",
+    "BDDX_Processed/video/0d66ed0a-b2595268_10530.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.62, 7.29, 8.39, 9.59, 11.92, 12.72, 11.79]\n Curvature: [-0.0, -0.0, -0.01, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.21, 1.34, 1.1, 1.73, 1.53, -0.05, -1.34]\n Course: [0.0, 0.0, 0.11, 0.27, 0.0, 0.0, 0.03]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no nearby traffic.\n\n",
+    "BDDX_Processed/video/1eee11d8-2a7a5c07_19277.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.79, 6.02, 5.32, 4.49, 3.65, 2.73, 2.05]\n Curvature: [0.02, 0.0, -0.01, -0.0, 0.02, 0.04, 0.07]\n Acceleration: [-1.7, -1.86, -2.01, -2.08, -2.02, -1.96, -1.57]\n Course: [0.0, -0.04, -0.04, -0.11, -0.07, -0.11, -0.28]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to a near stop\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a stop sign.\n\n",
+    "BDDX_Processed/video/1aa8ffe3-54a64768_17272.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 1.5, 2.68, 3.63, 4.18, 4.26, 4.84]\n Curvature: [3.53, 0.62, -0.26, 0.37, 0.79, 0.59, 1.09]\n Acceleration: [0.66, 0.98, 0.89, 0.57, 0.19, 0.25, 0.87]\n Course: [0.0, 1.33, 0.45, -0.84, 0.0, 84.5, -17.24]\nWhat is the action of ego car?\nAssistant: The car makes a U-turn\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is green and the intersection is clear.\n\n",
+    "BDDX_Processed/video/0bc0b27e-ab185876_09551.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.93, 0.46, 0.76, 0.12, 0.68, 0.85, 0.66]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-1.75, -1.14, 0.36, -0.32, 0.43, 0.04, -0.15]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down, stops, and reverses\n\nHuman: Why does the ego car doing this?\nAssistant: in order to parallel park in an open space on the left side of the road.\n\n",
+    "BDDX_Processed/video/1aa228ba-ba35fddc_17154.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.64, 7.12, 7.51, 7.71, 7.69, 7.67, 7.66]\n Curvature: [-0.01, -0.0, 0.01, 0.01, -0.0, -0.0, 0.0]\n Acceleration: [0.9, 0.62, 0.37, 0.14, -0.02, -0.02, -0.04]\n Course: [0.0, 0.33, 0.0, -0.38, -0.19, -0.01, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins merging left\n\nHuman: Why does the ego car doing this?\nAssistant: as the left lane is clear.\n\n",
+    "BDDX_Processed/video/0879ba7f-43a47bb3_07671.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.43, 0.18, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-1.49, -5.65, -5.68, -5.7, -5.73, -5.75, -5.78]\n Acceleration: [-2.07, -0.13, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, -0.07, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red and pedestrians are crossing the street.\n\n",
+    "BDDX_Processed/video/0ab1b9e8-4222958e_08921.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.32, 8.45, 7.58, 6.71, 5.95, 5.97, 5.98]\n Curvature: [-0.02, -0.02, -0.01, -0.01, -0.0, 0.01, 0.01]\n Acceleration: [-2.99, -2.64, -2.3, -1.96, -1.59, -1.05, -0.52]\n Course: [0.0, 0.02, 0.02, 0.02, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is braking briefly\n\nHuman: Why does the ego car doing this?\nAssistant: to go over a speed bump carefully\n\n",
+    "BDDX_Processed/video/20b3b81c-dfbb4471_20222.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.94, 8.76, 8.57, 8.39, 8.23, 7.56, 6.88]\n Curvature: [0.01, 0.01, 0.0, -0.0, -0.01, -0.01, -0.01]\n Acceleration: [-0.38, -0.7, -1.02, -1.34, -1.62, -1.96, -2.29]\n Course: [0.0, 0.05, 0.05, 0.05, 0.02, 0.07, 0.07]\nWhat is the action of ego car?\nAssistant: The car is coming almost to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped ahead.\n\n",
+    "BDDX_Processed/video/039f729b-0fb582da_00710.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.56, 7.86, 9.42, 10.17, 9.76, 9.68, 9.25]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.6, 0.62, 0.25, 0.11, -0.19, -0.13, 0.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates slowly to a maintained speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving slow, but smoothly.\n\n",
+    "BDDX_Processed/video/0d36d7ef-77501746_10479.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.73, 9.44, 10.88, 13.75, 15.86, 17.35, 18.09]\n Curvature: [0.01, -0.04, -0.0, -0.0, -0.0, 0.07, 0.0]\n Acceleration: [-0.86, -0.89, 1.18, 1.02, 0.67, 0.39, 0.26]\n Course: [0.0, 0.0, 0.24, 0.0, 0.0, -0.11, -0.21]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: due to the green traffic light.\n\n",
+    "BDDX_Processed/video/025b2ded-de608724_01615.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.01, 0.09, 0.18]\n Curvature: [-0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01]\n Acceleration: [-0.49, -0.23, 0.0, 0.04, 0.09, 0.18, 0.42]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: The traffic ahead of the car is stopped.\n\n",
+    "BDDX_Processed/video/05fa6494-8f436d04_02015.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.88, 16.84, 17.26, 17.37, 17.45, 18.17, 18.93]\n Curvature: [0.0, 0.04, 0.01, -0.0, -0.0, -0.01, -0.03]\n Acceleration: [0.4, 0.32, 0.06, 0.07, 0.08, 0.34, 0.23]\n Course: [0.0, -3.7, -1.47, 0.08, 0.0, 0.5, 4.72]\nWhat is the action of ego car?\nAssistant: The car maintains a constant speed and spacing with the vehicle ahead\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/1c4bf60c-0a6d4747_17997.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.07, 11.59, 8.82, 6.51, 5.33, 5.06, 5.56]\n Curvature: [0.0, -0.0, 0.0, -0.0, -0.73, -0.59, -0.02]\n Acceleration: [-1.07, -2.31, -2.73, -1.94, -0.84, 0.13, 0.95]\n Course: [0.0, 0.0, 0.0, 0.0, 7.64, 12.69, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows and turns right\n\nHuman: Why does the ego car doing this?\nAssistant: since the road is clear to turn.\n\n",
+    "BDDX_Processed/video/0c9f5bb9-4d89a4ed_09953.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.52, 7.77, 7.03, 6.28, 5.64, 5.0, 4.37]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.11, -2.25, -2.39, -2.53, -2.59, -2.27, -1.94]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car steers to the right and jerks back to its current lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it wants to change lanes but a car is next to it.\n\n",
+    "BDDX_Processed/video/0336de82-802d2437_05502.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.01, 0.07, 0.13]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.02, 0.03]\n Acceleration: [0.0, 0.03, 0.06, 0.09, 0.12, 0.16, 0.2]\n Course: [0.0, 0.02, 0.02, 0.02, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front moved up slightly.\n\n",
+    "BDDX_Processed/video/115d9e12-3d8bfc10_12463.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.06, 11.45, 13.29, 14.29, 13.23, 10.34, 9.65]\n Curvature: [-0.09, -0.12, -0.05, -0.05, -0.09, -0.15, -0.2]\n Acceleration: [0.82, 0.75, 0.9, 0.08, -1.36, -0.91, -0.0]\n Course: [0.0, 6.99, 2.77, 2.17, 3.55, 8.89, 10.59]\nWhat is the action of ego car?\nAssistant: The car proceeds down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is clear.\n\n",
+    "BDDX_Processed/video/0d0b3605-9162b343_10291.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.81, 10.21, 9.79, 9.53, 9.38, 9.2, 8.14]\n Curvature: [0.02, 0.02, 0.02, 0.01, -0.0, -0.01, -0.01]\n Acceleration: [-0.76, -0.64, -0.45, -0.28, -0.28, -0.82, -1.28]\n Course: [0.0, -0.68, -0.54, 0.0, 0.03, 0.07, 0.07]\nWhat is the action of ego car?\nAssistant: The car moves over two lanes to the left\n\nHuman: Why does the ego car doing this?\nAssistant: as the left lane is clear.\n\n",
+    "BDDX_Processed/video/1adbf9f5-fa9f8eac_17469.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [20.01, 19.16, 18.01, 17.9, 17.52, 18.05, 18.71]\n Curvature: [0.0, -0.0, -0.0, 0.01, -0.02, 0.01, -0.0]\n Acceleration: [-0.33, -0.5, -0.25, 0.06, -0.17, 0.35, 0.07]\n Course: [0.0, -0.0, 0.0, -2.03, 1.31, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car travels at an even speed down a busy road at night\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving well.\n\n",
+    "BDDX_Processed/video/160f6a0b-ea25518e_14912.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.41, 0.96, 1.66, 3.15, 4.38]\n Curvature: [0.12, 0.12, 0.09, 0.08, 0.09, -0.0, -0.01]\n Acceleration: [-0.13, 0.25, 0.57, 0.93, 1.38, 1.65, 0.88]\n Course: [0.0, 0.0, 0.0, -0.74, -1.67, -2.05, -0.21]\nWhat is the action of ego car?\nAssistant: The car starts moving again and passes the SUV from the left\n\nHuman: Why does the ego car doing this?\nAssistant: because the black SUV on the left side moved and the road ahead is clear.\n\n",
+    "BDDX_Processed/video/07184fbb-5d195420_01271.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.79, 3.08, 0.48, 0.48, 0.6, 0.28, 0.26]\n Curvature: [0.0, -0.0, -0.14, -0.06, 0.25, 0.25, 0.2]\n Acceleration: [-2.62, -2.61, -1.26, 0.07, -0.1, -0.15, -0.09]\n Course: [0.0, 0.18, 0.07, -0.55, -0.55, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car has slowed to a stop.\n\nHuman: Why does the ego car doing this?\nAssistant: The light controlling the intersection the car is stopped at is red.\n\n",
+    "BDDX_Processed/video/154ea6d1-6dfc3b99_14413.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is parked\n\nHuman: Why does the ego car doing this?\nAssistant: as there is another vehicle parked in front of it.\n\n",
+    "BDDX_Processed/video/037795d6-d456ac0c_00465.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.87, 0.37, 0.85, 0.2, 0.0, 0.0, 0.0]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, -0.38, -0.23, -0.23, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked.\n\n",
+    "BDDX_Processed/video/0fbf178c-7465e186_11527.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.99, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-0.44, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead is at a standstill.\n\n",
+    "BDDX_Processed/video/0b7cbaa7-d677bc73_09395.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.86, 7.89, 7.91, 7.94, 7.99, 8.14, 8.27]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.07, 0.15, 0.21, 0.28, 0.36, 0.5, 0.63]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no cars in its lane.\n\n",
+    "BDDX_Processed/video/0dcd165f-81255c47_10697.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.74, 0.55, 0.36, 0.17, 0.01, 0.05, 0.09]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.94, -0.78, -0.61, -0.44, -0.28, -0.21, -0.13]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: for the red light.\n\n",
+    "BDDX_Processed/video/04095551-efe2622a_05836.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red\n\n",
+    "BDDX_Processed/video/19de62f2-71da09bf_16852.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.57, 1.62, 1.19, 0.86, 0.96, 0.55, 0.13]\n Curvature: [0.0, 0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.08, -0.19, -0.38, -0.11, -0.15, -0.41, -0.27]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slowly moves forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the cars in front have moved forward just a bit.\n\n",
+    "BDDX_Processed/video/06836e19-0798237e_03385.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.13, 2.46, 1.79, 1.12, 0.54, 0.59, 0.65]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-3.97, -3.26, -2.54, -1.82, -1.12, -0.59, -0.06]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because there is cross traffic.\n\n",
+    "BDDX_Processed/video/15e7be3e-00017b62_14728.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.11, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.04, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, -0.21, 0.0, 0.0, 0.12, 0.23, 0.0]\nWhat is the action of ego car?\nAssistant: The car is parked on the right side of the road with its indicators flashing\n\nHuman: Why does the ego car doing this?\nAssistant: because it has arrived at its destination.\n\n",
+    "BDDX_Processed/video/01dc1bbe-b5109eb3_00224.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.76, 6.92, 9.3, 10.44, 12.82, 13.88, 14.8]\n Curvature: [-0.06, 0.02, -0.0, 0.0, -0.01, 0.02, -0.0]\n Acceleration: [0.29, 1.84, 0.4, 1.14, 0.64, 0.52, 0.26]\n Course: [0.0, -0.7, 0.08, 0.77, -0.19, -0.58, 0.11]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: since the traffic is clear\n\n",
+    "BDDX_Processed/video/1517f29b-0c1f7f43_14241.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.65, 4.03, 4.39, 4.7, 4.88, 5.09, 4.89]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.77, 0.76, 0.74, 0.6, 0.3, -0.03, -0.41]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car changes to the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because a bus has stopped in the right lane, creating more space in that lane amid congested traffic.\n\n",
+    "BDDX_Processed/video/1710645f-c7c8f003_15578.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.83, 8.44, 7.93, 7.87, 7.49, 6.88, 6.78]\n Curvature: [-0.0, -0.01, -0.0, 0.0, 0.01, 0.0, -0.02]\n Acceleration: [-1.3, -0.83, -0.24, -0.16, -0.46, -0.29, -0.02]\n Course: [0.0, 0.3, 0.31, 0.11, -0.18, -0.53, 0.2]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic ahead is moving normally.\n\n",
+    "BDDX_Processed/video/02d478d1-42ca3cfb_02996.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.57, 10.51, 10.44, 10.38, 10.3, 10.16, 10.02]\n Curvature: [-0.03, -0.03, -0.04, -0.04, -0.04, -0.04, -0.03]\n Acceleration: [0.49, 0.27, 0.04, -0.19, -0.43, -0.72, -1.01]\n Course: [0.0, 0.11, 0.11, 0.11, 0.14, 0.16, 0.16]\nWhat is the action of ego car?\nAssistant: The car maintains speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear.\n\n",
+    "BDDX_Processed/video/166ce665-c51e54f5_15126.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.6, 11.66, 10.85, 9.91, 8.98, 8.08, 7.3]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.63, -2.11, -2.53, -3.02, -3.4, -3.12, -2.87]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down to a rolling stop\n\nHuman: Why does the ego car doing this?\nAssistant: As it approaches a stop sign where the street merges with another street and it has to yield to other traffic.\n\n",
+    "BDDX_Processed/video/16504799-3bf2d2bf_15048.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.74, 10.77, 8.78, 8.6, 7.53, 7.44, 7.84]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.01, 0.0, 0.0]\n Acceleration: [0.2, -1.12, -0.59, -0.4, -0.14, 0.06, 0.18]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.11]\nWhat is the action of ego car?\nAssistant: The car heads down the street at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/026501c2-dfd80f2d_01789.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.96, 1.05, 0.9, 0.47, 0.95, 1.27, 1.2]\n Curvature: [0.0, -0.0, -0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.23, -0.25, -0.16, 0.07, 0.27, 0.26, -0.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's pulling to the right.\n\n",
+    "BDDX_Processed/video/07184fbb-2dc17016_06977.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.5, 4.49, 4.48, 4.44, 4.37, 4.3, 4.23]\n Curvature: [0.0, -0.05, -0.1, -0.12, -0.11, -0.1, -0.11]\n Acceleration: [-0.03, -0.06, -0.09, -0.12, -0.15, -0.19, -0.35]\n Course: [0.0, 2.64, 1.76, 2.04, 2.04, 2.04, 0.77]\nWhat is the action of ego car?\nAssistant: The car turns right then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/019c8305-0bb153d0_04147.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.13, 8.17, 8.43, 8.98, 5.22, 2.64, 3.14]\n Curvature: [0.04, 0.02, 0.01, 0.02, -0.07, -0.15, -0.18]\n Acceleration: [0.5, 0.47, -0.51, -2.65, -2.88, -2.03, 0.42]\n Course: [0.0, -4.31, -0.17, -0.17, 0.06, -0.4, -0.4]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/0ce3ea39-fbdbae02_10136.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.61, 4.3, 5.84, 6.83, 7.72, 8.29, 8.5]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.05, -0.25, 0.26]\n Acceleration: [1.64, 1.57, 1.22, 0.91, 0.71, 0.39, -0.11]\n Course: [0.0, 0.0, 0.0, 0.03, 0.04, -0.17, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: after the light turned green.\n\n",
+    "BDDX_Processed/video/0960d93c-1ce28b0f_08162.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.75, 9.98, 9.91, 10.08, 10.35, 10.18, 9.93]\n Curvature: [0.0, -0.0, -0.0, 0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.23, 0.07, 0.01, 0.23, 0.02, -0.19, -0.33]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down a street\n\nHuman: Why does the ego car doing this?\nAssistant: as the street is clear ahead.\n\n",
+    "BDDX_Processed/video/14eb5976-56de5300_14133.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.19, 3.75, 4.3, 4.86, 5.33, 5.69, 6.05]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.84, 1.81, 1.79, 1.77, 1.59, 0.33, -0.93]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car curves back to the right\n\nHuman: Why does the ego car doing this?\nAssistant: because the road straightens out\n\n",
+    "BDDX_Processed/video/1abefa71-2fc9f192_17344.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.42, 0.34, 1.1, 2.61, 4.39, 6.07, 6.99]\n Curvature: [-0.33, -0.65, -0.91, -0.41, 0.0, -0.0, 0.0]\n Acceleration: [-0.22, 0.25, 0.67, 1.14, 1.12, 0.9, 0.28]\n Course: [0.0, 15.81, 2.99, 6.61, 2.46, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating and finishing the right turn\n\nHuman: Why does the ego car doing this?\nAssistant: because the pedestrian has cleared this half of the road.\n\n",
+    "BDDX_Processed/video/1dcbf95d-5b276cdc_18723.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.3, 16.27, 16.24, 16.22, 16.19, 14.61, 13.03]\n Curvature: [-0.0, 0.0, 0.0, 0.01, 0.01, 0.0, -0.0]\n Acceleration: [-0.16, -0.9, -1.54, -2.28, -2.92, -2.86, -2.81]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.11, -0.11]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may move to the left lane after the vehicle to its left passes because its lane exits to the right.\n\n",
+    "BDDX_Processed/video/031637c4-10cdaf09_05476.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.51, 8.36, 9.11, 9.81, 9.65, 9.77, 9.0]\n Curvature: [-0.02, 0.03, 0.0, -0.0, -0.01, 0.02, -0.01]\n Acceleration: [0.81, 0.52, 0.61, 0.17, -0.04, -0.05, -1.02]\n Course: [0.0, 0.12, 0.5, -0.15, -0.34, 0.42, -0.12]\nWhat is the action of ego car?\nAssistant: The car drives down the road\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear.\n\n",
+    "BDDX_Processed/video/11e60919-1e8fb434_12725.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.52, 1.62, 1.74, 1.89, 1.63, 1.32, 1.08]\n Curvature: [0.0, 0.0, -0.03, -0.1, -0.1, -0.1, -0.13]\n Acceleration: [0.15, 0.18, 0.1, -0.11, -0.31, -0.42, -0.37]\n Course: [0.0, 0.61, 1.05, 1.05, 1.03, 0.76, 0.76]\nWhat is the action of ego car?\nAssistant: The car slows down at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: to allow pedestrians to cross.\n\n",
+    "BDDX_Processed/video/192792cc-6d0a0709_16628.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.48, 9.02, 9.38, 9.46, 9.49, 9.53, 9.61]\n Curvature: [-0.01, 0.02, 0.02, -0.0, -0.01, -0.02, -0.03]\n Acceleration: [0.55, 0.51, 0.37, 0.11, 0.08, 0.09, 0.12]\n Course: [0.0, 0.05, 0.0, 0.0, 0.12, 0.05, 0.06]\nWhat is the action of ego car?\nAssistant: The car continues down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/1ca994b0-9dd6ee6e_18154.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.26, 18.99, 19.2, 19.88, 20.09, 20.05, 19.89]\n Curvature: [0.0, 0.0, -0.0, 0.0, 0.01, -0.0, -0.01]\n Acceleration: [0.49, 0.12, 0.25, 0.24, 0.04, -0.07, -0.15]\n Course: [0.0, 0.04, -0.15, -0.32, 0.07, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road ahead is clear.\n\n",
+    "BDDX_Processed/video/20652cf8-15dd5dfa_20045.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.29, 0.28, 0.27, 0.26, 0.25, 0.18, 0.12]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.09, -0.11, -0.12, -0.13, -0.14, -0.14, -0.13]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic ahead is not moving.\n\n",
+    "BDDX_Processed/video/15eb3650-16b6a9ca_14779.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.37, 3.16, 5.86, 7.8, 10.08, 12.58, 14.39]\n Curvature: [0.02, -0.0, -0.05, 0.01, 0.0, 0.0, 0.0]\n Acceleration: [0.1, 1.06, 1.13, 1.15, 1.16, 1.28, 0.47]\n Course: [0.0, -0.64, 0.0, 1.15, 0.0, 0.0, -0.35]\nWhat is the action of ego car?\nAssistant: The car drives faster\n\nHuman: Why does the ego car doing this?\nAssistant: as it goes straight down the empty road\n\n",
+    "BDDX_Processed/video/054575c4-e461764c_00239.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.02, 0.14, 0.26]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.06, 0.12, 0.18, 0.24, 0.2, 0.17]\n Course: [0.0, 0.0, 0.0, 0.0, -1.94, -3.88, -1.94]\nWhat is the action of ego car?\nAssistant: The car moves forward slightly\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front moved forward.\n\n",
+    "BDDX_Processed/video/18f3b2d1-8127aacc_16475.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.4, 1.06, 0.36, 0.41, 0.48, 0.46, 0.24]\n Curvature: [0.04, 0.19, 0.54, 1.15, 0.26, -0.25, 0.25]\n Acceleration: [-2.94, -1.68, -0.67, 0.03, -0.02, -0.08, 0.02]\n Course: [0.0, 0.11, 0.06, 0.06, 0.28, 0.17, 0.17]\nWhat is the action of ego car?\nAssistant: The car pulls slightly to the left\n\nHuman: Why does the ego car doing this?\nAssistant: to see if he can pass the car in front of him.\n\n",
+    "BDDX_Processed/video/067f3361-39097fb1_06627.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.9, 14.48, 12.42, 12.6, 11.97, 12.73, 12.76]\n Curvature: [0.0, -0.0, 0.01, -0.0, -0.02, 0.0, -0.0]\n Acceleration: [0.47, -0.17, -0.65, 0.24, -0.18, 0.37, -0.06]\n Course: [0.0, -0.47, -0.16, 0.23, 0.0, -0.23, 0.32]\nWhat is the action of ego car?\nAssistant: The car travels down a city road at night keeping even distance with the car in front\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is flowing smoothly.\n\n",
+    "BDDX_Processed/video/11318349-2868691f_12319.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.92, 17.08, 16.25, 15.63, 15.33, 15.02, 14.74]\n Curvature: [0.0, 0.01, 0.01, 0.01, 0.01, 0.0, 0.0]\n Acceleration: [-1.31, -1.37, -1.44, -1.36, -1.08, -0.8, -0.74]\n Course: [0.0, -0.03, -0.03, -0.14, -0.14, -0.14, -0.07]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning off the highway.\n\n",
+    "BDDX_Processed/video/0f4fb685-0cc41310_11385.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.44, 11.24, 11.97, 12.39, 12.29, 12.19, 11.39]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [2.49, 1.84, 1.25, 0.5, -0.39, -1.28, -1.85]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/0a0eebd9-9f8ba3ef_08651.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because it has finished parking.\n\n",
+    "BDDX_Processed/video/1accee17-e4c1e644_17437.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: since it is parked on the side of the street.\n\n",
+    "BDDX_Processed/video/00091078-f32de4d2_00250.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.17, -0.08, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: Traffic ahead of the car is stopped.\n\n",
+    "BDDX_Processed/video/1f0fff77-eb02ae10_19337.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.12, 16.44, 16.55, 16.41, 16.3, 16.18, 16.08]\n Curvature: [-0.0, -0.0, 0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.85, 0.4, 0.04, -0.18, -0.18, -0.17, -0.19]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.01, -0.01]\nWhat is the action of ego car?\nAssistant: The car drives\n\nHuman: Why does the ego car doing this?\nAssistant: since the lane is clear.\n\n",
+    "BDDX_Processed/video/115d9e12-8a9ad671_12478.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.13, 0.08, 0.02, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-3.35, 0.05, -0.09, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/156dcd52-aecaa59c_14525.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.49, 0.39, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [1.01, 0.25, 0.21, 0.18, 0.14, 0.1, 0.06]\n Acceleration: [-0.09, -0.14, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stationary\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/014e76d7-a0c222bc_03229.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.13, 4.48, 4.12, 4.1, 4.42, 4.82, 5.31]\n Curvature: [-0.0, 0.02, 0.21, 0.61, 0.58, 0.42, 0.12]\n Acceleration: [-1.4, -0.83, -0.29, 0.21, 0.49, 0.7, 0.81]\n Course: [0.0, 0.0, -7.98, -7.98, -3.06, 54.66, 54.66]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: to make a left turn.\n\n",
+    "BDDX_Processed/video/20ff37cb-c1f75833_20334.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.58, 9.13, 6.47, 4.7, 3.8, 2.25, 1.99]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.05, -1.45, -1.84, -1.23, -0.95, -0.92, -0.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car gradually slows to a stop.\n\nHuman: Why does the ego car doing this?\nAssistant: Because the car ahead of it is stopped.\n\n",
+    "BDDX_Processed/video/028b5d16-be6486a6_02076.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [24.32, 23.93, 23.98, 24.33, 24.89, 25.31, 25.23]\n Curvature: [-0.02, 0.03, -0.02, -0.05, 0.02, 0.04, 0.0]\n Acceleration: [-0.59, -0.21, 0.23, 0.55, 0.58, 0.23, -0.64]\n Course: [0.0, 0.0, 1.68, 0.0, -0.31, -0.51, -0.46]\nWhat is the action of ego car?\nAssistant: The car veers right\n\nHuman: Why does the ego car doing this?\nAssistant: to enter the new lane.\n\n",
+    "BDDX_Processed/video/011aeca1-fe5af665_02874.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.31, 12.67, 10.89, 8.92, 6.91, 5.65, 4.19]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-1.76, -2.69, -3.6, -3.41, -3.22, -3.13, -2.96]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/150c0f61-39d07de7_14223.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.64, 9.44, 7.76, 5.32, 3.0, 2.61, 2.32]\n Curvature: [-0.01, 0.0, 0.01, -0.0, -0.02, 0.01, 0.01]\n Acceleration: [-0.04, -1.32, -2.46, -2.77, -1.74, -0.59, -0.42]\n Course: [0.0, -0.15, 0.06, 0.36, 0.22, 0.07, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead has stopped.\n\n",
+    "BDDX_Processed/video/029ad1d1-8c9b02b1_02195.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.17, 13.43, 12.65, 11.42, 10.26, 8.99, 7.87]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.02, -0.33, -0.53]\n Acceleration: [-0.92, -1.43, -1.93, -2.17, -2.36, -1.98, -1.7]\n Course: [0.0, 0.0, 0.0, 0.0, 1.12, 4.48, -28.36]\nWhat is the action of ego car?\nAssistant: The car swerves left\n\nHuman: Why does the ego car doing this?\nAssistant: to get around slow vehicles in front of it.\n\n",
+    "BDDX_Processed/video/0ac3cbf4-07c64adc_09010.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.13, 0.96, 1.1, 1.15, 0.91, 0.58, 0.3]\n Curvature: [1.2, 0.54, 0.33, 0.15, -0.78, -2.15, -2.63]\n Acceleration: [0.34, 0.05, 0.09, -0.1, -0.3, -0.34, -0.0]\n Course: [0.0, -2.31, -2.87, -28.76, 16.12, -2.32, 1.48]\nWhat is the action of ego car?\nAssistant: The car begins to back up\n\nHuman: Why does the ego car doing this?\nAssistant: as it moves backward into the cross street\n\n",
+    "BDDX_Processed/video/1e15eaab-f4b4838a_18913.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.5, 9.19, 8.91, 8.22, 6.73, 5.25, 4.81]\n Curvature: [-0.01, -0.05, -0.08, -0.08, 0.01, 0.09, 0.07]\n Acceleration: [-0.43, -1.14, -1.78, -2.24, -2.21, -2.18, -1.42]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.19]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has slowed down.\n\n",
+    "BDDX_Processed/video/1c71863d-a34af0f4_18102.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.15, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-1.42, -1.42, -1.42, -1.42, -1.42, -1.43, -1.43]\n Acceleration: [-1.38, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0f6ccc0d-943bb2d8_11405.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.59, 7.49, 9.25, 10.68, 10.83, 9.83, 10.04]\n Curvature: [-0.0, -0.01, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.11, 1.19, 1.0, 0.62, -0.44, -0.3, 0.47]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.15, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving forward again at normal speed.\n\nHuman: Why does the ego car doing this?\nAssistant: because the in front is moving normally again and the car from the right lane has merged.\n\n",
+    "BDDX_Processed/video/1508a138-e7785677_14206.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped ahead.\n\n",
+    "BDDX_Processed/video/06c58952-170a4d8f_06688.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car parks on the street\n\nHuman: Why does the ego car doing this?\nAssistant: because there is ample parking.\n\n",
+    "BDDX_Processed/video/06d54ae6-bf7ed032_00031.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.48, 9.65, 10.43, 10.79, 11.44, 10.85, 10.75]\n Curvature: [0.0, 0.0, 0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.18, 0.17, 0.49, 0.2, 0.01, -0.17, -0.98]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.22, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: The car has no obstructions and is moving forward.\n\n",
+    "BDDX_Processed/video/031637c4-d2a9e7f0_04151.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.05, -0.05, -0.05, -0.05, -0.05, -0.05, -0.05]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the driver is waiting for something.\n\n",
+    "BDDX_Processed/video/0478d053-f5bb4831_06019.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.15]\n Curvature: [0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.12]\n Acceleration: [0.0, 0.0, 0.0, 0.04, 0.12, 0.21, 0.55]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the car ahead is stopped.\n\n",
+    "BDDX_Processed/video/16269211-6ffb8bde_14957.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.28, 0.28, 0.28, 0.65, 1.91, 3.78]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.07, 0.09, 0.08, 0.34, 1.1, 1.93, 1.99]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/14eb5976-61a0c5c2_14142.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.71, 19.38, 19.56, 20.2, 20.69, 20.72, 20.81]\n Curvature: [-0.01, -0.04, -0.04, -0.04, -0.03, 0.04, 0.08]\n Acceleration: [-0.67, -0.08, 0.34, 0.53, 0.21, 0.04, 0.13]\n Course: [0.0, -0.12, 0.32, 0.53, 0.84, 0.74, 0.53]\nWhat is the action of ego car?\nAssistant: The car slows down a bit\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is getting a little bumpy.\n\n",
+    "BDDX_Processed/video/099d8913-48b371ce_08252.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.33, 10.07, 9.8, 9.54, 9.31, 9.04, 8.76]\n Curvature: [0.01, 0.01, 0.01, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.77, -0.84, -0.9, -0.97, -1.03, -0.94, -0.86]\n Course: [0.0, -0.12, -0.12, -0.12, -0.06, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches slower traffic in front of it.\n\n",
+    "BDDX_Processed/video/061c075c-3a32cdce_06480.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [29.73, 32.68, 32.8, 31.49, 31.01, 29.41, 30.22]\n Curvature: [-0.0, -0.0, 0.01, -0.01, 0.0, 0.0, -0.02]\n Acceleration: [0.58, 0.31, 0.22, -0.14, -0.11, -0.33, 0.34]\n Course: [0.0, 1.57, 1.36, 0.61, 0.0, 0.16, 0.7]\nWhat is the action of ego car?\nAssistant: The car is driving on the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic is moving steadily.\n\n",
+    "BDDX_Processed/video/19798cae-9790e91f_16706.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.36, 0.29, 0.21, 0.1, 0.0, 0.0, 0.0]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]\n Acceleration: [-0.14, -0.16, -0.18, -0.14, -0.1, -0.04, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/094a88ce-8a74ed19_08147.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.51, 13.52, 13.53, 13.31, 12.7, 12.15, 10.93]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.16, -0.17, -0.51, -0.98, -1.7, -2.37, -2.43]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges into the lane to its right\n\nHuman: Why does the ego car doing this?\nAssistant: because that lane has no traffic.\n\n",
+    "BDDX_Processed/video/0d23910c-89a32a86_10443.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.62, 2.33, 3.89, 5.24, 6.51, 7.63, 8.43]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.42, 1.92, 1.9, 1.69, 1.5, 1.23, 0.87]\n Course: [0.0, 0.0, 0.0, 0.0, -0.07, -0.11, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates and drives down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear ahead.\n\n",
+    "BDDX_Processed/video/025f49ef-0f6366f3_01692.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.04, 6.01, 5.98, 5.94, 5.88, 5.53, 5.17]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.46, 0.14, -0.18, -0.5, -0.76, -0.92, -1.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because cars in front of it are stopped.\n\n",
+    "BDDX_Processed/video/0b01ca80-852faffa_09140.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.73, 8.77, 8.8, 8.84, 8.85, 8.67, 8.52]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.69, 0.44, 0.22, -0.04, -0.28, -0.42, -0.54]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in front of it moves freely.\n\n",
+    "BDDX_Processed/video/09e2ff07-a0c1d501_08551.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.13, 11.66, 11.23, 10.85, 10.6, 10.35, 9.5]\n Curvature: [-0.0, -0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.97, -0.94, -0.92, -1.02, -1.3, -1.58, -2.12]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04]\nWhat is the action of ego car?\nAssistant: The car is merging into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it has no traffic.\n\n",
+    "BDDX_Processed/video/100a3dd7-edc04608_11676.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.39, 13.41, 13.53, 13.77, 13.09, 11.79, 9.74]\n Curvature: [0.01, 0.01, 0.0, -0.0, -0.01, -0.01, 0.01]\n Acceleration: [-0.23, 0.05, 0.01, -0.4, -1.46, -2.4, -2.81]\n Course: [0.0, 0.05, -0.18, -0.18, 0.05, -0.06, -0.06]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: The car in front has slowed down for a light.\n\n",
+    "BDDX_Processed/video/023d0f3c-564e6d31_01287.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.97, 5.74, 4.4, 3.42, 2.74, 2.11, 2.65]\n Curvature: [0.01, -0.0, -0.02, -0.02, -0.03, -0.03, -0.01]\n Acceleration: [-5.11, -4.13, -3.06, -2.07, -0.98, 0.03, 0.72]\n Course: [0.0, 0.16, 0.16, 0.07, 0.09, 0.07, -0.1]\nWhat is the action of ego car?\nAssistant: The car slows to a rolling stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/1cc77c32-f18a71c4_18234.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.38, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped behind a line of traffic\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a red light.\n\n",
+    "BDDX_Processed/video/09b80f74-c763ee9b_08470.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.11, 7.24, 7.39, 7.5, 7.58, 7.67, 7.5]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.35, 0.33, 0.3, 0.21, 0.04, -0.14, -0.28]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating and drifting to the left\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light is green and there is a car just ahead stopped in front of it with its hazard lights flashing and blocking the lane so it has to drive into the opposing traffic's lane to go around the stopped car.\n\n",
+    "BDDX_Processed/video/0710389f-038050ae_00815.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.85, 6.75, 6.93, 6.49, 5.52, 4.76, 4.52]\n Curvature: [0.0, 0.0, -0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.78, 0.44, -0.13, -0.55, -0.76, -0.33, -0.13]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car maintains a slow speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving slow, but smoothly.\n\n",
+    "BDDX_Processed/video/0104eb19-162abfea_02551.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: at the red light.\n\n",
+    "BDDX_Processed/video/15569b20-48e496f5_14430.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.02, 0.21, 0.09]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.04, 0.11, 0.02, -0.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car tentatively moves a slight bit forward.\n\nHuman: Why does the ego car doing this?\nAssistant: as the crosswalk is practically empty of people.\n\n",
+    "BDDX_Processed/video/014e76d7-afb481a3_04729.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.92, 9.1, 9.29, 9.27, 9.02, 8.74, 8.08]\n Curvature: [0.01, 0.0, -0.0, 0.0, 0.01, 0.03, -0.01]\n Acceleration: [0.78, 0.44, 0.08, -0.31, -0.76, -1.25, -1.21]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.04]\nWhat is the action of ego car?\nAssistant: The car drifts left into the opposing traffic's lane\n\nHuman: Why does the ego car doing this?\nAssistant: Because there is a car parked ahead of it in the middle of the street with its hazard lights on blocking the lane.\n\n",
+    "BDDX_Processed/video/18f5e81b-06ec3cfb_16482.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.17, 19.04, 18.93, 18.8, 18.64, 18.27, 17.95]\n Curvature: [-0.02, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01]\n Acceleration: [-0.19, -0.39, -0.56, -0.76, -0.96, -1.16, -1.34]\n Course: [0.0, 0.07, 0.04, 0.07, 0.1, 0.13, 0.06]\nWhat is the action of ego car?\nAssistant: The car pulls into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it's going to make a turn.\n\n",
+    "BDDX_Processed/video/0521fd41-40219907_04237.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.81, 1.44, 0.99, 1.07, 1.91, 2.15]\n Curvature: [0.0, -0.71, -0.12, 0.09, 0.02, 0.05, 0.05]\n Acceleration: [0.0, 0.56, -0.04, -0.18, 0.12, 0.48, -0.52]\n Course: [0.0, 0.85, 0.8, 1.2, 0.64, 0.41, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because it is slowed by the traffic in front of it.\n\n",
+    "BDDX_Processed/video/08b77e05-932ca5cf_07852.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.03, 1.09, 1.83, 3.15, 4.73, 5.79, 6.59]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.06, 0.46, 1.1, 1.6, 1.44, 1.08, 0.94]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/19167d13-f3f73ea4_16574.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/090f9b33-776dbf8b_08011.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.25, 2.84, 3.1, 3.83, 5.61, 6.84, 7.15]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, 0.0, -0.0]\n Acceleration: [-0.09, -0.01, 0.26, 0.97, 0.81, 0.54, -0.37]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car picks up speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/0b5b81ee-ef0e938d_09306.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.13, 6.84, 7.59, 8.4, 9.21, 7.85, 7.36]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0]\n Acceleration: [0.29, 0.34, 0.24, 0.4, -0.01, -0.55, -0.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.38, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is clear and traffic is moving.\n\n",
+    "BDDX_Processed/video/1710645f-e6d0c9c8_15581.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.06, 6.45, 8.31, 9.85, 10.63, 9.42, 9.89]\n Curvature: [0.0, -0.02, 0.01, 0.03, -0.01, 0.01, -0.01]\n Acceleration: [0.83, 1.4, 0.42, 0.57, 0.14, -0.28, -0.01]\n Course: [0.0, -1.76, -0.23, -0.19, 0.19, 0.19, -0.04]\nWhat is the action of ego car?\nAssistant: The car is driving down a city street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street ahead is clear.\n\n",
+    "BDDX_Processed/video/025b2ded-0f6cbb6b_01522.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.33, 4.82, 5.37, 6.08, 6.84, 7.56, 8.06]\n Curvature: [-0.36, -0.36, -0.34, -0.16, -0.0, -0.0, 0.0]\n Acceleration: [1.25, 1.23, 1.21, 1.33, 1.35, 0.67, -0.07]\n Course: [0.0, 5.86, 5.86, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: to make a right turn.\n\n",
+    "BDDX_Processed/video/1aa857d4-9415d6a4_17259.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.31, 5.16, 4.7, 3.9, 3.1, 2.32, 1.46]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.23, -0.56, -0.89, -1.2, -1.24, -1.23, -1.2]\n Course: [0.0, 0.0, -0.03, -0.03, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop on the right side of the street\n\nHuman: Why does the ego car doing this?\nAssistant: since there is an open area to park.\n\n",
+    "BDDX_Processed/video/1d2090ea-1274f7c1_18346.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.85, 11.59, 11.36, 11.1, 10.82, 10.38, 10.0]\n Curvature: [0.04, 0.02, -0.0, -0.02, -0.03, -0.03, -0.02]\n Acceleration: [-0.73, -0.88, -1.02, -1.17, -1.32, -1.41, -1.49]\n Course: [0.0, 0.02, 0.01, 0.02, -0.0, -0.02, -0.01]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic ahead is stopped at a red light.\n\n",
+    "BDDX_Processed/video/17662c87-d2526f74_15735.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.27, 17.17, 17.07, 17.04, 17.0, 16.89, 16.76]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.09, -0.11, -0.13, -0.13, -0.15, -0.24, -0.33]\n Course: [0.0, 0.14, 0.11, 0.04, 0.01, -0.05, -0.08]\nWhat is the action of ego car?\nAssistant: The car pulls into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it's going to turn down the road.\n\n",
+    "BDDX_Processed/video/01306b58-cf6faad9_04700.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.13, 10.25, 10.48, 9.95, 8.36, 7.53, 9.48]\n Curvature: [0.0, 0.01, 0.0, 0.0, 0.0, 0.0, -0.03]\n Acceleration: [-0.22, -0.34, 0.43, -0.63, -0.59, 0.1, 0.76]\n Course: [0.0, -0.54, 0.0, 1.08, -0.26, -1.04, 1.29]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is behind the vehicle.\n\n",
+    "BDDX_Processed/video/00a820ef-d655700e_02043.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.37, 10.05, 8.34, 6.85, 5.8, 5.41, 5.4]\n Curvature: [0.0, -0.0, 0.0, 0.0, 0.0, 0.04, 0.21]\n Acceleration: [-1.41, -1.88, -1.96, -1.61, -0.94, -0.31, 0.24]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -10.5]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: to make a left turn.\n\n",
+    "BDDX_Processed/video/0e7ecff9-34fbb2d7_11031.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.47, 10.28, 10.07, 9.86, 9.95, 10.04, 10.07]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [-0.34, -0.32, -0.24, -0.09, 0.04, 0.11, 0.11]\n Course: [0.0, 0.0, 0.16, 0.13, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/0183d6f9-806f64bd_04808.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.93, 4.22, 4.8, 5.72, 6.51, 7.29, 8.13]\n Curvature: [-0.02, -0.01, -0.0, -0.0, -0.0, -0.0, -0.01]\n Acceleration: [-0.01, 0.6, 1.04, 1.27, 1.25, 1.26, 1.36]\n Course: [0.0, 0.45, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues to move on forward\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is seen and the car can safely move onward.\n\n",
+    "BDDX_Processed/video/162d8d65-96bf071f_14990.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.12, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/201c531c-1b3dceed_19954.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01]\n Acceleration: [-1.18, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is at a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the street light is red.\n\n",
+    "BDDX_Processed/video/02065c11-0966f65d_00576.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.97, 8.98, 9.05, 9.18, 9.18, 9.05, 8.63]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.14, 0.12, 0.11, 0.1, -0.15, -0.41, -0.72]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/0711ab13-56b31255_00914.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.25, 3.06, 2.88, 2.69, 2.53, 2.5, 2.48]\n Curvature: [0.02, 0.03, 0.03, 0.04, 0.04, 0.03, 0.01]\n Acceleration: [-1.03, -0.87, -0.71, -0.55, -0.39, -0.23, -0.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slows to a near stop\n\nHuman: Why does the ego car doing this?\nAssistant: because cars are stopped in front of it after the light turned green.\n\n",
+    "BDDX_Processed/video/1380820f-7f92f23a_13571.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.11]\nWhat is the action of ego car?\nAssistant: The car is stationary\n\nHuman: Why does the ego car doing this?\nAssistant: since there is a large truck stopped in front of it.\n\n",
+    "BDDX_Processed/video/167bc8b8-32d59126_15154.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.15, 1.06, 0.94, 1.03, 1.47, 1.28, 0.54]\n Curvature: [-0.02, -0.01, 0.01, -0.01, -0.04, -0.06, -0.01]\n Acceleration: [-0.08, -0.08, -0.04, 0.25, 0.13, -0.45, -0.6]\n Course: [0.0, -0.06, 0.0, 0.0, 0.0, 0.1, -0.11]\nWhat is the action of ego car?\nAssistant: The car is moving slowly ahead\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving forward slowly.\n\n",
+    "BDDX_Processed/video/109d5675-ccf79e0e_11855.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.55, 13.82, 14.28, 14.7, 15.26, 15.34, 15.15]\n Curvature: [0.01, 0.01, -0.0, -0.0, -0.0, 0.01, 0.0]\n Acceleration: [0.55, 0.45, 0.25, 0.23, 0.29, -0.08, -0.18]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.04, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward at normal speed.\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/11502c6d-f4834c96_12391.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked.\n\n",
+    "BDDX_Processed/video/11e60919-a44a764e_12730.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.56, 2.53, 2.42, 2.42, 2.64, 3.14, 3.78]\n Curvature: [0.0, 0.03, 0.28, 0.39, 0.21, 0.04, 0.08]\n Acceleration: [-0.04, -0.09, -0.03, 0.16, 0.46, 0.73, 0.79]\n Course: [0.0, -1.23, -4.36, -6.47, -5.57, -6.82, -3.67]\nWhat is the action of ego car?\nAssistant: The car is turning left\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no oncoming traffic\n\n",
+    "BDDX_Processed/video/047e732b-aa79a87d_03380.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.21, 12.35, 13.41, 12.2, 13.75, 14.34, 14.52]\n Curvature: [0.0, 0.01, 0.02, -0.01, 0.01, 0.0, 0.01]\n Acceleration: [-0.39, 0.37, -0.34, 0.02, 0.72, -0.07, 0.13]\n Course: [0.0, -0.95, -1.17, -0.68, -0.41, -0.82, -0.87]\nWhat is the action of ego car?\nAssistant: The car is going down the highway at a steady speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady pace.\n\n",
+    "BDDX_Processed/video/0e711991-1e7bf684_10981.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.09, 5.68, 6.27, 6.89, 7.56, 8.24, 8.91]\n Curvature: [0.48, 0.3, 0.11, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.42, 1.5, 1.58, 1.65, 1.69, 1.73, 1.56]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01]\nWhat is the action of ego car?\nAssistant: The car is turning left\n\nHuman: Why does the ego car doing this?\nAssistant: to enter another road.\n\n",
+    "BDDX_Processed/video/0d1fc580-61bbac5d_10369.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.23, 2.35, 2.47, 2.63, 2.85, 3.07, 3.25]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.22, 0.3, 0.38, 0.45, 0.48, 0.52, 0.61]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drifts left\n\nHuman: Why does the ego car doing this?\nAssistant: because it is trying to pass the car in front of it and there is a bit of room opening up on the left.\n\n",
+    "BDDX_Processed/video/0c648d70-2fb6ee12_09846.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.05, 9.63, 9.99, 10.23, 9.55, 9.97, 9.81]\n Curvature: [0.0, 0.0, -0.0, 0.0, 0.01, -0.0, -0.0]\n Acceleration: [0.6, 0.44, 0.11, -0.43, 0.25, -0.09, 0.01]\n Course: [0.0, -0.08, 0.0, 0.0, -0.29, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving at normal speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear and there is no traffic.\n\n",
+    "BDDX_Processed/video/07919504-3a57c8d3_07207.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.48, 1.35, 3.17, 4.8, 5.89, 6.91, 7.9]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.45, 0.94, 1.47, 0.93, 0.83, 0.76, 0.73]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues to move on forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has begun moving forward.\n\n",
+    "BDDX_Processed/video/018d962a-fd869a57_04831.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.16, 0.86, 0.89, 0.85]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.18]\n Acceleration: [0.0, 0.0, 0.05, 0.37, 0.37, -0.08, 0.85]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning left\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turned green.\n\n",
+    "BDDX_Processed/video/038db692-9c25743b_05593.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.92, 10.03, 10.15, 10.26, 10.37, 10.56, 10.76]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.16, 0.28, 0.39, 0.5, 0.59, 0.63, 0.67]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves back slightly to the left\n\nHuman: Why does the ego car doing this?\nAssistant: after the other car has gone past\n\n",
+    "BDDX_Processed/video/195f33f4-073853d8_16676.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.51, 3.63, 3.78, 4.0, 4.29, 4.92, 5.56]\n Curvature: [-0.0, -0.28, -0.55, -0.34, -0.13, -0.06, 0.0]\n Acceleration: [0.15, 0.25, 0.37, 0.62, 0.87, 1.02, 1.13]\n Course: [0.0, 0.0, -8.93, -26.78, -17.85, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car makes a right turn\n\nHuman: Why does the ego car doing this?\nAssistant: because it has the right of way.\n\n",
+    "BDDX_Processed/video/02364369-bd3d1838_01209.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.71, 7.19, 6.84, 6.42, 5.85, 5.49, 5.28]\n Curvature: [0.02, -0.02, -0.06, -0.04, 0.04, -0.01, -0.01]\n Acceleration: [-1.26, -0.68, -0.55, -0.59, -0.58, -0.38, -0.2]\n Course: [0.0, 0.95, 0.64, 0.44, 0.07, -0.29, 0.12]\nWhat is the action of ego car?\nAssistant: The car moving forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: The car is approaching an intersection\n\n",
+    "BDDX_Processed/video/021c4a16-1c50e818_00827.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.48, 7.17, 6.91, 6.6, 6.33, 6.04, 5.75]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.67, -0.79, -0.9, -1.01, -1.11, -1.13, -1.16]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a rolling stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/1aa857d4-a6f9e781_17264.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.04, 1.38, 1.38, 1.49, 1.84, 2.88, 3.62]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [-1.01, -0.23, 0.04, 0.15, 0.52, 0.76, 0.15]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates as traffic is advancing\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turned green.\n\n",
+    "BDDX_Processed/video/1f4b8e7d-e6d19fea_19499.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.32, 7.01, 6.74, 6.42, 6.11, 5.81, 5.54]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-1.1, -1.12, -1.13, -1.15, -1.15, -1.08, -1.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues straight\n\nHuman: Why does the ego car doing this?\nAssistant: as it keeps up with another car\n\n",
+    "BDDX_Processed/video/1d8ee15c-a64eb02d_18639.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.58, 10.84, 11.08, 11.28, 11.38, 11.48, 10.73]\n Curvature: [-0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01]\n Acceleration: [0.68, 0.6, 0.52, 0.25, -0.4, -1.04, -1.63]\n Course: [0.0, 0.19, 0.12, 0.21, 0.21, 0.21, 0.15]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is empty.\n\n",
+    "BDDX_Processed/video/05be3c16-e85868fd_01406.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.99, 9.86, 9.74, 9.61, 9.43, 8.97, 8.56]\n Curvature: [-0.01, -0.0, 0.0, 0.01, 0.01, 0.01, 0.0]\n Acceleration: [-0.35, -0.56, -0.73, -0.94, -1.15, -1.39, -1.61]\n Course: [0.0, 0.0, 0.0, 0.0, 0.07, 0.14, 0.07]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: due to free flowing traffic.\n\n",
+    "BDDX_Processed/video/0308ce2b-8fc462fd_05443.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.99, 6.22, 3.85, 2.82, 1.47, 1.67, 2.08]\n Curvature: [-0.0, 0.04, 0.67, 0.29, 0.11, -0.01, -0.0]\n Acceleration: [-1.64, -2.03, -1.61, -1.17, -0.57, 0.29, 0.68]\n Course: [0.0, 75.26, -5.34, -9.27, -6.31, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing and turning left\n\nHuman: Why does the ego car doing this?\nAssistant: to enter another road.\n\n",
+    "BDDX_Processed/video/0f837def-7fbedddc_11445.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.19, 0.3, 0.12, 1.39, 0.78, 1.28]\n Curvature: [-0.26, -5.55, -2.33, -0.08, -0.03, 0.03, 0.21]\n Acceleration: [0.0, 0.08, 0.02, 0.12, 0.15, -0.24, -0.08]\n Course: [0.0, -0.25, 0.45, 0.0, -0.89, 0.29, 0.79]\nWhat is the action of ego car?\nAssistant: The car is edging forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: as it is stuck in heavy stop and go traffic.\n\n",
+    "BDDX_Processed/video/15345d9b-0162432e_14333.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.52, 14.76, 14.88, 15.19, 15.61, 16.02, 15.49]\n Curvature: [0.0, 0.01, 0.01, 0.01, 0.0, 0.0, -0.01]\n Acceleration: [0.16, 0.14, 0.12, 0.28, 0.3, 0.09, -0.87]\n Course: [0.0, -0.64, -0.37, -0.32, -0.12, -0.06, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road ahead is clear.\n\n",
+    "BDDX_Processed/video/0d0e69b6-a30b3acb_10306.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.23, 7.55, 7.9, 8.2, 8.91, 9.78, 10.68]\n Curvature: [0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.55, -0.07, 0.22, 0.28, 0.47, 0.59, 0.14]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives down a road\n\nHuman: Why does the ego car doing this?\nAssistant: as the road ahead is clear.\n\n",
+    "BDDX_Processed/video/03a08882-3c5b8923_00987.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.44, 4.68, 3.98, 3.34, 2.77, 2.36, 2.07]\n Curvature: [-0.0, -0.04, -0.07, -0.01, 0.03, -0.0, -0.02]\n Acceleration: [-1.42, -1.36, -1.3, -1.12, -0.94, -0.59, -0.15]\n Course: [0.0, 0.09, 0.08, 0.05, 0.06, 0.09, -0.05]\nWhat is the action of ego car?\nAssistant: The car moves slowly\n\nHuman: Why does the ego car doing this?\nAssistant: to go over a bump\n\n",
+    "BDDX_Processed/video/0e7ecff9-761fe466_11037.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked.\n\n",
+    "BDDX_Processed/video/0cd7b5f1-21b1e324_10098.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.12, 6.06, 4.93, 3.53, 3.28, 2.94, 1.92]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.86, -1.05, -1.24, -0.8, -0.27, -0.66, -0.78]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down to stop at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: since there is a stop sign.\n\n",
+    "BDDX_Processed/video/036f3b5f-ee1521de_00370.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.76, 2.55, 2.37, 2.16, 1.99, 2.09, 2.17]\n Curvature: [-0.01, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.33, -1.03, -0.77, -0.48, -0.18, 0.07, 0.29]\n Course: [0.0, 0.05, 0.03, 0.05, 0.03, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: as the car in front of it brakes.\n\n",
+    "BDDX_Processed/video/09b80f74-c763ee9b_08473.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.2, 8.2, 8.2, 8.2, 8.2, 8.06, 7.93]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.21, 0.09, -0.01, -0.14, -0.24, -0.38, -0.52]\n Course: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: Because it is preparing to make a U-Turn.\n\n",
+    "BDDX_Processed/video/21f4edde-9b70fa56_20769.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.09, 9.87, 10.66, 11.06, 10.73, 10.34, 8.86]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.92, 0.76, 0.32, 0.11, -0.3, -0.66, -1.17]\n Course: [0.0, 0.02, 0.0, 0.0, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car speeds up and continues down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/19776f37-ccaf551e_16700.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.87, 7.15, 6.16, 4.86, 4.37, 3.98, 3.69]\n Curvature: [-0.13, -0.09, -0.06, -0.03, -0.02, -0.02, -0.01]\n Acceleration: [-0.47, -1.18, -1.5, -1.35, -0.91, -0.52, -0.39]\n Course: [0.0, 2.02, 0.79, 0.79, 0.6, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car reduces its speed\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in the lane ahead has slowed.\n\n",
+    "BDDX_Processed/video/05584a52-590d4e59_06264.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.99, 7.86, 7.7, 7.49, 7.27, 7.07, 6.82]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.26, -0.3, -0.35, -0.38, -0.43, -0.59, -0.78]\n Course: [0.0, 0.0, 0.05, 0.1, 0.02, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the light was red.\n\n",
+    "BDDX_Processed/video/0070bc56-401304be_01370.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.58, 7.68, 7.77, 7.87, 7.99, 8.3, 8.6]\n Curvature: [-0.01, -0.01, -0.01, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.2, 0.4, 0.6, 0.79, 0.9, 1.0]\n Course: [0.0, 0.11, 0.11, 0.11, 0.06, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving slightly to the left\n\nHuman: Why does the ego car doing this?\nAssistant: The car is avoiding a pedestrian in the roadway.\n\n",
+    "BDDX_Processed/video/0501cda3-5c939ca7_06152.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.69, 9.99, 10.25, 10.56, 10.82, 10.96, 11.11]\n Curvature: [-0.01, -0.01, -0.01, -0.01, -0.01, -0.05, -0.1]\n Acceleration: [1.41, 1.25, 1.12, 0.96, 0.83, 0.54, 0.25]\n Course: [0.0, -0.14, -0.14, -0.14, -0.07, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car is coasting\n\nHuman: Why does the ego car doing this?\nAssistant: As the driver is preparing to decelerate in anticipation of an upcoming speed bump.\n\n",
+    "BDDX_Processed/video/06e4a00e-346390c8_06780.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.76, 7.1, 6.43, 5.77, 5.18, 4.43, 3.68]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-1.95, -2.15, -2.35, -2.55, -2.67, -2.51, -2.35]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins to slow down\n\nHuman: Why does the ego car doing this?\nAssistant: because it is approaching an intersection\n\n",
+    "BDDX_Processed/video/015af63d-df1ef690_03311.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.12, 11.14, 11.16, 11.19, 11.21, 11.19, 11.17]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.12, 0.09, 0.06, 0.03, 0.01, -0.03, -0.06]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is merging left\n\nHuman: Why does the ego car doing this?\nAssistant: to pass a slow moving police car\n\n",
+    "BDDX_Processed/video/212f5e63-fe607059_20416.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.86, 10.58, 11.07, 12.08, 12.9, 13.05, 12.68]\n Curvature: [-0.0, 0.0, -0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.2, 0.18, 0.78, 0.98, 0.53, -0.1, -0.61]\n Course: [0.0, 0.0, 0.0, 0.0, -0.18, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates down the road\n\nHuman: Why does the ego car doing this?\nAssistant: to beat a yellow light.\n\n",
+    "BDDX_Processed/video/10739276-9131b28a_11832.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.89, 3.02, 4.15, 5.16, 5.98, 6.68, 7.32]\n Curvature: [-0.0, -0.0, -0.0, -0.01, -0.01, -0.0, -0.0]\n Acceleration: [1.39, 1.58, 1.6, 1.41, 1.2, 1.02, 0.87]\n Course: [0.0, 0.0, 0.0, 0.0, 0.3, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins to accelerate\n\nHuman: Why does the ego car doing this?\nAssistant: since the intersection is now clear.\n\n",
+    "BDDX_Processed/video/167e3ac6-54189bd3_15177.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.37, 0.32, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.03, -0.17, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red and pedestrians are crossing the street.\n\n",
+    "BDDX_Processed/video/05476b39-9d783f66_00261.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped in the city.\n\nHuman: Why does the ego car doing this?\nAssistant: Because the car is parked.\n\n",
+    "BDDX_Processed/video/18285276-becaf072_16080.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.12, 0.22, 0.2, 0.31, 0.47]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.09, 0.1, 0.08, 0.06, 0.16, 0.17]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls slowly forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving forward.\n\n",
+    "BDDX_Processed/video/0711ab13-698c82be_06934.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is parked in a parking lot.\n\n",
+    "BDDX_Processed/video/11589021-eb71270b_12440.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.94, 18.96, 18.99, 19.01, 19.03, 19.04, 19.05]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.06, 0.06, 0.06, 0.06, 0.06, 0.07, 0.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car goes through an intersection.\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a green light.\n\n",
+    "BDDX_Processed/video/040dfa50-4082932d_02267.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [20.93, 19.74, 16.77, 15.68, 15.23, 15.92, 16.53]\n Curvature: [0.0, -0.01, -0.01, -0.02, 0.0, 0.02, -0.02]\n Acceleration: [-0.14, -1.04, -1.03, -0.22, 0.04, 0.31, 0.29]\n Course: [0.0, 1.03, 0.61, 0.55, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car maintains a constant speed and spacing with the vehicle ahead\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/03df831f-c91ea298_05736.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.07, 1.5, 3.09, 5.94, 8.45, 11.92, 14.46]\n Curvature: [0.16, -0.07, 0.05, -0.08, -0.01, 0.03, -0.0]\n Acceleration: [0.55, 0.79, 1.94, 2.31, 2.51, 2.68, 1.59]\n Course: [0.0, -0.16, -0.21, 0.72, -0.41, 0.11, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear again.\n\n",
+    "BDDX_Processed/video/1c676e43-de296285_18071.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.83, 4.49, 2.08, 1.12, 0.21, 0.1, 0.0]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-3.5, -3.4, -3.23, -2.11, -1.01, -0.53, -0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because it has reached its destination and is preparing to park.\n\n",
+    "BDDX_Processed/video/029ad1d1-2f5ad804_02181.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.57, 15.28, 15.97, 16.65, 16.98, 17.17, 17.11]\n Curvature: [-0.01, 0.01, 0.01, 0.01, 0.01, 0.01, -0.01]\n Acceleration: [1.14, 1.1, 0.98, 0.78, 0.44, 0.16, 0.06]\n Course: [0.0, 0.0, -0.11, -0.11, -0.14, -0.17, -0.17]\nWhat is the action of ego car?\nAssistant: The car is merging left\n\nHuman: Why does the ego car doing this?\nAssistant: to get out of the exit lane\n\n",
+    "BDDX_Processed/video/0a7cbfc6-bfeebf8d_08810.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.17, 0.04, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.17, -0.1, -0.04, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the vehicle in front of it is stopped.\n\n",
+    "BDDX_Processed/video/20b8607e-7550b48f_20231.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.25, 10.0, 8.74, 7.41, 6.26, 5.68, 4.77]\n Curvature: [0.02, 0.03, 0.04, 0.01, 0.02, 0.33, 0.46]\n Acceleration: [-1.86, -2.17, -2.44, -2.09, -1.87, -2.39, -2.77]\n Course: [0.0, 0.09, 0.04, -0.09, -0.02, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a red light.\n\n",
+    "BDDX_Processed/video/02fc82b3-279906a9_03632.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.96, 1.92, 3.44, 4.49, 5.35, 6.07, 6.17]\n Curvature: [-0.0, 0.0, 0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.66, 1.21, 1.25, 0.93, 0.77, 0.4, -0.14]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/13796ade-e12adb3b_13509.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.98, 6.97, 6.97, 6.96, 6.95, 6.88, 6.81]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.01, -0.1, -0.19]\n Acceleration: [0.02, -0.02, -0.07, -0.11, -0.13, -0.05, 0.03]\n Course: [0.0, 0.0, 0.0, 0.0, -0.01, -0.02, -0.02]\nWhat is the action of ego car?\nAssistant: The car turns right\n\nHuman: Why does the ego car doing this?\nAssistant: while entering a cross road\n\n",
+    "BDDX_Processed/video/05fa6494-8f436d04_02017.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.04, 11.85, 12.59, 13.3, 13.84, 14.42, 14.93]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [1.6, 1.66, 1.71, 1.67, 1.53, 1.37, 1.24]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car veers left\n\nHuman: Why does the ego car doing this?\nAssistant: to merge into the faster lane.\n\n",
+    "BDDX_Processed/video/11b47a8a-24a77ef8_12646.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.03, 3.61, 3.24, 2.82, 2.44, 2.4, 2.35]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.5, -1.33, -1.19, -1.02, -0.84, -0.52, -0.24]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down.\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has slowed down to turn left.\n\n",
+    "BDDX_Processed/video/0c29b846-90e94a08_09753.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.03, 10.5, 10.58, 10.74, 10.42, 10.66, 10.74]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [-0.53, -0.22, 0.12, -0.09, -0.04, 0.19, -0.67]\n Course: [0.0, -0.03, -0.2, -0.07, -0.07, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is going down the street at a steady speed.\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/0886e393-b1f18174_07727.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.52, 16.01, 15.38, 14.77, 12.76, 12.24, 12.47]\n Curvature: [0.04, 0.01, 0.02, -0.0, -0.01, 0.0, 0.01]\n Acceleration: [-0.26, -0.4, -0.52, -1.04, -1.11, -0.09, 0.43]\n Course: [0.0, -0.86, -0.91, -0.7, 0.2, 0.0, -0.23]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: to keep up with traffic\n\n",
+    "BDDX_Processed/video/1be932d6-d6dce22b_17864.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is red and pedestrians are crossing the street.\n\n",
+    "BDDX_Processed/video/10a18a70-851b463c_11864.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.85, 1.75, 1.65, 1.55, 1.48, 1.61, 1.72]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.39, -0.27, -0.17, -0.05, 0.07, 0.26, 0.42]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving slowly left and forward on the right side of the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is pulling out into the street.\n\n",
+    "BDDX_Processed/video/14098efb-538670fc_13749.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.06, 14.89, 14.73, 14.55, 14.39, 14.22, 14.33]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.64, -0.56, -0.49, -0.37, -0.19, -0.0, 0.03]\n Course: [0.0, 0.08, 0.05, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it's going to make a turn down the road.\n\n",
+    "BDDX_Processed/video/031637c4-10cdaf09_03972.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.08, 1.71, 2.88, 4.06, 4.39, 4.42, 4.32]\n Curvature: [0.02, -0.01, 0.01, -0.02, -0.04, 0.02, -0.05]\n Acceleration: [0.39, 0.72, 0.91, 0.6, 0.12, -0.03, 0.0]\n Course: [0.0, 0.1, 0.0, 0.0, 0.05, 0.12, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowly accelerating.\n\nHuman: Why does the ego car doing this?\nAssistant: The light controlling the intersection has turned green and traffic is proceeding through the intersection.\n\n",
+    "BDDX_Processed/video/06cb522f-dd1b9be7_06701.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.72, 1.02, 2.46, 5.37, 7.59, 8.56, 9.6]\n Curvature: [-0.07, -0.06, 0.01, -0.03, -0.01, 0.01, -0.0]\n Acceleration: [-0.53, -0.06, 1.48, 1.05, 0.85, 0.51, 0.43]\n Course: [0.0, 0.61, 1.88, 2.82, 1.04, 0.0, -0.45]\nWhat is the action of ego car?\nAssistant: The car begins driving down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is coming and can safely move forward.\n\n",
+    "BDDX_Processed/video/0406b065-a759da9c_05796.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.46, 2.87, 3.29, 3.7, 4.09, 4.29, 4.49]\n Curvature: [-0.03, -0.04, -0.04, -0.05, -0.06, -0.06, -0.06]\n Acceleration: [1.89, 1.71, 1.53, 1.35, 1.17, 0.97, 0.76]\n Course: [0.0, 0.05, 0.05, 0.05, 0.18, 0.32, 0.32]\nWhat is the action of ego car?\nAssistant: The car is moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the light has turned from red to green.\n\n",
+    "BDDX_Processed/video/18c4d009-778cd77b_16367.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.85, 10.74, 12.43, 12.82, 10.59, 9.41, 7.05]\n Curvature: [0.02, 0.02, 0.01, 0.01, 0.01, 0.05, 0.07]\n Acceleration: [1.17, 1.61, 0.94, -0.78, -1.67, -1.32, -3.73]\n Course: [0.0, -0.41, -0.75, -0.84, 0.82, 0.0, -13.31]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear.\n\n",
+    "BDDX_Processed/video/0723c4dd-6d50a276_07034.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.55, 3.78, 4.96, 5.51, 5.99, 6.03, 6.03]\n Curvature: [0.03, 0.05, 0.06, 0.01, -0.04, -0.01, 0.02]\n Acceleration: [2.22, 1.96, 1.68, 1.08, 0.52, 0.18, -0.06]\n Course: [0.0, -0.09, -0.2, -0.54, -0.34, -0.14, -0.14]\nWhat is the action of ego car?\nAssistant: The car accelerates and merges into the lane to its left\n\nHuman: Why does the ego car doing this?\nAssistant: as the light turned green and there are no impeding cars in the vicinity.\n\n",
+    "BDDX_Processed/video/19de62f2-71da09bf_16854.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.04, 3.48, 3.21, 3.37, 4.05, 4.26, 3.17]\n Curvature: [-0.0, -0.0, 0.0, -0.0, -0.05, -0.51, -0.19]\n Acceleration: [0.92, 0.07, -0.06, 0.42, 0.46, -0.41, -1.28]\n Course: [0.0, 0.0, 0.0, 0.0, 3.97, 11.62, 4.74]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's making a right turn.\n\n",
+    "BDDX_Processed/video/1534d7b4-879592c0_14345.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.78, 7.84, 4.95, 2.61, 1.19, 0.52, 0.17]\n Curvature: [-0.01, 0.0, 0.02, 0.02, 0.04, 0.06, 0.06]\n Acceleration: [-3.92, -3.73, -2.95, -2.08, -1.19, -0.58, -0.28]\n Course: [0.0, 0.16, -0.16, -0.08, 0.0, 0.08, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because there is traffic stopped ahead of it at a red light just after the exit ramp that it just exited from.\n\n",
+    "BDDX_Processed/video/16504799-3bf2d2bf_15051.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.82, 11.95, 12.2, 11.88, 10.96, 10.07, 9.45]\n Curvature: [0.0, -0.0, 0.01, 0.0, -0.01, -0.0, 0.0]\n Acceleration: [0.01, 0.14, 0.02, -0.45, -0.65, -0.63, -0.3]\n Course: [0.0, 0.0, 0.59, -0.19, 0.0, 0.33, 0.0]\nWhat is the action of ego car?\nAssistant: The car proceeds down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving steadily.\n\n",
+    "BDDX_Processed/video/0810662d-2ea3d3be_07412.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.15, 0.82, 2.6, 4.6, 6.66, 9.19, 11.76]\n Curvature: [0.19, 0.02, -0.05, -0.14, -0.22, -0.04, 0.0]\n Acceleration: [-0.45, 0.65, 1.59, 1.78, 1.92, 2.27, 1.5]\n Course: [0.0, -0.99, 0.0, 0.0, 16.34, 0.0, -0.01]\nWhat is the action of ego car?\nAssistant: The car is accelerating and turning right\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned green.\n\n",
+    "BDDX_Processed/video/0fe850c9-6c8dc241_11593.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.05, 12.33, 11.7, 11.64, 11.59, 11.66, 11.63]\n Curvature: [0.02, 0.0, -0.02, -0.01, -0.0, -0.01, -0.01]\n Acceleration: [-1.27, -0.98, -0.71, -0.32, -0.0, -0.22, -0.49]\n Course: [0.0, -0.09, -0.08, -0.05, 0.04, 0.23, 0.16]\nWhat is the action of ego car?\nAssistant: The car is merging into the left lane.\n\nHuman: Why does the ego car doing this?\nAssistant: The car has left the highway and is moving onto an exit lane\n\n",
+    "BDDX_Processed/video/02097021-05dcbf23_00667.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.09, -0.09, -0.09, -0.09, -0.09, -0.09, -0.07]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped.\n\n",
+    "BDDX_Processed/video/06cb522f-aaed5ecf_06697.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.45, 12.67, 10.9, 9.18, 7.46, 5.87, 5.21]\n Curvature: [-0.06, -0.05, -0.04, -0.02, -0.02, -0.01, -0.01]\n Acceleration: [-2.45, -3.19, -3.93, -4.19, -3.46, -2.79, -2.04]\n Course: [0.0, 0.56, 0.56, 0.31, 0.41, 0.31, 0.2]\nWhat is the action of ego car?\nAssistant: The car comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped in front.\n\n",
+    "BDDX_Processed/video/08649f3e-0a88fbfe_07613.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.66, 3.62, 2.71, 1.68, 0.74, 0.54, 0.36]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-3.66, -3.31, -2.99, -2.64, -2.27, -1.75, -1.29]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to an abrupt stop\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is red\n\n",
+    "BDDX_Processed/video/04348402-062a4eac_02617.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.09, 0.39, 1.32, 2.51]\n Curvature: [0.05, 0.05, 0.05, 0.04, -0.01, -0.06, -0.02]\n Acceleration: [0.0, 0.0, 0.07, 0.34, 0.81, 1.32, 1.39]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates slowly\n\nHuman: Why does the ego car doing this?\nAssistant: when traffic in front of it starts to move.\n\n",
+    "BDDX_Processed/video/0cf398b3-ce65ab64_10211.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [30.23, 28.63, 27.16, 25.8, 24.77, 23.75, 23.6]\n Curvature: [0.01, 0.01, 0.02, 0.02, 0.02, 0.01, 0.02]\n Acceleration: [-2.77, -2.97, -3.16, -2.96, -2.2, -1.44, -0.87]\n Course: [0.0, -0.28, -0.19, -0.18, -0.18, -0.18, -0.11]\nWhat is the action of ego car?\nAssistant: The car brakes and slows down a bit\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a slower moving car ahead in the lane.\n\n",
+    "BDDX_Processed/video/039f729b-2ba56f17_05613.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.06, 1.91, 2.44, 2.39, 2.91, 4.16, 5.23]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.01, 0.16, 0.09, 0.14, 0.35, 0.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead is slowly moving and congested.\n\n",
+    "BDDX_Processed/video/1e1ff90e-24d540ec_18934.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.46, 6.79, 5.28, 4.42, 3.62, 2.73, 1.93]\n Curvature: [0.02, 0.0, -0.01, 0.09, 0.15, 0.01, -0.1]\n Acceleration: [-2.84, -2.59, -2.34, -1.97, -1.64, -1.42, -1.18]\n Course: [0.0, 0.14, 0.12, 0.05, 0.03, 0.05, 0.01]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has slowed down.\n\n",
+    "BDDX_Processed/video/0501cda3-6d714e69_03993.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.05, 0.1]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.68, -0.48, -0.28, -0.08, 0.09, 0.29, 0.49]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/18c4d009-c765b465_16371.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.99, 1.99, 2.0, 2.0, 2.0, 2.01, 2.03]\n Curvature: [-0.02, -0.01, -0.0, 0.0, 0.01, 0.01, 0.0]\n Acceleration: [-0.2, -0.14, -0.08, -0.02, 0.03, 0.23, 0.43]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is turning.\n\n",
+    "BDDX_Processed/video/0595227e-bf4ccdda_06362.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.59, 6.94, 6.37, 7.07, 7.91, 7.76, 7.68]\n Curvature: [0.0, -0.03, -0.02, -0.0, 0.01, -0.01, 0.01]\n Acceleration: [0.75, -0.07, -0.22, 0.4, -0.12, -0.06, -0.08]\n Course: [0.0, -0.67, -0.94, 0.0, -0.36, -7.1, 0.88]\nWhat is the action of ego car?\nAssistant: The car moves slowly along the road\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving slowly\n\n",
+    "BDDX_Processed/video/1adbf9f5-fa9f8eac_17472.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.44, 3.71, 3.98, 4.35, 4.81, 5.32, 5.89]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.67, 0.79, 0.91, 1.05, 1.2, 1.37, 1.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.16]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic ahead is advancing quickly.\n\n",
+    "BDDX_Processed/video/10af5310-a3e93daa_11901.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.4, 2.46, 3.11, 3.44, 3.59, 3.92, 4.45]\n Curvature: [0.0, -0.0, 0.0, -0.14, -0.41, -0.31, -0.11]\n Acceleration: [1.37, 1.08, 0.66, 0.36, 0.32, 0.55, 0.77]\n Course: [0.0, 0.0, 0.0, 7.17, 8.71, 10.25, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls into the intersection and turns right\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/0308ce2b-8fc462fd_03785.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.59, 8.15, 6.64, 4.55, 3.35, 2.2, 1.52]\n Curvature: [0.0, -0.0, 0.02, 0.52, 0.45, 0.22, 0.07]\n Acceleration: [-0.72, -1.55, -1.96, -1.75, -1.38, -0.96, -0.33]\n Course: [0.0, 0.0, 65.18, -5.41, -5.17, -11.05, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning to the left.\n\n",
+    "BDDX_Processed/video/0b4892bd-444faa63_09246.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.72, 6.14, 5.6, 5.11, 4.62, 3.83, 3.03]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-1.7, -1.32, -1.01, -1.11, -1.22, -1.38, -1.51]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because another car is\n\n",
+    "BDDX_Processed/video/1d8cd3b4-72fb8013_18626.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.45, 1.15, 0.83, 0.61, 0.5, 0.39, 0.44]\n Curvature: [0.47, 7.4, 14.91, 21.24, 27.18, 32.67, 34.44]\n Acceleration: [-0.81, -0.71, -0.6, -0.46, -0.25, -0.06, 0.03]\n Course: [0.0, -0.13, -0.13, -0.03, -0.04, -0.03, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls over and stops on the right side of the street\n\nHuman: Why does the ego car doing this?\nAssistant: since there is an open area to park.\n\n",
+    "BDDX_Processed/video/150a973a-f7e004cf_14220.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.84, 8.46, 7.8, 10.36, 9.32, 7.66, 9.25]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.01, 0.0]\n Acceleration: [0.6, -0.53, 0.75, 0.2, -0.35, 0.1, -0.02]\n Course: [0.0, -0.37, 0.0, -0.17, -0.27, -0.37, -1.27]\nWhat is the action of ego car?\nAssistant: The car heads down the highway at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/07184fbb-2e67b768_01233.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.86, 14.24, 13.92, 13.99, 14.01, 13.84, 13.39]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.16, -0.7, -0.28, 0.03, -0.17, -0.34, -0.33]\n Course: [0.0, 0.05, 0.0, 0.0, 0.0, 0.08, 0.08]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the car ahead is moving faster.\n\n",
+    "BDDX_Processed/video/13081d41-6bc6d1e4_13205.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.18, 0.32, 0.46, 0.6, 0.76, 1.24, 1.71]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.35, 0.57, 0.8, 1.02, 1.21, 1.38, 1.55]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins to accelerate\n\nHuman: Why does the ego car doing this?\nAssistant: as the intersection is clear.\n\n",
+    "BDDX_Processed/video/183c36c2-cf36f3c2_16118.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as it is at a red light.\n\n",
+    "BDDX_Processed/video/0cdb6d7e-55d7c1d9_10106.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.32, 10.33, 11.06, 11.94, 12.49, 13.98, 14.8]\n Curvature: [-0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.63, 0.66, 0.63, 0.52, 0.8, 0.93, 0.51]\n Course: [0.0, -0.04, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is clear.\n\n",
+    "BDDX_Processed/video/0cd7b5f1-21b1e324_10097.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.04, 9.76, 9.76, 9.7, 9.02, 9.28, 8.91]\n Curvature: [-0.0, -0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.39, 0.1, -0.13, 0.19, -0.28, -0.04, -0.12]\n Course: [0.0, 0.0, 0.0, -0.42, 0.0, 0.25, 0.0]\nWhat is the action of ego car?\nAssistant: The car is going down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/2105466b-a8ae3ede_20353.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.01, 4.16, 7.07, 9.72, 11.45, 12.65, 12.84]\n Curvature: [-0.03, 0.01, 0.03, 0.01, 0.01, 0.0, 0.0]\n Acceleration: [1.31, 1.99, 2.18, 1.69, 1.13, 0.58, -0.68]\n Course: [0.0, 0.74, -1.02, -1.52, -0.29, 0.0, 0.12]\nWhat is the action of ego car?\nAssistant: The car accelerates forward down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the crossing vehicles have gotten out of the way.\n\n",
+    "BDDX_Processed/video/12fee2da-e253d389_13181.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.18, 0.26, 0.18, 0.22, 0.45, 1.13]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.03, 0.07, 0.06, -0.01, 0.3, 0.76, 1.47]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is inching forward slowly.\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is anticipating that the car ahead will move soon.\n\n",
+    "BDDX_Processed/video/03d34d05-4a61174c_05712.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [23.2, 22.72, 22.27, 21.72, 21.13, 20.48, 20.06]\n Curvature: [-0.01, -0.01, 0.0, 0.0, -0.01, -0.01, 0.03]\n Acceleration: [-0.44, -0.83, -1.19, -1.39, -1.34, -1.29, -1.73]\n Course: [0.0, 0.0, 0.0, 0.21, 0.14, 0.21, -0.25]\nWhat is the action of ego car?\nAssistant: The car starts slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: while in the exit lane\n\n",
+    "BDDX_Processed/video/097450c5-3e2b46e6_08182.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.28, 2.17, 4.27, 6.33, 8.13, 9.79, 10.81]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.01, 0.0, -0.0]\n Acceleration: [1.05, 1.93, 2.02, 1.87, 1.69, 1.29, 1.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.07, 0.05, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light has turned green and there are no cars in front of it.\n\n",
+    "BDDX_Processed/video/03d34d05-4a61174c_05711.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [22.77, 22.63, 22.5, 22.36, 22.25, 22.42, 22.57]\n Curvature: [-0.05, -0.03, -0.01, 0.01, 0.03, 0.01, 0.0]\n Acceleration: [-0.07, -0.03, -0.01, 0.02, 0.06, 0.18, 0.28]\n Course: [0.0, 0.28, 0.14, 0.28, 0.16, 0.05, 0.02]\nWhat is the action of ego car?\nAssistant: The car merges to the right\n\nHuman: Why does the ego car doing this?\nAssistant: since it has reached the exit lane\n\n",
+    "BDDX_Processed/video/11035230-a4cd3082_12177.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.93, 10.41, 10.72, 11.56, 11.9, 11.56, 11.71]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.3, 0.28, 0.26, 0.44, -0.07, -0.06, -0.05]\n Course: [0.0, 0.0, 0.0, -0.03, -0.06, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car on the roadway\n\n",
+    "BDDX_Processed/video/05584a52-aa126299_06300.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.63, 8.05, 9.33, 9.96, 10.68, 11.37, 12.07]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [1.41, 1.23, 0.79, 0.59, 0.6, 0.59, 0.56]\n Course: [0.0, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues to move forward\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is coming and can safely move forward.\n\n",
+    "BDDX_Processed/video/1292244b-e425e3a1_12983.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.82, 13.18, 15.47, 17.76, 19.31, 20.42, 20.46]\n Curvature: [0.01, 0.0, 0.0, 0.02, 0.0, 0.01, 0.01]\n Acceleration: [-2.59, 0.94, 1.31, 0.99, 0.66, 0.36, -0.28]\n Course: [0.0, 0.0, 0.0, -0.86, 0.0, -0.91, 0.0]\nWhat is the action of ego car?\nAssistant: The car is veering left.\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is changing lanes again to avoid stopped cars in it's lane.\n\n",
+    "BDDX_Processed/video/0ac90ac0-c74d62d6_09063.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.25, 5.08, 4.51, 4.66, 5.35, 6.09, 6.82]\n Curvature: [0.0, 0.13, 0.33, 0.62, 0.36, 0.12, 0.02]\n Acceleration: [-1.79, -1.14, -0.36, 0.6, 0.96, 1.18, 1.27]\n Course: [0.0, -5.56, -4.76, -4.76, -8.26, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning left\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may enter another road.\n\n",
+    "BDDX_Processed/video/077192f8-4d59e803_02522.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.2, 8.19, 8.19, 8.18, 8.18, 8.18, 8.18]\n Curvature: [-0.15, -0.1, -0.05, 0.0, 0.04, 0.0, -0.04]\n Acceleration: [-0.02, -0.02, -0.02, -0.01, -0.02, -0.14, -0.26]\n Course: [0.0, 0.0, 0.0, 0.0, -0.25, -0.5, -0.5]\nWhat is the action of ego car?\nAssistant: The car is swerving left\n\nHuman: Why does the ego car doing this?\nAssistant: to get around the vehicle parked in its lane.\n\n",
+    "BDDX_Processed/video/10b038d4-a0efb156_11920.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.38, 10.28, 6.32, 5.49, 4.51, 3.75, 4.81]\n Curvature: [-0.05, -0.13, -0.32, -0.17, -0.12, -0.13, -0.21]\n Acceleration: [-3.59, -4.44, -2.32, -0.89, -0.88, 0.12, 1.12]\n Course: [0.0, 2.84, -16.08, -41.06, 5.14, 3.84, 5.79]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's making a turn to the right.\n\n",
+    "BDDX_Processed/video/1136797c-862a53c6_12338.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.72, 12.77, 11.9, 11.05, 10.35, 9.65, 9.0]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.01]\n Acceleration: [-1.41, -1.67, -1.91, -2.02, -1.91, -1.8, -2.03]\n Course: [0.0, 0.0, 0.0, 0.04, 0.04, 0.04, -0.02]\nWhat is the action of ego car?\nAssistant: The car is heading down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear.\n\n",
+    "BDDX_Processed/video/16f49088-51432b28_15517.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.21, 4.41, 4.53, 3.29, 2.16, 1.74, 1.41]\n Curvature: [-0.0, -0.04, -0.09, -0.09, -0.09, -0.08, -0.06]\n Acceleration: [0.74, -0.13, -1.02, -1.33, -1.52, -0.94, -0.45]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6]\nWhat is the action of ego car?\nAssistant: The car moves forward slightly\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has moved forward.\n\n",
+    "BDDX_Processed/video/0aab75aa-0d3bd022_08879.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.01, 6.6, 6.58, 7.01, 7.47, 8.0, 8.6]\n Curvature: [0.02, 0.03, 0.02, 0.0, -0.03, -0.03, 0.02]\n Acceleration: [-0.75, -0.26, 0.22, 0.68, 0.79, 0.75, 0.5]\n Course: [0.0, -0.51, -0.18, -0.18, 0.0, 0.06, 0.06]\nWhat is the action of ego car?\nAssistant: The car merges one lane left\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is open.\n\n",
+    "BDDX_Processed/video/16504799-3bf2d2bf_15049.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.19, 8.29, 9.73, 10.66, 10.73, 7.95, 7.72]\n Curvature: [0.0, -0.01, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.05, 0.77, 1.16, 0.52, -1.35, -1.46, 0.27]\n Course: [0.0, 0.08, 0.45, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: to go around the car in front of him.\n\n",
+    "BDDX_Processed/video/1deff035-72e48ccc_18834.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.61, 3.8, 3.98, 4.04, 4.04, 3.68, 3.29]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.89, 0.55, 0.22, -0.05, -0.33, -0.62, -0.84]\n Course: [0.0, -0.04, -0.03, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving forward.\n\n",
+    "BDDX_Processed/video/064bad4e-160c2ad1_06519.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.57, 0.39, 0.74, 2.17, 3.61, 4.16, 4.2]\n Curvature: [-0.57, -2.58, -2.18, -0.05, -0.07, 0.0, 0.02]\n Acceleration: [-0.09, 0.07, 0.86, 1.4, 0.96, 0.29, -0.15]\n Course: [0.0, -0.09, -0.08, -0.3, -0.89, -2.0, -1.44]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: as the car in front of it accelerates after the light turned green.\n\n",
+    "BDDX_Processed/video/21f9ad35-74dbf9e1_20786.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.28, 8.69, 8.18, 7.59, 7.07, 6.95, 6.85]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.64, -1.56, -1.48, -1.39, -1.29, -1.08, -0.89]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: because it is going over a speed bump.\n\n",
+    "BDDX_Processed/video/033dea13-5a8114fe_00140.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.04, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.01, -0.06, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/02c2a4d8-94d32b2f_05285.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.75, 9.04, 9.31, 9.7, 10.04, 10.25, 10.46]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.36, 0.51, 0.64, 0.6, 0.56, 0.46, 0.36]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues forward down the road\n\nHuman: Why does the ego car doing this?\nAssistant: since the heavy traffic is moving\n\n",
+    "BDDX_Processed/video/0c5ff0c6-b1853637_09808.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.36, 1.67, 2.99, 3.05, 3.25, 5.47, 7.28]\n Curvature: [-0.84, -0.68, -0.51, -0.39, -0.28, -0.14, -0.04]\n Acceleration: [1.09, 1.23, 1.4, 1.75, 2.09, 2.49, 2.65]\n Course: [0.0, 1.27, 1.97, 2.81, 1.94, 2.13, 1.94]\nWhat is the action of ego car?\nAssistant: The car is turning to the right\n\nHuman: Why does the ego car doing this?\nAssistant: because the off ramp ends and it needs to enter another road.\n\n",
+    "BDDX_Processed/video/0dcd165f-e537bad5_10700.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.01, 0.12, 0.22]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is red\n\n",
+    "BDDX_Processed/video/1036b362-92000280_11761.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.41, 12.2, 11.53, 11.37, 11.3, 11.38, 11.58]\n Curvature: [0.0, -0.01, -0.0, 0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.17, -0.36, -0.2, 0.03, -0.1, 0.15, 0.27]\n Course: [0.0, -0.29, 0.13, 0.0, 0.0, -0.44, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road ahead is clear.\n\n",
+    "BDDX_Processed/video/1d055cd0-578f6d47_18278.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.95, 7.18, 6.35, 5.78, 5.58, 5.35, 5.23]\n Curvature: [-0.13, -0.14, -0.15, -0.15, -0.12, -0.09, -0.09]\n Acceleration: [-1.4, -1.37, -1.34, -1.19, -0.85, -0.47, -0.32]\n Course: [0.0, 1.01, 1.51, 2.25, 1.5, 2.25, 2.18]\nWhat is the action of ego car?\nAssistant: The car is turning right\n\nHuman: Why does the ego car doing this?\nAssistant: because it has the right of way.\n\n",
+    "BDDX_Processed/video/05584a52-9725bd6b_06293.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.77, 8.36, 6.84, 5.2, 3.64, 3.53, 3.61]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.43, -2.24, -3.01, -2.36, -1.59, -0.32, 0.92]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down, but not stopping at the stop sign\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no other cars around.\n\n",
+    "BDDX_Processed/video/1d97c075-dac6540f_18647.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.31, 11.46, 12.2, 12.45, 13.29, 13.48, 13.18]\n Curvature: [0.0, -0.01, 0.0, 0.0, -0.03, -0.06, -0.01]\n Acceleration: [0.32, 0.5, -0.05, 0.46, 0.02, 0.06, -0.07]\n Course: [0.0, 0.05, -0.06, 0.0, 0.12, -0.4, -0.17]\nWhat is the action of ego car?\nAssistant: The car is heading down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/0048f391-8eb40ca6_04380.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.72, 9.01, 10.33, 7.05, 3.34, 5.1, 6.61]\n Curvature: [-0.04, -0.01, -0.01, -0.02, 0.01, 0.01, -0.0]\n Acceleration: [1.66, 0.88, -0.03, -2.92, -0.26, 1.4, 0.1]\n Course: [0.0, 0.16, 0.0, 0.64, -0.0, -0.14, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign\n\n",
+    "BDDX_Processed/video/0e764680-0710a009_10994.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.56, 3.17, 2.82, 2.43, 2.02, 1.57, 1.17]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.03, -1.18, -1.31, -1.46, -1.56, -1.36, -1.18]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a stop sign.\n\n",
+    "BDDX_Processed/video/1e272076-566f6711_18949.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.52, 12.18, 12.0, 12.0, 11.35, 10.75, 10.39]\n Curvature: [-0.0, 0.0, -0.0, -0.01, -0.0, -0.0, -0.0]\n Acceleration: [-0.26, -0.26, -0.35, -0.54, -0.72, -0.69, -0.27]\n Course: [0.0, 0.0, 0.0, 0.0, 0.15, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car proceeds down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the street is clear of traffic.\n\n",
+    "BDDX_Processed/video/0711ab13-698c82be_00926.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked in a lot\n\n",
+    "BDDX_Processed/video/12ac7475-69905215_13036.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is still red.\n\n",
+    "BDDX_Processed/video/1f40dccd-948ec31e_19436.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.97, 3.91, 6.01, 7.12, 7.44, 6.84, 5.75]\n Curvature: [-0.0, 0.0, -0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [1.92, 1.97, 1.56, 0.68, -0.12, -0.81, -1.36]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerated slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because the road ahead was clear with no cars in the way.\n\n",
+    "BDDX_Processed/video/03db1bc2-833ca88c_01591.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.26, 0.18, 0.07, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.26, -1.53, -2.09, -2.09, -2.09, -2.1, -2.1]\n Acceleration: [-0.24, -0.06, -0.09, -0.01, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0ce3ea39-1a4ad70f_10129.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.44, 0.32, 0.22, 0.1, 0.0, 0.0, 0.0]\n Curvature: [-0.03, -0.03, -0.03, -0.03, -0.03, -0.03, -0.03]\n Acceleration: [-0.32, -0.3, -0.27, -0.24, -0.22, -0.12, -0.03]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping again\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is still red\n\n",
+    "BDDX_Processed/video/111aecfb-d6fb70d7_12244.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.0, 2.9, 2.78, 2.68, 2.57, 2.45, 2.32]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.13, -0.01, -0.18, -0.32, -0.44, -0.43, -0.41]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because the car directly in front puts on brakes.\n\n",
+    "BDDX_Processed/video/02a17951-3d88d95e_02387.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.77, 7.83, 9.73, 11.0, 11.27, 10.69, 11.02]\n Curvature: [0.02, -0.03, -0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.6, 0.66, 0.35, 0.42, -0.12, -0.27, 0.62]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02]\nWhat is the action of ego car?\nAssistant: The car moves forward\n\nHuman: Why does the ego car doing this?\nAssistant: since traffic in front of it is flowing.\n\n",
+    "BDDX_Processed/video/0f837def-6802879c_11444.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.22, 0.52, 1.31, 1.54, 1.59, 2.17]\n Curvature: [0.2, 0.11, 0.07, 0.02, 0.0, -0.0, 0.0]\n Acceleration: [-0.02, 0.25, 0.54, 0.5, 0.12, 0.31, 0.43]\n Course: [0.0, 0.0, 0.02, -0.06, -0.15, -0.02, -0.08]\nWhat is the action of ego car?\nAssistant: The car begins to move slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned green.\n\n",
+    "BDDX_Processed/video/11035230-a4cd3082_12179.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.92, 8.49, 9.15, 9.38, 9.23, 8.45, 8.91]\n Curvature: [0.01, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.01, 0.39, 0.12, 0.14, -0.27, -0.32, 0.66]\n Course: [0.0, 0.0, 0.79, 0.0, 0.18, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road\n\nHuman: Why does the ego car doing this?\nAssistant: The ar is moving with the flow of traffic down the roadway\n\n",
+    "BDDX_Processed/video/12da8593-780d421a_13101.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.2, 15.4, 15.54, 15.6, 15.53, 15.39, 15.14]\n Curvature: [-0.02, -0.01, -0.01, 0.0, 0.0, 0.01, 0.0]\n Acceleration: [0.41, 0.28, 0.14, -0.0, -0.16, -0.29, -0.36]\n Course: [0.0, 0.35, 0.14, 0.18, 0.23, -0.12, -0.09]\nWhat is the action of ego car?\nAssistant: The car merges into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: as there is a truck blocking the lane ahead and the right lane is clear.\n\n",
+    "BDDX_Processed/video/0573f031-8ef23cf6_06336.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.89, 14.37, 13.85, 13.33, 12.86, 12.68, 12.5]\n Curvature: [-0.02, -0.0, 0.01, 0.03, 0.04, 0.03, 0.03]\n Acceleration: [-2.24, -2.01, -1.78, -1.55, -1.36, -1.46, -1.57]\n Course: [0.0, 0.2, 0.2, 0.2, -0.06, -0.32, -0.32]\nWhat is the action of ego car?\nAssistant: The car moves over two lanes to the right\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is more clear of traffic.\n\n",
+    "BDDX_Processed/video/20322e6e-e8ba2c22_19993.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.03, 11.42, 11.71, 11.97, 12.85, 12.93, 12.78]\n Curvature: [-0.01, 0.0, -0.01, 0.0, 0.0, 0.01, 0.0]\n Acceleration: [0.28, 0.23, 0.1, 0.44, 0.41, -0.1, -0.03]\n Course: [0.0, 0.41, 0.11, 0.14, 0.0, -0.28, -0.24]\nWhat is the action of ego car?\nAssistant: The car accelerates down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear and lights are green.\n\n",
+    "BDDX_Processed/video/03c66a02-3deee2e5_01332.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.0, 12.4, 12.17, 12.12, 12.39, 12.25, 12.07]\n Curvature: [0.0, 0.01, -0.01, 0.0, 0.01, 0.0, 0.0]\n Acceleration: [-0.09, -0.22, -0.12, 0.05, 0.06, -0.12, -0.01]\n Course: [0.0, 0.09, 0.25, -0.18, 0.2, -0.19, 0.27]\nWhat is the action of ego car?\nAssistant: The car maintains a constant speed and lane\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/225dad3c-023a25e9_20982.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.88, 6.16, 6.44, 6.4, 6.37, 6.42, 6.47]\n Curvature: [0.0, 0.07, 0.13, 0.08, 0.04, -0.01, -0.05]\n Acceleration: [0.56, 0.4, 0.23, 0.12, 0.02, 0.04, 0.0]\n Course: [0.0, 0.07, 0.07, 0.0, -0.19, -0.28, 0.05]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: because the driver is getting ready to pull over.\n\n",
+    "BDDX_Processed/video/1cbc652a-535dd150_18196.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.98, 7.41, 7.84, 8.27, 8.64, 9.07, 9.5]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [1.61, 1.62, 1.62, 1.63, 1.62, 1.5, 1.38]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning to the right\n\nHuman: Why does the ego car doing this?\nAssistant: to enter the road.\n\n",
+    "BDDX_Processed/video/0ac3cbf4-73c76d25_09039.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.01, 0.13, 0.22]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car isn't moving\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light ahead is red.\n\n",
+    "BDDX_Processed/video/08b7e1dc-3728a8ce_07866.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.61, 9.7, 9.78, 9.87, 9.96, 10.04, 10.13]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.38, 0.42, 0.45, 0.49, 0.53, 0.57, 0.6]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in front of it drives forward.\n\n",
+    "BDDX_Processed/video/03715d08-cb7c434d_05554.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.61, 12.01, 12.24, 12.4, 12.52, 12.61, 12.77]\n Curvature: [0.0, 0.0, -0.01, 0.01, 0.0, 0.0, 0.0]\n Acceleration: [0.36, 0.27, 0.18, 0.11, 0.1, 0.1, 0.03]\n Course: [0.0, 0.11, -0.09, -0.11, -0.32, 0.0, 0.09]\nWhat is the action of ego car?\nAssistant: the car is going forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is clear\n\n",
+    "BDDX_Processed/video/13c13518-96f102e2_13657.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is still red\n\n",
+    "BDDX_Processed/video/09abd73b-216c1c1d_08289.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [22.99, 22.69, 22.38, 22.08, 21.81, 21.4, 21.0]\n Curvature: [0.0, -0.0, -0.01, -0.01, -0.02, -0.02, -0.02]\n Acceleration: [-0.96, -1.06, -1.16, -1.26, -1.35, -1.35, -1.35]\n Course: [0.0, 0.05, 0.05, 0.05, 0.02, 0.14, 0.14]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: as its lane is free of impeding traffic.\n\n",
+    "BDDX_Processed/video/00f42f6b-b1ca1f6c_04620.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.47, 6.05, 4.77, 4.04, 3.92, 3.76, 3.62]\n Curvature: [-0.05, -0.01, -0.11, -0.18, -0.12, -0.14, -0.11]\n Acceleration: [-1.25, -1.58, -1.22, -0.62, -0.18, -0.19, 0.74]\n Course: [0.0, 0.11, 2.25, 4.99, 4.16, 4.99, 2.25]\nWhat is the action of ego car?\nAssistant: The car decelerates and turns right\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/173c837b-57b0224f_15661.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.62, 9.56, 9.5, 8.87, 7.58, 6.17, 5.09]\n Curvature: [0.01, -0.0, -0.01, -0.02, -0.02, -0.03, -0.02]\n Acceleration: [0.16, -0.64, -1.37, -2.04, -2.48, -2.96, -2.85]\n Course: [0.0, -0.25, -0.25, 0.11, 0.07, 0.11, 0.42]\nWhat is the action of ego car?\nAssistant: The car starts to pull into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because the driver wants to go down that street.\n\n",
+    "BDDX_Processed/video/0caabbb0-795b4e67_10024.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.19, 6.54, 6.85, 7.19, 7.56, 8.07, 8.52]\n Curvature: [-0.01, 0.01, 0.03, 0.05, 0.06, 0.04, 0.01]\n Acceleration: [1.57, 1.58, 1.59, 1.6, 1.62, 1.68, 1.74]\n Course: [0.0, -0.21, -0.11, -0.21, -0.11, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: because it is past the obstacle in that lane.\n\nHuman: Why does the ego car doing this?\nAssistant: The car merges back into the left lane\n\n",
+    "BDDX_Processed/video/0b3d2eb9-39a83a6c_09232.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.95, 6.09, 5.54, 4.53, 3.49, 2.7, 2.0]\n Curvature: [0.0, -0.01, 0.03, -0.02, 0.02, -0.01, -0.04]\n Acceleration: [-0.62, -0.68, -0.76, -1.0, -0.89, -0.72, -0.66]\n Course: [0.0, -0.16, -0.02, -0.09, -0.05, 0.06, 0.02]\nWhat is the action of ego car?\nAssistant: The car gradually slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign ahead.\n\n",
+    "BDDX_Processed/video/118da47c-78b4a87b_12605.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.86, 12.54, 13.56, 14.08, 14.12, 14.46, 14.47]\n Curvature: [-0.0, -0.06, -0.0, -0.0, -0.0, -0.01, -0.0]\n Acceleration: [0.59, 0.31, 0.36, 0.16, 0.09, 0.09, -0.19]\n Course: [0.0, 0.83, 0.0, 0.4, 0.36, 0.35, 0.35]\nWhat is the action of ego car?\nAssistant: The car is driving down a road\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/004071a4-a45d905f_00857.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.24, -0.24, -0.24, -0.24, -0.24, -0.24, -0.24]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.17, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as a worker directs traffic ahead.\n\n",
+    "BDDX_Processed/video/1008a17e-5dfe3438_11647.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.7, 7.58, 6.3, 5.98, 6.77, 7.91, 9.25]\n Curvature: [0.02, 0.02, 0.01, 0.04, -0.03, -0.01, -0.01]\n Acceleration: [-0.93, -0.91, -0.24, 0.1, 0.39, 0.8, 0.36]\n Course: [0.0, 0.0, 0.0, 0.0, 0.18, 0.07, -0.18]\nWhat is the action of ego car?\nAssistant: The car reduces its rate of speed as it continues forward\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead has slowed.\n\n",
+    "BDDX_Processed/video/065992a2-5bef4d51_02678.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.29, 14.45, 14.61, 14.77, 14.92, 15.19, 15.45]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.62, 0.67, 0.72, 0.78, 0.82, 0.89, 0.95]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges right\n\nHuman: Why does the ego car doing this?\nAssistant: to change lanes.\n\n",
+    "BDDX_Processed/video/22afe949-954a315b_21125.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.15, 0.0, 0.16, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-13.21, -13.37, -22.86, -24.47, -24.63, -24.8, -24.96]\n Acceleration: [-0.83, -0.0, 0.01, -0.02, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is sitting still\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked.\n\n",
+    "BDDX_Processed/video/086c8f1b-fb69efcb_07637.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.21, 6.02, 6.77, 7.52, 8.15, 8.83, 8.99]\n Curvature: [-0.13, -0.06, -0.0, 0.05, 0.07, 0.09, 0.05]\n Acceleration: [1.01, 1.35, 1.67, 1.71, 1.37, 0.99, 0.87]\n Course: [0.0, 0.35, 0.35, -0.11, -0.07, -0.11, -0.46]\nWhat is the action of ego car?\nAssistant: The car switches one lane over from left to right.\n\nHuman: Why does the ego car doing this?\nAssistant: To go around the left turners blocking the lane that goes left and straight.\n\n",
+    "BDDX_Processed/video/15b4627e-caecb332_14636.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.84, 15.85, 15.83, 15.57, 15.24, 14.65, 14.05]\n Curvature: [0.0, 0.0, 0.0, -0.02, -0.03, -0.0, 0.02]\n Acceleration: [0.01, -0.12, -0.28, -0.58, -0.87, -0.93, -1.03]\n Course: [0.0, 0.04, 0.08, 0.14, 0.19, 0.14, 0.12]\nWhat is the action of ego car?\nAssistant: The car is driving down a street\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/00091078-c1d32eea_04280.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.25, 18.07, 17.9, 17.72, 17.54, 17.27, 17.01]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.33, -0.46, -0.6, -0.73, -0.89, -1.18, -1.47]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges back into its original lane\n\nHuman: Why does the ego car doing this?\nAssistant: since there are no cars blocking the lane.\n\n",
+    "BDDX_Processed/video/05fa6494-2fad5381_06451.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.15, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, -0.07, -0.02, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/12503fc4-ad0a4d0d_12875.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.28, 8.57, 7.18, 7.87, 9.24, 9.0, 8.5]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, -0.02, -0.02]\n Acceleration: [-1.91, -1.8, 0.08, 0.58, 0.39, -0.57, 0.12]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 1.42, 1.12]\nWhat is the action of ego car?\nAssistant: The car slows and continues ahead\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic has slowed ahead.\n\n",
+    "BDDX_Processed/video/037795d6-d456ac0c_05565.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.85, 1.08, 0.33, 0.06, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.67, -0.88, -0.2, -0.12, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is red.\n\n",
+    "BDDX_Processed/video/148aff32-c27bd967_13988.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.12, 2.23, 2.34, 2.45, 2.54, 2.6, 2.67]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.41, 0.39, 0.37, 0.35, 0.33, 0.3, 0.27]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves out to the left\n\nHuman: Why does the ego car doing this?\nAssistant: as it leaves a parking spot\n\n",
+    "BDDX_Processed/video/00690c26-e4bbbd72_01223.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.97, 7.79, 7.75, 7.63, 7.91, 9.18, 10.08]\n Curvature: [0.01, 0.06, -0.04, -0.0, -0.03, -0.0, -0.03]\n Acceleration: [-0.17, -0.02, -0.12, 0.05, 0.54, 0.86, 0.57]\n Course: [0.0, -2.37, 0.0, 0.0, 2.76, 0.22, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving freely.\n\n",
+    "BDDX_Processed/video/04e9173a-d978b34d_03824.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [26.26, 25.94, 26.21, 26.77, 27.32, 27.35, 27.81]\n Curvature: [0.0, 0.01, 0.0, -0.0, -0.0, -0.01, -0.0]\n Acceleration: [-0.4, -0.0, 0.47, 0.63, 0.44, 0.38, 0.67]\n Course: [0.0, -0.06, 0.0, -0.01, -0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving two lanes to the left\n\nHuman: Why does the ego car doing this?\nAssistant: to pass slower vehicles ahead.\n\n",
+    "BDDX_Processed/video/06ff7d61-53ba5061_00612.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [21.25, 23.25, 23.7, 23.51, 23.07, 21.92, 20.84]\n Curvature: [0.0, -0.0, 0.01, -0.01, 0.01, -0.0, -0.0]\n Acceleration: [1.6, 0.9, -0.03, -0.21, -0.55, -0.87, -0.6]\n Course: [0.0, 0.0, 0.4, 0.06, 0.0, -0.21, -0.39]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/161d548f-493862d3_14942.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.66, 15.05, 15.24, 14.97, 13.16, 12.16, 11.44]\n Curvature: [0.0, 0.0, -0.0, -0.02, -0.01, -0.0, -0.01]\n Acceleration: [0.85, 0.44, -0.1, -0.81, -0.5, -0.41, -0.02]\n Course: [0.0, -0.19, 0.0, 1.72, 0.92, 0.0, 0.24]\nWhat is the action of ego car?\nAssistant: The car is driving down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear.\n\n",
+    "BDDX_Processed/video/225dad3c-023a25e9_20986.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.22, 0.16, 0.1, 0.04, 0.0, 0.0, 0.0]\n Curvature: [0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34]\n Acceleration: [-0.08, -0.08, -0.09, -0.1, -0.11, -0.08, -0.05]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0e460dd3-2cf2a7b9_10899.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.95, 14.69, 12.08, 10.93, 10.29, 8.87, 9.71]\n Curvature: [0.0, 0.01, 0.02, 0.01, -0.01, 0.01, 0.0]\n Acceleration: [0.07, -0.37, -1.13, -0.07, -0.69, -0.31, 0.85]\n Course: [0.0, -0.32, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstacles in the path.\n\n",
+    "BDDX_Processed/video/215cac43-d21a40a2_20477.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.05, 2.95, 3.08, 3.49, 5.0, 7.15, 8.85]\n Curvature: [0.03, 0.09, 0.15, 0.16, 0.09, 0.02, -0.03]\n Acceleration: [-0.99, 0.0, 0.21, 0.81, 1.63, 1.65, 1.16]\n Course: [0.0, -2.84, -6.43, -6.98, -5.06, -2.95, 0.55]\nWhat is the action of ego car?\nAssistant: The car turns left and continues onward\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/0b8d1c37-f2105bb2_09480.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.1, 10.78, 11.49, 12.64, 13.79, 14.9, 15.98]\n Curvature: [0.0, 0.0, -0.0, -0.01, -0.02, -0.01, -0.01]\n Acceleration: [1.18, 1.48, 1.79, 2.0, 2.18, 2.09, 1.76]\n Course: [0.0, 0.0, 0.0, 0.0, 0.23, 0.46, 0.11]\nWhat is the action of ego car?\nAssistant: The car merges to the right and accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: in order to pass a slower vehicle in the lane ahead\n\n",
+    "BDDX_Processed/video/08649f3e-0a88fbfe_07609.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.36, 10.81, 11.23, 11.63, 11.92, 12.22, 12.46]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.35, 1.2, 1.06, 0.92, 0.81, 0.71, 0.64]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues straight down the road\n\nHuman: Why does the ego car doing this?\nAssistant: since traffic is clear\n\n",
+    "BDDX_Processed/video/0104a3f0-9294818d_04625.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.19, 0.19, 0.19, 0.19, 0.2, 0.2, 0.2]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped in front.\n\n",
+    "BDDX_Processed/video/20ceee16-817c575c_20246.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.02, 2.19, 3.17, 3.06, 3.75, 5.59, 7.37]\n Curvature: [-0.0, -0.0, -0.0, 0.14, 0.55, 0.4, 0.01]\n Acceleration: [1.3, 1.19, 0.48, 0.41, 1.37, 1.92, 1.34]\n Course: [0.0, 0.0, 0.0, 0.0, -7.29, -10.81, 0.0]\nWhat is the action of ego car?\nAssistant: The car slowly turns left\n\nHuman: Why does the ego car doing this?\nAssistant: after letting another car and pedestrians clear the area.\n\n",
+    "BDDX_Processed/video/08b5c3d0-bd78ccb2_07847.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.43, 8.48, 8.11, 7.51, 7.24, 6.54, 5.82]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.41, -0.24, -0.09, -0.26, -0.27, -0.14, -0.38]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no cars nearby.\n\n",
+    "BDDX_Processed/video/10f1b8b5-cdd37d24_12118.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.28, 2.29, 2.29, 2.3, 2.3, 2.27, 2.25]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [-1.53, -1.15, -0.76, -0.38, -0.04, -0.02, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops short.\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a car in front that has stopped and the car can't get into the right lane yet.\n\n",
+    "BDDX_Processed/video/00391a82-d1428e56_00739.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.15, 0.31, 0.46, 0.6, 0.8, 0.99]\n Curvature: [0.93, 0.18, -0.56, -1.31, -1.81, -1.32, -0.84]\n Acceleration: [0.29, 0.39, 0.49, 0.59, 0.67, 0.69, 0.71]\n Course: [0.0, 0.02, 0.02, 0.02, 0.17, 0.34, 0.34]\nWhat is the action of ego car?\nAssistant: The car begins to accelerate\n\nHuman: Why does the ego car doing this?\nAssistant: to turn right onto the road once it is clear.\n\n",
+    "BDDX_Processed/video/036f3b5f-ee1521de_00374.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.01, 0.05, 0.08]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.02, 0.04, 0.06, 0.07, 0.05, 0.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls forward\n\nHuman: Why does the ego car doing this?\nAssistant: to get closer to the car in front of it.\n\n",
+    "BDDX_Processed/video/015fcc2d-2d44d478_03370.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.48, 19.62, 17.72, 16.16, 16.28, 17.88, 17.03]\n Curvature: [0.0, 0.01, 0.02, 0.01, 0.03, 0.05, 0.02]\n Acceleration: [-0.02, 0.1, -1.22, -0.25, 0.4, 0.52, -0.3]\n Course: [0.0, -0.44, -0.93, -2.53, -2.5, -1.61, -1.49]\nWhat is the action of ego car?\nAssistant: The car maintains a constant speed and spacing with the vehicle ahead\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/0c32a627-c812f5fe_09772.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.68, 0.68, 0.68, 0.68, 0.69, 0.69, 0.69]\n Acceleration: [-0.08, -0.06, -0.04, -0.02, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped again.\n\n",
+    "BDDX_Processed/video/06fce5b8-efc1a0dd_06844.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.67, 1.93, 4.27, 6.73, 8.83, 10.69, 12.54]\n Curvature: [0.0, 0.0, -0.01, -0.01, -0.0, -0.0, 0.0]\n Acceleration: [-0.9, 1.73, 2.56, 2.48, 2.21, 2.08, 1.98]\n Course: [0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no traffic on the street.\n\n",
+    "BDDX_Processed/video/15b8be6c-ab5d258f_14654.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.28, 19.74, 20.19, 20.61, 20.98, 21.35, 21.57]\n Curvature: [0.0, 0.01, 0.01, 0.01, -0.01, -0.03, -0.02]\n Acceleration: [1.12, 1.1, 1.08, 1.01, 0.88, 0.75, 0.66]\n Course: [0.0, 0.04, 0.04, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The ar pulls into the center lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it's going faster than the current lane.\n\n",
+    "BDDX_Processed/video/1fbec55a-ceaf0e62_19840.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.2, 7.72, 9.2, 9.84, 9.63, 9.43, 9.97]\n Curvature: [0.0, 0.02, -0.01, 0.03, 0.05, -0.0, 0.0]\n Acceleration: [0.87, 0.93, 0.4, 0.07, -0.12, -0.04, 0.55]\n Course: [0.0, -1.69, 0.0, 0.54, -0.43, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving slowly down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because it is dark and the road is narrow.\n\n",
+    "BDDX_Processed/video/115d9e12-793f1ad5_12469.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.96, 5.29, 5.62, 5.94, 6.24, 6.65, 7.06]\n Curvature: [-0.08, -0.07, -0.05, -0.04, -0.03, -0.02, -0.01]\n Acceleration: [1.31, 1.34, 1.36, 1.39, 1.42, 1.46, 1.51]\n Course: [0.0, 1.01, 1.01, 1.01, 0.07, 0.14, 0.14]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as it has completed the turn.\n\n",
+    "BDDX_Processed/video/18a9f14d-0a43f7c9_16304.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.31, 4.41, 5.67, 7.08, 8.32, 9.94, 11.11]\n Curvature: [-0.01, 0.01, -0.01, 0.0, 0.01, 0.0, 0.01]\n Acceleration: [-0.34, 0.69, 1.44, 1.48, 1.59, 1.46, 0.77]\n Course: [0.0, 0.0, 0.33, 0.08, -0.07, -0.16, -0.08]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is clear.\n\n",
+    "BDDX_Processed/video/0e1feef0-c870dc17_10865.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.59, 5.08, 6.18, 6.6, 7.26, 8.43, 9.36]\n Curvature: [0.0, 0.0, -0.0, -0.01, -0.12, -0.16, 0.0]\n Acceleration: [0.45, 0.78, 0.73, 0.53, 0.89, 1.03, 0.5]\n Course: [0.0, 0.0, 0.0, 0.01, 5.37, 4.02, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/117138e4-033d0d25_12521.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.61, 9.24, 11.25, 12.24, 13.53, 14.1, 14.79]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.05, 0.97, 0.51, 0.54, 0.41, 0.04, 0.6]\n Course: [0.0, 0.0, 0.0, 0.36, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear of traffic.\n\n",
+    "BDDX_Processed/video/2081ca78-89ca7e40_20124.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.78, 9.54, 9.22, 9.31, 10.39, 12.26, 9.87]\n Curvature: [-0.02, -0.02, 0.03, -0.09, 0.06, 0.3, -0.28]\n Acceleration: [-0.09, -0.27, -0.1, 0.44, 1.32, -0.2, -1.36]\n Course: [0.0, 0.32, 0.0, 0.31, -0.11, -0.41, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues at a reasonable speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic starts moving again.\n\n",
+    "BDDX_Processed/video/15569b20-8d825141_14436.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.84, 1.62, 2.34, 2.84, 3.38, 3.95]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [1.05, 1.33, 1.58, 1.68, 1.55, 1.42, 1.34]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/03af3912-36636dd5_05655.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.05, 12.55, 14.57, 14.72, 13.8, 13.84, 12.5]\n Curvature: [-0.0, 0.0, -0.0, -0.0, 0.0, 0.01, -0.0]\n Acceleration: [0.09, 0.65, 0.0, 0.09, 0.45, -0.91, 0.84]\n Course: [0.0, 0.0, 0.0, 0.32, 0.0, -1.19, 1.21]\nWhat is the action of ego car?\nAssistant: The car moves forward then turns right\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/00d1bafa-1b47b41c_02276.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.75, 8.13, 7.45, 6.81, 6.07, 5.39, 4.66]\n Curvature: [-0.04, -0.03, -0.02, -0.02, -0.02, -0.02, -0.03]\n Acceleration: [-1.09, -1.33, -1.59, -1.75, -1.78, -1.81, -1.82]\n Course: [0.0, 0.36, 0.54, 0.29, 0.44, 0.29, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/043026cb-cf576c90_02515.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/21649da7-9005fb00_20508.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stationary\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked on the right side of the road.\n\n",
+    "BDDX_Processed/video/0fd7d527-313c3f93_11564.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.75, 3.01, 3.24, 3.48, 3.67, 3.88, 2.92]\n Curvature: [-0.04, -0.0, 0.03, 0.05, 0.02, -0.01, 0.0]\n Acceleration: [0.97, 0.81, 0.67, 0.28, -0.39, -1.11, -1.44]\n Course: [0.0, -0.04, -0.04, 0.04, 0.02, 0.04, -0.21]\nWhat is the action of ego car?\nAssistant: The car pulls over a bit to the left\n\nHuman: Why does the ego car doing this?\nAssistant: to pull around some cars parked by the road.\n\n",
+    "BDDX_Processed/video/0d207cff-4d922795_10389.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.16, 7.82, 8.94, 9.63, 10.29, 10.55, 9.88]\n Curvature: [-0.01, -0.02, 0.02, 0.01, 0.0, -0.02, -0.04]\n Acceleration: [0.83, 0.86, 0.86, 0.67, 0.47, -0.19, -1.16]\n Course: [0.0, 0.09, 0.03, -0.19, -0.25, 0.36, 1.66]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving freely.\n\n",
+    "BDDX_Processed/video/138be3dc-f3c84b98_13581.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.04, 3.22, 2.99, 2.61, 2.13, 1.36, 0.56]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.14, -0.02, -0.29, -0.42, -0.62, -0.77, -0.56]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as there is traffic ahead stopped at a red light.\n\n",
+    "BDDX_Processed/video/011a2aa0-677a45ae_04650.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.41, 0.36, 0.95, 1.67, 2.02, 1.85, 1.03]\n Curvature: [0.14, 0.15, 0.16, 0.18, 0.13, -0.21, -1.05]\n Acceleration: [-1.04, 0.17, 0.7, 0.62, 0.08, -0.61, -0.75]\n Course: [0.0, -0.07, -1.29, -27.0, -16.03, -5.06, -6.68]\nWhat is the action of ego car?\nAssistant: The car is backing up\n\nHuman: Why does the ego car doing this?\nAssistant: to continue turning around.\n\n",
+    "BDDX_Processed/video/0de6128f-d367dc4d_10787.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.7, 5.97, 6.29, 6.71, 7.04, 7.34, 7.58]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.52, 0.53, 0.54, 0.57, 0.5, 0.44, 0.43]\n Course: [0.0, 0.0, 0.0, 0.0, 0.08, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows and merges into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: to avoid a car parked partially in its lane on the right side of the road.\n\n",
+    "BDDX_Processed/video/043026cb-a30ed3f0_05888.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.45, 4.79, 5.4, 5.44, 7.18, 6.68, 6.05]\n Curvature: [0.0, -0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.18, -0.32, 0.91, -0.18, 0.27, 0.15, -0.06]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.17, -0.27]\nWhat is the action of ego car?\nAssistant: The car moves slowly along the road\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/1d56a6ef-86725847_18501.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.02, 11.35, 11.65, 11.92, 12.09, 12.28, 12.37]\n Curvature: [-0.0, -0.0, -0.01, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.91, 0.81, 0.71, 0.59, 0.48, 0.35, 0.23]\n Course: [0.0, 0.15, 0.15, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves to the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: while continuing down the street\n\n",
+    "BDDX_Processed/video/04e55a30-fc0b249b_06112.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.81, 9.74, 9.65, 9.47, 9.37, 9.16, 8.96]\n Curvature: [0.01, -0.0, 0.0, -0.01, 0.0, 0.02, 0.01]\n Acceleration: [0.01, -0.08, -0.13, -0.13, -0.15, -0.2, -0.23]\n Course: [0.0, -0.07, 0.11, 0.05, -0.23, -0.39, -0.39]\nWhat is the action of ego car?\nAssistant: The car is going forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear\n\n",
+    "BDDX_Processed/video/17ffc2bb-0eed94db_16019.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.24, 10.4, 8.46, 6.61, 5.14, 4.1, 3.59]\n Curvature: [-0.02, 0.01, 0.02, -0.01, 0.01, 0.0, -0.06]\n Acceleration: [-0.97, -2.2, -2.79, -2.55, -1.89, -1.4, -1.2]\n Course: [0.0, -0.05, -0.18, -0.14, -0.12, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car reduces its speed and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because it is approaching a red light.\n\n",
+    "BDDX_Processed/video/22b4f5e6-3035cfb2_21132.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.42, 6.02, 5.11, 4.88, 5.78, 6.81, 7.67]\n Curvature: [0.0, 0.03, 0.07, 0.1, 0.15, 0.18, 0.18]\n Acceleration: [-2.21, -1.62, -0.7, 0.51, 1.12, 1.45, 1.34]\n Course: [0.0, -0.75, -3.46, -3.46, -3.23, -3.52, -3.52]\nWhat is the action of ego car?\nAssistant: The car turns left\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a t-junction.\n\n",
+    "BDDX_Processed/video/015fe6c9-48a58255_04754.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.18, 11.22, 10.34, 9.28, 8.03, 6.77, 6.08]\n Curvature: [0.0, -0.01, -0.02, -0.04, -0.09, -0.13, -0.15]\n Acceleration: [-2.31, -2.5, -2.68, -2.75, -2.58, -2.4, -1.92]\n Course: [0.0, -0.03, -0.02, 0.35, 0.35, 0.35, -22.78]\nWhat is the action of ego car?\nAssistant: The car makes a right turn\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is seen and the car can safely move onward.\n\n",
+    "BDDX_Processed/video/02ac138c-6bc72d56_02447.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.13, 14.02, 15.39, 16.63, 17.57, 18.15, 18.58]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [2.24, 1.81, 1.15, 0.92, 0.67, 0.41, 0.17]\n Course: [0.0, 0.0, 0.0, 0.05, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is free of traffic.\n\n",
+    "BDDX_Processed/video/02f208e7-904032da_03522.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.84, 4.62, 4.41, 4.16, 3.88, 3.59, 3.38]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.25, -0.41, -0.55, -0.64, -0.64, -0.64, -0.62]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning right\n\nHuman: Why does the ego car doing this?\nAssistant: to get onto the highway entrance ramp\n\n",
+    "BDDX_Processed/video/030a6a6c-fca2979d_05460.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.09, 8.12, 8.14, 8.06, 7.81, 7.57, 6.26]\n Curvature: [-0.02, -0.01, -0.0, 0.01, 0.02, 0.04, 0.02]\n Acceleration: [0.47, 0.19, -0.1, -0.58, -1.38, -2.12, -2.5]\n Course: [0.0, 0.07, 0.07, 0.03, 0.05, 0.03, 0.07]\nWhat is the action of ego car?\nAssistant: The car has come to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped.\n\n",
+    "BDDX_Processed/video/00268999-a4b8e39d_04313.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.4, 2.34, 4.22, 5.87, 7.15, 8.73, 10.07]\n Curvature: [-0.07, -0.02, 0.01, 0.03, -0.01, 0.01, 0.0]\n Acceleration: [0.54, 1.11, 1.44, 1.02, 1.13, 1.15, 1.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear of obstructions.\n\n",
+    "BDDX_Processed/video/151db5e5-fc5cc087_14274.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [-1.0, 3.19, 7.34, 6.71, 5.87, 3.95, 2.35]\n Curvature: [0.84, 0.12, -0.58, -0.24, 0.75, 5.7, 9.09]\n Acceleration: [-1.62, 0.97, 3.36, 0.31, -2.48, -2.03, -1.4]\n Course: [0.0, 0.55, 0.59, 0.11, -0.02, -0.14, -0.16]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has stopped.\n\n",
+    "BDDX_Processed/video/1458e4ad-f3ce8d5e_13926.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.75, 4.5, 4.25, 4.08, 4.01, 3.93, 3.8]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.63, -0.55, -0.46, -0.39, -0.34, -0.28, -0.3]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there is space between it and the vehicle at the stop light ahead.\n\n",
+    "BDDX_Processed/video/011ad6b2-1dfff443_04659.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.48, 9.96, 9.91, 10.17, 10.34, 10.29, 10.11]\n Curvature: [-0.0, -0.02, -0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.74, 0.25, 0.15, 0.22, 0.08, -0.13, -0.18]\n Course: [0.0, 0.35, 0.32, 0.16, -0.14, 0.0, 0.08]\nWhat is the action of ego car?\nAssistant: The car drives straight down the road\n\nHuman: Why does the ego car doing this?\nAssistant: since the road has no traffic\n\n",
+    "BDDX_Processed/video/1a5dc090-ba1e6c6e_17067.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.3, 7.96, 7.62, 7.29, 6.87, 6.53, 6.19]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.92, -2.89, -2.86, -2.83, -2.8, -2.77, -2.75]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car reduces its speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the light ahead turns red and the cars ahead are braking.\n\n",
+    "BDDX_Processed/video/09abd73b-8e245bf8_08300.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [22.64, 22.64, 22.63, 22.53, 22.32, 22.09, 21.96]\n Curvature: [-0.03, 0.06, 0.14, 0.14, 0.03, -0.1, -0.07]\n Acceleration: [0.02, -0.1, -0.22, -0.32, -0.36, -0.42, -0.67]\n Course: [0.0, 0.38, 0.38, -0.96, -0.64, -0.96, -0.03]\nWhat is the action of ego car?\nAssistant: The car moves over one lane to the right\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is clear of traffic.\n\n",
+    "BDDX_Processed/video/07cc937c-6dbe4029_03488.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0.01]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car inches forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned green.\n\n",
+    "BDDX_Processed/video/20ff37cb-5d6ab0dc_20324.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.77, 13.34, 13.76, 12.4, 11.21, 10.61, 10.07]\n Curvature: [0.03, 0.02, 0.01, 0.03, 0.04, 0.02, 0.01]\n Acceleration: [1.14, 0.13, -0.78, -1.37, -1.78, -1.27, -0.95]\n Course: [0.0, -0.16, -0.25, -0.53, -0.33, -0.4, -0.14]\nWhat is the action of ego car?\nAssistant: The car moves to the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: as it is safe to do so.\n\n",
+    "BDDX_Processed/video/06d501fd-94c914c9_04236.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.45, 1.9, 3.85, 5.7, 7.33, 8.63, 9.69]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.32, 1.04, 1.63, 1.46, 1.29, 0.99, 0.83]\n Course: [0.0, 0.0, 0.0, 0.0, 0.06, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the pedestrians have finished crossing\n\n",
+    "BDDX_Processed/video/1e55e072-fd08f112_19044.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: because it's at a light.\n\n",
+    "BDDX_Processed/video/1127aff5-4ca99a83_12292.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.64, 1.59, 1.52, 1.34, 1.34, 1.33, 0.9]\n Curvature: [-0.0, -0.0, 0.01, 0.34, 1.4, 1.75, 1.9]\n Acceleration: [-0.05, -0.06, -0.12, -0.08, -0.0, -0.21, -0.58]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car reverses, veering left\n\nHuman: Why does the ego car doing this?\nAssistant: to make a turn.\n\n",
+    "BDDX_Processed/video/02b7b9ee-4cd88e31_02592.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.52, 9.12, 10.08, 11.02, 11.83, 11.93, 11.74]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.0, -0.01, 0.0]\n Acceleration: [0.8, 0.45, 0.53, 0.38, 0.25, -0.08, -0.13]\n Course: [0.0, 0.0, 0.0, -0.1, -0.25, 0.3, 0.0]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions in the roadway ahead.\n\n",
+    "BDDX_Processed/video/06cbfcda-32875f1e_04083.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.31, 3.63, 3.91, 4.23, 4.53, 4.72, 4.88]\n Curvature: [-0.05, -0.03, -0.01, 0.02, 0.04, 0.01, -0.0]\n Acceleration: [1.17, 1.11, 1.06, 1.0, 0.91, 0.63, 0.38]\n Course: [0.0, 0.21, 0.11, 0.21, 0.12, 0.02, 0.01]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front of it is braking.\n\n",
+    "BDDX_Processed/video/1f6c987d-75076c41_19604.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.7, 6.33, 7.74, 8.49, 9.05, 9.73, 10.2]\n Curvature: [-0.03, -0.02, -0.01, 0.0, 0.0, -0.0, 0.02]\n Acceleration: [1.99, 1.74, 0.96, 0.5, 0.57, 0.49, 0.37]\n Course: [0.0, 0.93, -0.21, 0.11, -0.32, -0.1, 0.11]\nWhat is the action of ego car?\nAssistant: The car is driving through a neighborhood\n\nHuman: Why does the ego car doing this?\nAssistant: as it passes two other cars on the left side.\n\n",
+    "BDDX_Processed/video/1358b742-90ffc3bf_13348.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.61, 0.62, 0.64, 0.65, 0.67, 0.68, 0.7]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.69, -0.59, -0.48, -0.38, -0.28, -0.17, -0.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls forward to the left slightly\n\nHuman: Why does the ego car doing this?\nAssistant: as it gets closer to the stopped traffic\n\n",
+    "BDDX_Processed/video/1504d2f9-933da05f_14191.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.73, 8.33, 7.93, 7.67, 7.61, 7.55, 7.55]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.8, -0.72, -0.64, -0.51, -0.3, -0.1, -0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: because a car in front of it is braking\n\n",
+    "BDDX_Processed/video/1e1ff90e-24d540ec_18931.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.88, 3.86, 3.85, 4.03, 4.45, 4.9, 5.45]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.01, -0.02, -0.01]\n Acceleration: [-0.04, 0.2, 0.41, 0.67, 0.94, 1.23, 1.28]\n Course: [0.0, 0.04, 0.04, 0.04, 0.02, 0.04, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving forward\n\n",
+    "BDDX_Processed/video/0a7cbfc6-e40c2fde_08817.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.2, 9.98, 8.57, 7.09, 6.08, 5.11, 3.93]\n Curvature: [-0.03, -0.01, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [-1.59, -1.94, -2.07, -1.92, -1.74, -1.49, -1.06]\n Course: [0.0, 0.59, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is braking\n\nHuman: Why does the ego car doing this?\nAssistant: in order to slow down because there are cars stopped at a red light ahead\n\n",
+    "BDDX_Processed/video/090916a1-36c89f3d_07997.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.1, 2.33, 1.65, 0.88, 0.19, 0.14, 0.09]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.01]\n Acceleration: [-3.1, -2.68, -2.32, -1.91, -1.49, -1.05, -0.66]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: while it waits for vehicles in front of it to drive forward.\n\n",
+    "BDDX_Processed/video/04076a65-57bf4de3_05813.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [23.08, 23.01, 22.96, 22.95, 22.97, 23.01, 23.11]\n Curvature: [-0.02, -0.02, -0.02, 0.01, 0.01, -0.0, -0.0]\n Acceleration: [-0.03, -0.05, -0.04, 0.0, 0.06, 0.09, 0.08]\n Course: [0.0, 0.18, 0.23, 0.19, 0.18, 0.05, 0.06]\nWhat is the action of ego car?\nAssistant: The car moves into the right lane of travel\n\nHuman: Why does the ego car doing this?\nAssistant: to accommodate faster moving traffic.\n\n",
+    "BDDX_Processed/video/0f7d9c3c-3e26fbd4_11427.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.41, 9.38, 9.34, 9.31, 9.25, 8.99, 8.77]\n Curvature: [0.0, 0.0, 0.0, 0.01, 0.01, -0.0, -0.01]\n Acceleration: [-0.13, -0.24, -0.34, -0.45, -0.57, -0.74, -0.89]\n Course: [0.0, 0.02, 0.01, 0.02, -0.01, -0.05, -0.02]\nWhat is the action of ego car?\nAssistant: The car is moving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as there is not traffic ahead.\n\n",
+    "BDDX_Processed/video/04488604-03cba446_05920.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.29, 14.14, 14.96, 15.97, 14.95, 12.28, 4.57]\n Curvature: [-0.0, 0.0, -0.0, 0.01, -0.0, -0.01, 0.14]\n Acceleration: [0.39, 0.35, 0.48, -0.04, -0.56, -2.16, -2.7]\n Course: [0.0, 0.0, 0.0, 0.0, -0.28, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is attempting to merge.\n\n",
+    "BDDX_Processed/video/03e19d79-c79b6a0f_01676.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.09, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: The light is still red.\n\n",
+    "BDDX_Processed/video/023a07ea-89f817c3_05058.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.66, 2.34, 4.71, 6.54, 7.83, 8.24, 8.72]\n Curvature: [-0.78, -0.09, 0.02, -0.02, 0.01, -0.02, -0.01]\n Acceleration: [0.87, 1.42, 1.55, 1.11, 0.58, 0.2, 0.48]\n Course: [0.0, -1.12, 0.25, -0.11, 0.0, 0.34, -0.25]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as to continue down the street as it was not at a stop sign or traffic light.\n\n",
+    "BDDX_Processed/video/135a9651-7271668f_13392.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.41, 0.31, 0.33, 0.51, 0.79, 1.22, 1.92]\n Curvature: [0.06, 0.05, 0.05, 0.04, 0.02, -0.0, -0.01]\n Acceleration: [-0.11, -0.0, 0.15, 0.36, 0.59, 0.8, 0.92]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped.\n\n",
+    "BDDX_Processed/video/009215e2-8500bd0b_01804.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.36, 4.35, 4.34, 4.33, 4.33, 4.42, 4.5]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.2, -0.11, -0.02, 0.07, 0.15, 0.2, 0.26]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down a little\n\nHuman: Why does the ego car doing this?\nAssistant: because it is approaching a stopped car at an intersection.\n\n",
+    "BDDX_Processed/video/0569fe1c-ffe5f96c_00839.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.24, 7.16, 7.87, 8.23, 7.93, 7.63, 7.39]\n Curvature: [0.0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.0]\n Acceleration: [1.01, 1.01, 0.71, 0.07, -0.27, -0.42, -0.3]\n Course: [0.0, -0.27, -0.37, -0.3, 0.0, 47.66, 59.58]\nWhat is the action of ego car?\nAssistant: The car maintains a slow speed\n\nHuman: Why does the ego car doing this?\nAssistant: trying to decide what lane to be in.\n\n",
+    "BDDX_Processed/video/06ec79fd-a832f627_06810.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.07, 2.78, 4.45, 6.0, 6.97, 7.6, 7.98]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.48, 1.83, 1.79, 1.37, 0.89, 0.55, 0.32]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.04, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned green and the car ahead of them has accelerated.\n\n",
+    "BDDX_Processed/video/156dcd52-d023f184_14534.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.29, 13.25, 13.21, 13.17, 13.11, 12.99, 12.87]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.0]\n Acceleration: [0.3, 0.14, -0.02, -0.18, -0.32, -0.28, -0.24]\n Course: [0.0, -0.09, -0.09, -0.09, -0.05, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it is in a left turn only lane\n\n",
+    "BDDX_Processed/video/17cea723-b622fe45_15873.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.23, 1.35, 3.12, 3.84, 4.36, 6.21, 8.03]\n Curvature: [0.0, 0.0, 0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.49, 1.11, 1.04, 0.28, 0.94, 1.42, 1.25]\n Course: [0.0, -0.02, 0.0, 0.0, 0.0, 0.0, 0.05]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/02c2a4d8-94d32b2f_05287.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.37, 4.44, 5.43, 6.34, 7.02, 7.76, 8.34]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.25, 1.04, 1.04, 0.87, 0.81, 0.7, 0.32]\n Course: [0.0, 0.0, 0.0, -0.01, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates forward again\n\nHuman: Why does the ego car doing this?\nAssistant: as it moves through an intersection\n\n",
+    "BDDX_Processed/video/016c5aac-f5e49c84_03692.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.91, 9.79, 8.64, 7.01, 5.34, 3.45, 1.75]\n Curvature: [0.04, 0.02, 0.0, 0.02, -0.04, -0.59, -1.08]\n Acceleration: [-1.95, -2.32, -2.69, -3.07, -3.36, -2.99, -2.52]\n Course: [0.0, -0.03, -0.02, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: so that it can park.\n\n",
+    "BDDX_Processed/video/0c674b1e-b5bed7ba_09871.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.03, 0.2, 0.41]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/11b47a8a-e8b493d6_12676.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [20.62, 19.92, 20.49, 19.33, 18.58, 17.25, 16.41]\n Curvature: [0.04, 0.05, 0.04, 0.02, 0.04, 0.02, 0.01]\n Acceleration: [-0.94, -0.05, -0.13, -0.52, -0.79, -0.39, -1.22]\n Course: [0.0, -4.09, -1.46, -2.95, -2.22, 0.73, 3.78]\nWhat is the action of ego car?\nAssistant: The car is driving quickly\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/025b2ded-47ac9b1d_05109.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.41, 6.11, 7.68, 7.91, 7.68, 8.27, 9.2]\n Curvature: [0.0, -0.0, 0.02, 0.01, 0.01, -0.0, -0.0]\n Acceleration: [-0.15, 0.18, 0.58, 0.02, -0.14, 0.48, 0.04]\n Course: [0.0, 0.0, -0.03, 0.22, 0.0, 0.67, 0.18]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/1b71edc8-18a1ff2d_17673.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.4, 6.28, 6.16, 6.04, 5.96, 6.03, 6.09]\n Curvature: [-0.03, -0.02, -0.01, -0.01, -0.0, -0.0, -0.0]\n Acceleration: [-0.34, -0.27, -0.21, -0.14, -0.08, 0.01, 0.1]\n Course: [0.0, 0.09, 0.09, 0.09, 0.01, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car moves slightly to the left\n\nHuman: Why does the ego car doing this?\nAssistant: to give space to a parked bus in the right lane.\n\n",
+    "BDDX_Processed/video/0c140522-57d3aac5_09713.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.0, 10.21, 10.41, 10.62, 10.85, 11.07, 10.97]\n Curvature: [-0.04, -0.02, 0.0, 0.01, -0.0, -0.01, -0.0]\n Acceleration: [1.07, 0.86, 0.67, 0.47, 0.29, 0.11, -0.3]\n Course: [0.0, 0.39, 0.26, -0.11, -0.11, -0.11, 0.25]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no other cars on the road.\n\n",
+    "BDDX_Processed/video/1ea408f5-29059b61_19201.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.0, 12.09, 12.18, 12.28, 12.4, 12.52, 12.36]\n Curvature: [-0.02, -0.01, -0.0, 0.0, -0.0, -0.01, -0.0]\n Acceleration: [-0.12, 0.03, 0.18, 0.2, 0.06, -0.1, -0.48]\n Course: [0.0, -0.48, -0.48, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear.\n\n",
+    "BDDX_Processed/video/03afa5eb-b47a1969_01166.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.53, 1.02, 1.78, 2.97, 4.78, 6.51, 8.12]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.01]\n Acceleration: [0.6, 0.81, 1.31, 1.86, 2.28, 2.16, 1.69]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is free of traffic.\n\n",
+    "BDDX_Processed/video/1aa857d4-850fac0a_17258.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.44, 4.35, 1.0, 0.38, 0.53, 0.44, 0.17]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.61, -3.93, -2.21, -0.35, 0.02, -0.19, -0.24]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a fast stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turns red.\n\n",
+    "BDDX_Processed/video/0fc3f3c1-60c29dcd_11547.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.96, 0.26, 0.0, 0.0, 0.03, 0.09, 0.0]\n Curvature: [-0.02, -0.01, -0.01, -0.01, 0.03, 0.23, 0.23]\n Acceleration: [-0.64, -0.4, -0.07, 0.0, 0.07, -0.04, -0.02]\n Course: [0.0, -0.09, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops behind an SUV\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/158aa019-bbe75f01_14587.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.61, 8.47, 8.3, 7.99, 7.68, 6.5, 5.24]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [-0.12, -0.12, -0.21, -0.26, -0.63, -1.11, -1.41]\n Course: [0.0, -0.02, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is going down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/1dd3640d-bbfd155a_18758.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.64, 13.3, 13.15, 12.74, 12.46, 12.41, 12.45]\n Curvature: [0.01, 0.02, 0.01, 0.02, 0.01, 0.04, 0.04]\n Acceleration: [-0.27, -0.23, -0.27, -0.34, -0.16, -0.01, 0.05]\n Course: [0.0, -0.38, -0.77, -0.75, -0.49, -1.53, -1.01]\nWhat is the action of ego car?\nAssistant: The car proceeds down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear.\n\n",
+    "BDDX_Processed/video/0d6dc44c-c4ec8d75_10537.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.33, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.05, 0.03, -0.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/01ff4acd-b2311e18_00548.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.23, 6.16, 6.1, 6.03, 5.96, 5.81, 5.67]\n Curvature: [-0.01, -0.01, -0.01, -0.01, -0.0, -0.0, -0.0]\n Acceleration: [-0.55, -0.51, -0.47, -0.44, -0.42, -0.46, -0.5]\n Course: [0.0, -0.09, -0.09, -0.09, -0.01, -0.02, -0.02]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped.\n\n",
+    "BDDX_Processed/video/20ff37cb-73e58926_20328.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.93, 12.01, 11.15, 9.54, 8.1, 7.39, 6.22]\n Curvature: [-0.01, -0.01, -0.0, -0.0, -0.0, 0.01, 0.01]\n Acceleration: [-1.69, -2.05, -2.39, -2.27, -2.22, -2.97, -3.57]\n Course: [0.0, 0.32, 0.32, 0.05, -0.06, -0.33, -0.26]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light ahead is red.\n\n",
+    "BDDX_Processed/video/179ab72e-13e61ed1_15803.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, -0.14, 0.0, 0.0, -0.07, -0.08, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the driver has parked.\n\n",
+    "BDDX_Processed/video/12dc1ad6-e8d7e789_13111.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [24.35, 23.84, 23.29, 22.06, 20.77, 20.07, 18.92]\n Curvature: [0.0, 0.02, 0.01, 0.02, 0.0, 0.01, 0.03]\n Acceleration: [-0.37, -0.31, -0.52, -0.9, -0.64, -0.62, -0.51]\n Course: [0.0, -0.98, -0.68, -1.55, -0.69, -0.56, -0.87]\nWhat is the action of ego car?\nAssistant: The car makes it ways to the second to last right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it's going to turn soon.\n\n",
+    "BDDX_Processed/video/06924f46-1708b96f_03528.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.39, 7.62, 4.75, 1.87, 0.51, 0.31, 0.66]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-0.8, -2.24, -2.78, -2.03, -0.78, 0.06, 0.4]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there's a stop sign.\n\n",
+    "BDDX_Processed/video/06ff7d61-734f0911_00654.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.48, 0.58, 0.67, 0.77, 0.86, 0.93, 0.99]\n Curvature: [0.21, 0.18, 0.16, 0.14, 0.12, 0.08, 0.03]\n Acceleration: [0.43, 0.4, 0.37, 0.34, 0.31, 0.14, -0.03]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car is beginning to slowly move forward.\n\nHuman: Why does the ego car doing this?\nAssistant: The driver is anticipating that the light is about to change to green.\n\n",
+    "BDDX_Processed/video/1accee17-deeb6eb8_17427.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [-1.0, 26.85, 27.18, 27.0, 26.76, 26.13, 27.09]\n Curvature: [0.0, 0.04, 0.0, 0.0, 0.0, 0.01, -0.01]\n Acceleration: [28.02, -0.08, 0.01, -0.07, 0.01, -0.08, 0.47]\n Course: [0.0, -2.99, 0.12, 0.11, 0.44, -0.62, -2.74]\nWhat is the action of ego car?\nAssistant: The car is going down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/01ff4acd-abd98a91_04943.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.94, 9.38, 11.28, 10.42, 11.5, 12.25, 13.43]\n Curvature: [0.0, 0.0, -0.02, -0.0, 0.0, -0.02, -0.0]\n Acceleration: [0.49, 0.95, -0.17, 0.23, 0.14, 0.15, -0.21]\n Course: [0.0, 1.14, 1.92, 0.29, -0.28, 0.14, 0.22]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/1d092676-d1d3ad6b_18326.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.02, 7.15, 6.99, 6.57, 6.32, 6.34, 5.73]\n Curvature: [-0.0, -0.0, 0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [1.7, 0.5, -0.3, -0.24, 0.09, -0.29, -0.14]\n Course: [0.0, 0.35, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: the car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/0fb0e6f3-ce79dfa4_11499.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.55, 6.65, 5.86, 4.96, 4.08, 3.38, 2.77]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-2.96, -2.97, -2.98, -2.99, -2.94, -2.45, -2.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because it's at a stop sign.\n\n",
+    "BDDX_Processed/video/049f2340-26fc3c56_03601.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.39, 0.89, 1.39, 2.14, 2.98]\n Curvature: [0.0, 0.0, 0.03, 0.09, 0.2, 0.44, -4.4]\n Acceleration: [0.0, 0.26, 0.52, 0.7, 0.82, 1.01, 1.04]\n Course: [0.0, 0.0, 0.0, 0.0, -31.77, -63.55, 5.48]\nWhat is the action of ego car?\nAssistant: The car accelerates slowly\n\nHuman: Why does the ego car doing this?\nAssistant: to make a right turn.\n\n",
+    "BDDX_Processed/video/1f7ba5ab-fa498166_19660.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.1, 4.93, 4.84, 6.24, 7.52, 7.36, 7.2]\n Curvature: [-0.25, -0.02, 0.2, 0.07, -0.06, -0.03, -0.01]\n Acceleration: [1.78, 1.5, 1.22, 1.23, 1.13, 0.38, -0.26]\n Course: [0.0, 0.91, 1.09, 0.53, 0.82, 0.7, 0.3]\nWhat is the action of ego car?\nAssistant: The car is emerging from the far right lane to the middle lane\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no cars in the way.\n\n",
+    "BDDX_Processed/video/02fa8491-2a4967e3_03591.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.57, 6.87, 6.2, 5.96, 5.66, 4.84, 4.06]\n Curvature: [0.01, -0.02, -0.04, 0.0, 0.04, 0.03, 0.02]\n Acceleration: [-1.47, -1.18, -0.91, -0.97, -1.04, -1.22, -1.29]\n Course: [0.0, 0.0, -0.01, -0.05, -0.02, 0.0, 0.03]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: The vehicle ahead of the car is moving slowly.\n\n",
+    "BDDX_Processed/video/0062e803-38c0a33a_01149.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.23, 0.17, 0.11, 0.06, 0.01, 0.11, 0.22]\n Curvature: [-0.04, -0.04, -0.04, -0.04, -0.04, -0.02, -0.01]\n Acceleration: [-0.08, -0.04, 0.01, 0.05, 0.1, 0.18, 0.28]\n Course: [0.0, 0.01, 0.02, 0.01, 0.0, -0.01, -0.02]\nWhat is the action of ego car?\nAssistant: The car starts to accelerate as it moves forward.\n\nHuman: Why does the ego car doing this?\nAssistant: The car in front has started to drive forward.\n\n",
+    "BDDX_Processed/video/15f559a7-20524e66_14838.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.56, 4.72, 4.16, 5.31, 5.65, 6.4, 5.6]\n Curvature: [0.0, 0.0, -0.03, -0.04, -0.01, 0.0, -0.03]\n Acceleration: [0.08, 0.07, -0.44, 0.73, 0.72, 0.03, -1.06]\n Course: [0.0, -0.15, 1.01, -2.52, -2.51, -0.32, 0.92]\nWhat is the action of ego car?\nAssistant: The car drives down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear.\n\n",
+    "BDDX_Processed/video/0d13ae3d-1bf40521_10310.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.98, 11.52, 13.43, 14.0, 13.88, 13.68, 13.31]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.01, 0.0, -0.0]\n Acceleration: [1.0, 0.86, 0.47, 0.09, -0.13, -0.1, -0.12]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.23, -0.17]\nWhat is the action of ego car?\nAssistant: The car drives down a street\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear ahead.\n\n",
+    "BDDX_Processed/video/04d49b8a-f0e342cb_03726.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.13, 3.23, 3.33, 3.54, 3.9, 4.26, 4.94]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.26, 0.39, 0.52, 0.73, 1.05, 1.37, 1.57]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is carefully steering to the right and moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: The car is maneuvering between a parked car on the right hand side of the road and a vehicle stopped in the roadway ahead of the car.\n\n",
+    "BDDX_Processed/video/007c01ea-ad9f940b_04485.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.73, 9.46, 9.94, 10.22, 10.9, 10.7, 9.62]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, 0.0, -0.0]\n Acceleration: [-0.31, 0.14, 0.16, 0.34, 0.13, -0.2, -1.01]\n Course: [0.0, -0.46, 0.0, -0.07, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: the car is moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear\n\n",
+    "BDDX_Processed/video/04056725-91d0bd08_05790.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.27, 0.39, 0.51, 0.62, 0.72, 0.82, 1.22]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.29, 0.29, 0.29, 0.35, 0.5, 0.67, 1.13]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating and moving to the left\n\nHuman: Why does the ego car doing this?\nAssistant: because it is pulling out of the parking spot.\n\n",
+    "BDDX_Processed/video/0478d053-0eb9fe2f_06015.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [20.41, 20.86, 21.27, 21.64, 22.05, 22.41, 22.68]\n Curvature: [-0.02, -0.02, -0.02, -0.02, -0.02, -0.01, 0.0]\n Acceleration: [0.43, 0.56, 0.63, 0.61, 0.56, 0.43, 0.17]\n Course: [0.0, 0.4, 0.4, 0.4, 0.45, 0.57, 0.57]\nWhat is the action of ego car?\nAssistant: The car is merging into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane they are currently in is an exit only lane.\n\n",
+    "BDDX_Processed/video/191d17e1-aaeadac1_16603.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.86, 0.89, 0.45, 0.22, 0.16, 0.3]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.41, 0.11, -0.13, -0.17, -0.17, 0.09, 0.16]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slowly edges forward\n\nHuman: Why does the ego car doing this?\nAssistant: as it waits for oncoming traffic to clear so that it can turn left.\n\n",
+    "BDDX_Processed/video/1e7af489-44f98c39_19117.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.55, 7.29, 6.43, 4.33, 4.32, 6.27, 6.93]\n Curvature: [-0.13, 0.28, 0.58, -0.37, 0.0, -0.01, 0.0]\n Acceleration: [1.04, 0.08, -1.41, -0.92, 0.86, 1.23, -0.59]\n Course: [0.0, -0.19, -0.11, 0.2, -0.42, -1.05, -0.21]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is empty.\n\n",
+    "BDDX_Processed/video/1cd5d614-61eedf38_18251.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.94, 11.52, 12.13, 12.6, 13.11, 13.6, 14.07]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.01, -0.01]\n Acceleration: [1.16, 1.11, 1.05, 1.0, 0.94, 0.82, 0.67]\n Course: [0.0, -0.25, -0.23, 0.04, 0.05, 0.04, 0.01]\nWhat is the action of ego car?\nAssistant: The car travels quickly down the rain soaked road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear of traffic\n\n",
+    "BDDX_Processed/video/02d478d1-e6811391_05351.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.41, 17.28, 18.53, 18.32, 18.96, 20.97, 21.56]\n Curvature: [0.0, -0.01, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [1.78, 0.95, -0.08, -0.02, 0.43, 0.9, -0.55]\n Course: [0.0, 0.25, 0.0, -0.06, -0.13, 0.18, 0.0]\nWhat is the action of ego car?\nAssistant: The car is going down the street at a steady speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear.\n\n",
+    "BDDX_Processed/video/130add4f-42fc5213_13220.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.34, 3.12, 3.5, 4.13, 4.74, 5.29, 5.95]\n Curvature: [-0.13, -0.2, -0.14, -0.07, -0.04, -0.02, -0.04]\n Acceleration: [-0.43, 0.1, 0.57, 0.79, 0.75, 0.79, 0.91]\n Course: [0.0, 3.46, 4.5, 2.81, 2.36, 1.11, 0.61]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's making a right turn.\n\n",
+    "BDDX_Processed/video/1b2bbaec-93e98a2a_17617.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.84, 0.46, 0.48, 0.58, 0.17, 0.0, 0.0]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.74, -0.62, 0.23, -0.11, -0.29, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0fb8db78-3f42b797_11516.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.62, 15.54, 15.71, 15.84, 15.35, 14.23, 14.1]\n Curvature: [0.0, 0.01, 0.01, -0.0, -0.01, -0.01, 0.02]\n Acceleration: [-0.07, 0.0, 0.2, -0.21, -0.67, -0.62, -0.64]\n Course: [0.0, 0.01, 0.0, 0.0, 0.21, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the path is clear.\n\n",
+    "BDDX_Processed/video/0b7b613f-aca8634e_09383.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.63, 1.73, 1.83, 1.83, 1.77, 1.26, 0.9]\n Curvature: [0.06, 0.08, 0.29, 3.53, 6.04, 3.57, 1.41]\n Acceleration: [0.53, 0.31, 0.08, -0.22, -0.47, -0.35, -0.08]\n Course: [0.0, 0.5, 0.49, 0.45, 2.32, 4.18, 1.05]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because several people are crossing the street.\n\n",
+    "BDDX_Processed/video/025ecc28-3d894af1_05123.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.09, -0.06, -0.04, -0.02, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light remains red.\n\n",
+    "BDDX_Processed/video/1e6a6965-4b3d39fe_19076.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.36, 6.8, 7.2, 7.67, 8.13, 8.64, 9.08]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, -0.01]\n Acceleration: [1.06, 1.11, 1.15, 1.18, 1.18, 1.18, 0.99]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car emerged onto another road on the right side\n\nHuman: Why does the ego car doing this?\nAssistant: because the road was clear and other cars were not in the same lane.\n\n",
+    "BDDX_Processed/video/0b5c2365-35b41936_09311.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.07, 12.9, 12.72, 12.47, 12.16, 11.55, 10.86]\n Curvature: [0.01, 0.01, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.22, -0.32, -0.43, -0.64, -0.89, -1.27, -1.61]\n Course: [0.0, -0.27, -0.2, 0.0, 0.01, 0.02, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no other cars in its lane.\n\n",
+    "BDDX_Processed/video/04056725-14e1b11d_05788.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.75, 0.35, 0.54, 1.37, 2.08, 3.12, 4.49]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.94, -0.27, 0.55, 0.83, 1.0, 1.32, 1.24]\n Course: [0.0, 0.0, 0.0, 0.02, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins driving down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving forward\n\n",
+    "BDDX_Processed/video/11589021-eb71270b_12442.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.13, 19.14, 19.15, 19.15, 19.16, 19.2, 19.22]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.01, 0.0, 0.0]\n Acceleration: [-0.02, 0.01, 0.03, 0.05, 0.07, 0.06, 0.06]\n Course: [0.0, -0.02, -0.01, -0.02, -0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car goes through an intersection.\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a green light.\n\n",
+    "BDDX_Processed/video/06836e19-0798237e_03383.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.81, 7.7, 8.63, 9.45, 9.49, 9.6, 9.33]\n Curvature: [0.0, -0.0, -0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.33, 0.23, 0.49, 0.19, -0.05, 0.04, -0.16]\n Course: [0.0, 0.0, 0.0, -0.18, 0.0, 0.0, -0.06]\nWhat is the action of ego car?\nAssistant: The car maintains a steady, moderate, speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear.\n\n",
+    "BDDX_Processed/video/04076a65-57bf4de3_02035.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [23.13, 23.24, 22.94, 22.75, 22.49, 22.23, 21.95]\n Curvature: [-0.0, 0.0, 0.01, -0.01, 0.01, -0.01, 0.0]\n Acceleration: [0.08, -0.0, -0.14, -0.3, 0.02, -0.06, -0.09]\n Course: [0.0, -0.12, -0.52, -0.41, 0.33, -0.84, -0.16]\nWhat is the action of ego car?\nAssistant: The car maintains a constant speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/10df32aa-9b9779b7_12036.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.24, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.98, -0.02, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.07, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/073d4e2a-55c1f733_02080.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.44, 15.28, 17.81, 19.2, 19.27, 18.85, 16.75]\n Curvature: [0.0, 0.01, -0.01, 0.02, 0.02, 0.01, -0.01]\n Acceleration: [1.09, 0.68, 0.89, -0.01, -0.06, -0.38, -0.38]\n Course: [0.0, 0.0, 0.64, 0.2, 0.49, 0.5, -0.15]\nWhat is the action of ego car?\nAssistant: The car maintains a constant speed and spacing with the vehicle ahead\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/07116ae0-0f12a817_00889.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [29.38, 30.29, 30.07, 29.97, 30.69, 31.13, 30.67]\n Curvature: [0.0, -0.0, -0.0, -0.01, -0.01, 0.04, 0.0]\n Acceleration: [0.54, 0.05, -0.04, -0.0, 0.52, 0.03, -0.65]\n Course: [0.0, 0.26, -0.02, -0.12, 0.13, 0.07, -0.48]\nWhat is the action of ego car?\nAssistant: The car maintains a constant speed and lane\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/0ecc89ce-a5dc232e_11224.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.75, 0.54, 0.43, 0.24, 0.0, 0.0, 0.0]\n Curvature: [0.27, 0.26, 0.06, -0.04, -0.05, -0.05, -0.05]\n Acceleration: [-0.16, -0.11, -0.03, -0.2, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: for the red light.\n\n",
+    "BDDX_Processed/video/07a3c8b9-63776739_07250.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.28, 13.4, 14.03, 14.18, 14.2, 13.97, 12.98]\n Curvature: [-0.0, 0.0, 0.0, -0.01, 0.0, -0.0, -0.0]\n Acceleration: [1.03, 0.45, 0.45, -0.07, 0.02, -0.28, -1.3]\n Course: [0.0, -0.56, -0.16, 0.27, 0.21, -0.14, 0.1]\nWhat is the action of ego car?\nAssistant: The car drives steadily down a road\n\nHuman: Why does the ego car doing this?\nAssistant: since the road is clear.\n\n",
+    "BDDX_Processed/video/17ffc2bb-5e67da48_16021.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.82, 5.33, 2.88, 0.48, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.04, 0.03, 0.01, 0.01, 0.01]\n Acceleration: [0.17, -0.75, -1.29, -0.65, -0.02, 0.0, 0.0]\n Course: [0.0, -2.23, -0.38, -3.22, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is pulling up to the light and stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because it is red and there is traffic crossing the intersection.\n\n",
+    "BDDX_Processed/video/06cbfcda-957a0b95_04113.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.73, 3.54, 6.34, 5.21, 5.47, 6.1, 9.0]\n Curvature: [0.0, -0.01, -0.0, -0.0, -0.0, 0.02, 0.0]\n Acceleration: [0.11, 1.02, 0.99, -0.72, 0.19, 1.18, -0.23]\n Course: [0.0, 0.24, -0.24, -0.19, 0.0, 0.14, 0.01]\nWhat is the action of ego car?\nAssistant: The car is slowly moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: to keep up with slow moving heavy traffic\n\n",
+    "BDDX_Processed/video/160a76cc-b5993b9d_14891.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.36, 0.52, 0.66, 0.78, 0.8, 0.83, 0.71]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.38, 0.33, 0.27, 0.18, 0.02, -0.14, -0.21]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car reverses\n\nHuman: Why does the ego car doing this?\nAssistant: to line itself up.\n\n",
+    "BDDX_Processed/video/0ea2cab3-e5d8177a_11189.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.99, 13.85, 15.44, 16.33, 16.45, 16.87, 16.86]\n Curvature: [-0.0, 0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.44, 1.07, 0.56, 0.12, 0.14, 0.17, -0.18]\n Course: [0.0, 0.0, -0.02, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street ahead is clear.\n\n",
+    "BDDX_Processed/video/0e8e5273-bbb295ad_11106.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.74, 5.97, 6.2, 6.44, 6.67, 6.9, 7.13]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.08, 1.03, 0.98, 0.94, 0.89, 0.84, 0.78]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car steers to the left\n\nHuman: Why does the ego car doing this?\nAssistant: to avoid parked vehicles to its right.\n\n",
+    "BDDX_Processed/video/17662c87-9476da54_15723.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.46, 1.96, 3.4, 5.14, 6.7, 8.14, 9.39]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.64, 0.97, 1.9, 2.14, 1.91, 1.73, 1.57]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12]\nWhat is the action of ego car?\nAssistant: The car accelerates forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the road ahead is clear.\n\n",
+    "BDDX_Processed/video/05be3c16-e85868fd_01410.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.0, 3.99, 3.98, 4.13, 4.49, 4.85, 5.32]\n Curvature: [-0.15, -0.17, -0.18, -0.17, -0.12, -0.07, -0.04]\n Acceleration: [-0.41, -0.07, 0.24, 0.56, 0.83, 1.11, 1.2]\n Course: [0.0, 2.32, 1.55, 2.14, 2.14, 2.14, 0.75]\nWhat is the action of ego car?\nAssistant: The car is turning right\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may enter another road.\n\n",
+    "BDDX_Processed/video/02346f91-87969c22_05038.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped in the road\n\nHuman: Why does the ego car doing this?\nAssistant: because there is standstill traffic ahead\n\n",
+    "BDDX_Processed/video/1c804640-adb884fc_18130.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/101002a3-af1f7874_11695.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.15, 6.44, 7.54, 8.59, 8.69, 8.69, 9.38]\n Curvature: [0.0, -0.0, 0.0, 0.0, -0.0, 0.0, -0.0]\n Acceleration: [3.15, 1.83, 0.98, 0.48, 0.01, 0.31, 0.87]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving forward at normal speed on the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the path is clear.\n\n",
+    "BDDX_Processed/video/05a93853-0d9f37e7_01251.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.21, 3.57, 3.75, 3.69, 3.63, 3.78, 4.34]\n Curvature: [-0.0, 0.0, 0.01, 0.02, 0.01, 0.0, 0.01]\n Acceleration: [0.31, 0.26, 0.14, -0.07, 0.18, 0.35, 0.3]\n Course: [0.0, 0.0, 0.0, 0.0, -0.53, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car maintains a slow speed\n\nHuman: Why does the ego car doing this?\nAssistant: to allow another vehicle to begin merging into the lane.\n\n",
+    "BDDX_Processed/video/06924f46-983ee289_03586.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.09, 12.38, 13.23, 14.08, 14.48, 14.61, 14.68]\n Curvature: [0.0, 0.0, -0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.98, 1.24, 0.97, 0.68, 0.32, 0.11, -0.16]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.18, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving ahead\n\nHuman: Why does the ego car doing this?\nAssistant: as there are no cars in front of it.\n\n",
+    "BDDX_Processed/video/15569b20-8d825141_14435.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.1, 0.15, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.12, -0.1, -0.02, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/064bad4e-ae51d829_06523.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.95, 4.85, 4.52, 4.48, 4.97, 5.75, 6.33]\n Curvature: [-0.07, -0.07, -0.12, -0.16, -0.15, -0.06, -0.08]\n Acceleration: [-0.19, -0.3, -0.13, 0.27, 0.8, 0.84, 0.85]\n Course: [0.0, 1.7, -69.54, 2.55, 3.5, 3.28, 3.47]\nWhat is the action of ego car?\nAssistant: The car is turning right\n\nHuman: Why does the ego car doing this?\nAssistant: to enter another road.\n\n",
+    "BDDX_Processed/video/0b01ca80-852faffa_09137.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.0, 8.77, 7.54, 6.3, 5.39, 5.48, 5.57]\n Curvature: [0.08, 0.08, 0.08, 0.08, 0.08, 0.06, 0.04]\n Acceleration: [-3.74, -3.31, -2.88, -2.46, -2.06, -1.42, -0.77]\n Course: [0.0, -0.12, -0.12, -0.12, -0.18, -0.36, -0.36]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: as the vehicle in front of it brakes for a speed bump.\n\n",
+    "BDDX_Processed/video/114e578a-47a24858_12382.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.01, 7.5, 8.0, 8.49, 8.94, 9.57, 10.21]\n Curvature: [0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.0]\n Acceleration: [1.11, 1.39, 1.67, 1.95, 2.16, 2.24, 2.32]\n Course: [0.0, -0.26, -0.26, -0.26, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car speeds up slightly\n\nHuman: Why does the ego car doing this?\nAssistant: as it begins to merge onto the roadway.\n\n",
+    "BDDX_Processed/video/007c01ea-ad9f940b_04486.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.57, 10.58, 10.59, 10.56, 10.47, 10.39, 10.39]\n Curvature: [0.0, -0.0, -0.0, -0.1, -0.33, -0.56, -0.72]\n Acceleration: [0.1, 0.02, -0.05, -0.09, -0.1, -0.1, -0.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.4]\nWhat is the action of ego car?\nAssistant: the car is turning\n\nHuman: Why does the ego car doing this?\nAssistant: so as to take a right turn\n\n",
+    "BDDX_Processed/video/16269211-6ffb8bde_14959.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.83, 10.09, 11.07, 11.83, 12.34, 12.65, 12.58]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [1.67, 1.58, 1.38, 1.01, 0.61, 0.15, -0.38]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls over slightly to the left\n\nHuman: Why does the ego car doing this?\nAssistant: to pass by a car parked on the side of the road.\n\n",
+    "BDDX_Processed/video/06e4a00e-9ed56776_00104.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.51, 1.17, 0.83, 0.49, 0.23, 0.17, 0.11]\n Curvature: [2.47, 2.73, 2.99, 3.25, 3.44, 3.44, 3.44]\n Acceleration: [-1.05, -0.97, -0.89, -0.81, -0.72, -0.47, -0.23]\n Course: [0.0, -0.09, -0.09, -0.09, 0.01, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car reverses slowly\n\nHuman: Why does the ego car doing this?\nAssistant: to better park.\n\n",
+    "BDDX_Processed/video/01df5158-81b61006_00260.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.96, 7.96, 6.3, 5.13, 4.47, 4.14, 4.14]\n Curvature: [-0.0, 0.0, -0.0, -0.02, -0.06, -0.19, -0.39]\n Acceleration: [-2.61, -2.34, -1.8, -1.18, -0.65, -0.23, -0.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.01]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it going to turn to the right into an alleyway\n\n",
+    "BDDX_Processed/video/06d54ae6-76749641_00020.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.34, 0.41, 0.48, 0.46, 0.25, 0.04, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.26, 0.1, -0.06, -0.18, -0.22, -0.26, -0.19]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car inches forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front moves forward.\n\n",
+    "BDDX_Processed/video/0f5580fb-4e846aef_11394.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.73, 5.01, 3.32, 2.09, 0.97, 0.56, 0.23]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.62, -2.26, -2.81, -2.16, -1.51, -0.91, -0.37]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as the light ahead turned red.\n\n",
+    "BDDX_Processed/video/11381662-48d2471f_12347.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.27, 3.04, 3.13, 3.63, 4.27, 4.9, 5.48]\n Curvature: [-0.0, -0.63, -0.66, 0.03, 0.06, 0.03, 0.0]\n Acceleration: [0.22, 0.21, 0.41, 0.85, 0.93, 0.94, 0.89]\n Course: [0.0, 0.02, 7.53, 7.53, 6.89, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning right.\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is entering another street.\n\n",
+    "BDDX_Processed/video/08688b9f-4e2c11c8_07623.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.41, 0.68, 0.9, 1.05, 1.42, 1.84, 2.34]\n Curvature: [0.19, 0.19, 0.17, 0.13, 0.06, -0.0, -0.01]\n Acceleration: [0.42, 0.36, 0.35, 0.41, 0.58, 0.73, 0.86]\n Course: [0.0, -0.23, -0.22, -0.22, -0.46, -0.51, -0.51]\nWhat is the action of ego car?\nAssistant: The car is accelerating slowly\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic in front of it is accelerating slowly.\n\n",
+    "BDDX_Processed/video/09f81455-e194d277_08623.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.89, 12.64, 13.89, 14.4, 15.02, 15.57, 15.9]\n Curvature: [0.01, 0.02, 0.01, 0.0, 0.01, 0.01, -0.01]\n Acceleration: [2.74, 2.13, 1.52, 0.96, 0.82, 0.74, 0.7]\n Course: [0.0, -0.09, -0.06, -0.06, -0.05, -0.12, -0.12]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at normal speed.\n\n",
+    "BDDX_Processed/video/06ff7d61-1fa52d1c_06851.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.43, 6.94, 6.45, 5.96, 5.5, 5.23, 4.97]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.89, -1.78, -1.67, -1.56, -1.45, -1.31, -1.16]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: for the stop sign ahead.\n\n",
+    "BDDX_Processed/video/17662c87-e55cb57e_15746.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.35, 8.27, 9.27, 10.55, 10.91, 10.95, 9.99]\n Curvature: [-0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.07, 0.36, 1.02, 0.67, 0.16, -0.39, -0.72]\n Course: [0.0, 0.0, -0.04, 0.0, 0.0, 0.02, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving forward at a steady pace.\n\n",
+    "BDDX_Processed/video/0dd60257-e68ca2cd_10725.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.47, 2.08, 2.22, 2.26, 2.25, 2.15, 1.96]\n Curvature: [0.0, 0.01, 0.01, 0.03, -0.01, -0.02, 0.01]\n Acceleration: [0.65, 0.4, 0.14, 0.02, -0.06, -0.16, -0.17]\n Course: [0.0, -0.16, 0.0, 0.08, 0.32, 0.16, 0.14]\nWhat is the action of ego car?\nAssistant: The car drives slowly forward\n\nHuman: Why does the ego car doing this?\nAssistant: as it drives with other traffic\n\n",
+    "BDDX_Processed/video/0d22eba4-32d54614_10403.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.58, 7.35, 7.15, 6.93, 6.7, 6.42, 6.18]\n Curvature: [0.0, -0.01, -0.03, -0.04, -0.05, -0.03, -0.01]\n Acceleration: [-0.85, -0.88, -0.9, -0.92, -0.95, -1.01, -1.06]\n Course: [0.0, 0.02, 0.01, 0.02, 0.04, 0.05, 0.02]\nWhat is the action of ego car?\nAssistant: The car is driving down a street\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/0714456d-2648a0c9_00997.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.25, 2.04, 2.42, 2.38, 3.21, 4.57, 5.91]\n Curvature: [-0.01, -0.14, -0.14, -0.25, -0.23, -0.17, -0.11]\n Acceleration: [0.51, 0.61, 0.2, 0.48, 1.17, 1.49, 1.44]\n Course: [0.0, 0.64, 2.7, 4.52, 6.43, -72.32, 5.64]\nWhat is the action of ego car?\nAssistant: The car is turning right\n\nHuman: Why does the ego car doing this?\nAssistant: to merge onto another road.\n\n",
+    "BDDX_Processed/video/198d8f5e-2b4d439d_16751.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.19, 0.0, 0.0]\n Curvature: [-0.97, -0.96, -0.96, -0.3, 0.33, 0.46, 2.88]\n Acceleration: [0.0, 0.0, 0.0, -0.12, -0.0, 0.06, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: since the traffic light is red\n\n",
+    "BDDX_Processed/video/14306a74-00fdc9ef_13821.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.76, 6.84, 5.83, 4.73, 4.27, 4.07, 4.35]\n Curvature: [-0.11, 0.02, 0.07, 0.03, -0.05, -0.07, 0.01]\n Acceleration: [-2.05, -1.72, -1.43, -1.21, -0.58, 0.22, 1.25]\n Course: [0.0, 1.35, -0.4, -0.4, -0.54, 0.06, 0.06]\nWhat is the action of ego car?\nAssistant: The car slows down rapidly\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign at the end of the merging lane.\n\n",
+    "BDDX_Processed/video/0d0e69b6-507e6590_10301.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.85, 5.03, 5.21, 5.39, 5.49, 5.17, 4.85]\n Curvature: [0.12, 0.09, 0.06, 0.03, 0.0, 0.0, 0.0]\n Acceleration: [0.62, 0.38, 0.14, -0.1, -0.33, -0.76, -1.19]\n Course: [0.0, -0.34, -0.34, -0.34, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstacles in the path.\n\n",
+    "BDDX_Processed/video/0b88820f-3a647f1d_09423.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.16, 0.24, 0.32, 0.56, 1.0, 1.44, 1.92]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.19, 0.38, 0.55, 0.76, 0.99, 1.22, 1.16]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: after the light turns green.\n\n",
+    "BDDX_Processed/video/1480d3a6-baad66d8_13968.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.23, 0.23, 0.73, 1.99, 3.61, 4.77, 5.32]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.01]\n Acceleration: [0.12, 0.29, 0.95, 1.58, 1.51, 0.95, 0.69]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates and drives down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turns green.\n\n",
+    "BDDX_Processed/video/06fb4e9c-efadbbae_00395.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.18]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.06, -0.0, -0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -34.44, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/136fcc4f-bcfca77d_13440.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.84, 0.51, 0.81, 0.44, 0.45, 0.41, 0.25]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.02, -0.0, -0.04, -0.18, -0.01, -0.09, -0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car backs up\n\nHuman: Why does the ego car doing this?\nAssistant: to park near the sidewalk.\n\n",
+    "BDDX_Processed/video/021c4a16-1c50e818_04995.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.98, 3.21, 2.68, 2.51, 2.93, 3.4, 3.76]\n Curvature: [0.0, 0.0, -0.04, -0.12, -0.39, -0.5, -0.25]\n Acceleration: [-1.25, -0.93, -0.44, 0.19, 0.49, 0.71, 0.82]\n Course: [0.0, 0.0, 0.0, 0.0, 6.17, 7.34, 7.34]\nWhat is the action of ego car?\nAssistant: The car turns right and continues moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear in front.\n\n",
+    "BDDX_Processed/video/028b5d16-be6486a6_02075.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [25.69, 25.72, 25.85, 25.73, 25.73, 25.59, 25.39]\n Curvature: [0.0, -0.01, -0.02, -0.01, -0.01, -0.0, -0.02]\n Acceleration: [0.03, 0.07, -0.03, 0.03, -0.08, -0.07, -0.26]\n Course: [0.0, 0.46, 0.78, 0.59, 0.61, 0.47, 0.69]\nWhat is the action of ego car?\nAssistant: The car maintains a constant speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/0e861c60-15949d4e_11084.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04]\n Acceleration: [-0.23, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is parked in the middle of the road.\n\n",
+    "BDDX_Processed/video/1bc82b26-7383dedd_17788.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.15, 0.34, 0.47, 0.49, 0.23, 0.55]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.14, 0.14, 0.19, 0.07, -0.1, 0.08, 0.3]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves slowly forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is slowly moving forward.\n\n",
+    "BDDX_Processed/video/052fd7cc-b5dee1de_00148.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.81, 2.05, 3.36, 4.4, 5.37, 6.19, 7.08]\n Curvature: [0.2, 0.11, 0.1, 0.15, 0.15, 0.05, 0.08]\n Acceleration: [1.12, 1.38, 1.28, 1.12, 0.99, 0.95, 1.11]\n Course: [0.0, -0.07, -1.23, -3.66, -5.32, -3.66, -5.07]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: to turn left.\n\n",
+    "BDDX_Processed/video/0f837def-ce515e16_11449.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [25.42, 24.72, 25.26, 24.65, 24.5, 25.54, 26.22]\n Curvature: [0.0, 0.01, -0.02, -0.0, 0.02, 0.01, 0.01]\n Acceleration: [-0.23, 0.16, -0.1, -0.15, 0.08, 0.43, -0.36]\n Course: [0.0, 0.0, 0.27, -0.52, -0.76, -1.6, -1.29]\nWhat is the action of ego car?\nAssistant: The car is driving forward at freeway speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/20ed775f-b93a37e6_20300.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.39, 10.07, 10.81, 11.67, 12.56, 13.26, 13.67]\n Curvature: [-0.0, -0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.39, 1.47, 1.54, 1.55, 1.44, 0.51, -0.59]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: to pass the car in front of it.\n\n",
+    "BDDX_Processed/video/1f45df68-26795801_19458.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.53, 0.69, 0.6, 0.77, 1.02, 1.27, 2.18]\n Curvature: [0.11, 0.02, 0.05, 0.03, -0.04, -0.0, -0.04]\n Acceleration: [0.32, 0.03, -0.01, 0.14, 0.17, 0.36, 0.65]\n Course: [0.0, 0.14, -0.09, -0.28, 0.0, 0.0, 0.34]\nWhat is the action of ego car?\nAssistant: The car moves at a very slow speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the roads are covered with snow and the car ahead of it is moving slow.\n\n",
+    "BDDX_Processed/video/1508a138-bf1a6f0b_14201.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.64, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.56, -0.14, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a red light and there is a pedestrian crossing the road.\n\n",
+    "BDDX_Processed/video/05584a52-9725bd6b_06288.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.98, 6.79, 8.12, 9.33, 10.08, 10.47, 10.49]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.77, 1.04, 1.23, 0.95, 0.56, 0.19, 0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: the car is moving quickly down the dark city street\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no traffic.\n\n",
+    "BDDX_Processed/video/0505c36e-579b416c_04118.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.11, 7.02, 6.55, 5.96, 6.76, 7.99, 7.43]\n Curvature: [-0.03, 0.01, 0.0, 0.04, -0.03, 0.01, -0.0]\n Acceleration: [0.09, 0.26, -0.64, 0.09, 0.69, 0.34, -0.75]\n Course: [0.0, 0.0, -0.18, -0.54, -0.7, -0.42, -0.31]\nWhat is the action of ego car?\nAssistant: The car is moving slowly forward\n\nHuman: Why does the ego car doing this?\nAssistant: The car is maintaining distance to the vehicle ahead by keeping pace with traffic\n\n",
+    "BDDX_Processed/video/04dc483a-f595d9f1_06107.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.62, 16.14, 19.57, 22.6, 18.73, 15.9, 15.9]\n Curvature: [0.0, -0.01, -0.02, -0.0, -0.04, 0.01, 0.0]\n Acceleration: [1.36, 0.62, 0.85, -0.38, -1.39, 0.03, 0.51]\n Course: [0.0, 0.28, 3.59, 0.0, 0.22, -1.45, -0.22]\nWhat is the action of ego car?\nAssistant: The car moves down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic is light.\n\n",
+    "BDDX_Processed/video/0ecdbdbb-1d56bde8_11228.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [23.9, 23.67, 23.4, 23.05, 23.02, 23.06, 23.16]\n Curvature: [0.03, 0.01, -0.0, 0.01, 0.01, 0.01, 0.02]\n Acceleration: [-0.03, -0.3, -0.39, -0.28, -0.07, 0.06, 0.02]\n Course: [0.0, -0.23, -0.06, -0.06, -0.14, -0.35, -0.35]\nWhat is the action of ego car?\nAssistant: The car is resuming its speed\n\nHuman: Why does the ego car doing this?\nAssistant: because all traffic is moving well.\n\n",
+    "BDDX_Processed/video/06595ed1-6aa7c911_02629.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.51, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: The car has arrived at an intersection and the light has changed to red.\n\n",
+    "BDDX_Processed/video/2038e4b0-fd759b13_20006.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.23, 6.72, 8.3, 9.54, 10.13, 10.37, 10.07]\n Curvature: [-0.14, -0.09, -0.04, -0.03, -0.01, 0.0, 0.0]\n Acceleration: [1.0, 1.1, 1.02, 0.64, 0.28, 0.01, -0.32]\n Course: [0.0, 5.14, 2.96, 1.02, 0.52, 0.47, -0.26]\nWhat is the action of ego car?\nAssistant: The car is moving forward at a slow steady rate\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving forward steadily\n\n",
+    "BDDX_Processed/video/05cb8e1c-e064ca63_06409.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.1, 7.71, 11.52, 13.36, 14.81, 14.41, 15.14]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [2.22, 1.49, 1.17, 0.33, 0.53, -0.46, 0.9]\n Course: [0.0, 0.22, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then turns right\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear.\n\n",
+    "BDDX_Processed/video/1a5117d3-3b968ba9_17045.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.53, 5.59, 5.65, 5.59, 5.33, 5.08, 4.67]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.16, 0.0, -0.15, -0.35, -0.6, -0.85, -1.05]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives down a road\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear.\n\n",
+    "BDDX_Processed/video/09ac7416-a3f1d634_08388.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.95, 3.46, 4.0, 4.7, 5.64, 6.72, 7.63]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.37, 0.6, 0.83, 1.07, 1.3, 1.28, 1.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turned green and there are no cars in front of it.\n\n",
+    "BDDX_Processed/video/10066516-bc9fa456_11636.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.72, 15.93, 15.82, 15.48, 14.93, 14.46, 13.5]\n Curvature: [0.0, 0.01, 0.01, 0.04, -0.0, 0.01, 0.04]\n Acceleration: [0.23, 0.05, -0.24, -0.5, -0.56, -0.84, -1.33]\n Course: [0.0, -1.07, -0.56, -0.48, -0.34, -0.71, -0.95]\nWhat is the action of ego car?\nAssistant: The car is going down the highway at a steady speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear.\n\n",
+    "BDDX_Processed/video/0dca3d7c-931b1cb4_10678.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.97, 2.01, 2.76, 3.9, 5.15, 6.3, 7.45]\n Curvature: [-0.01, -0.04, -0.5, -0.61, 0.07, -0.07, -0.05]\n Acceleration: [0.0, 0.53, 1.14, 1.52, 1.54, 1.49, 1.48]\n Course: [0.0, 0.0, 8.55, 3.38, 3.62, 5.79, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning right\n\nHuman: Why does the ego car doing this?\nAssistant: to change streets since the traffic is clear\n\n",
+    "BDDX_Processed/video/0193a01e-90028c79_04840.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.94, 10.56, 10.23, 9.85, 9.51, 9.07, 8.62]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.37, -1.41, -1.45, -1.49, -1.54, -1.66, -1.79]\n Course: [0.0, 0.07, 0.07, 0.07, 0.02, 0.05, 0.05]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: due to the road being free of traffic.\n\n",
+    "BDDX_Processed/video/04d49b8a-f0e342cb_06103.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.98, 5.52, 5.07, 4.9, 5.15, 5.4, 5.8]\n Curvature: [-0.03, -0.32, -0.6, -0.71, -0.55, -0.4, -0.25]\n Acceleration: [-1.49, -1.02, -0.55, -0.08, 0.36, 0.81, 1.03]\n Course: [0.0, 4.26, 4.26, 2.25, 2.25, 2.25, 2.6]\nWhat is the action of ego car?\nAssistant: The car is turning right\n\nHuman: Why does the ego car doing this?\nAssistant: to enter another road.\n\n",
+    "BDDX_Processed/video/025f49ef-0f6366f3_01688.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.83, 9.33, 8.82, 8.32, 7.89, 7.49, 7.08]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [-2.21, -2.08, -1.95, -1.82, -1.7, -1.48, -1.26]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows and veers slightly left\n\nHuman: Why does the ego car doing this?\nAssistant: as there is a speed bump in its lane.\n\n",
+    "BDDX_Processed/video/13796ade-b4bcd515_13499.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.87, 8.45, 8.98, 9.37, 9.49, 9.62, 9.52]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.51, 1.26, 1.03, 0.74, 0.41, 0.05, -0.16]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car resumes driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the way is clear.\n\n",
+    "BDDX_Processed/video/09a24438-2716030b_08280.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.23, 17.29, 17.59, 17.97, 18.06, 17.77, 17.36]\n Curvature: [-0.01, 0.04, 0.01, 0.01, -0.0, 0.0, -0.01]\n Acceleration: [1.24, 0.58, 0.29, 0.22, -0.1, -0.3, -0.59]\n Course: [0.0, -0.5, 0.11, 0.1, -0.27, -0.21, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the road immediately ahead is clear.\n\n",
+    "BDDX_Processed/video/20ed775f-4d9f4da5_20280.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.47, 11.25, 12.33, 13.43, 15.69, 17.01, 15.96]\n Curvature: [-0.0, 0.0, 0.0, -0.01, -0.02, -0.0, 0.01]\n Acceleration: [1.84, 1.22, -0.1, 0.91, 1.01, -0.08, -0.57]\n Course: [0.0, -0.29, -0.24, 3.28, 1.29, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car completes a left turn and accelerates down a road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is completely clear.\n\n",
+    "BDDX_Processed/video/0913cf42-fec3e0c0_08023.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.15, 5.8, 9.15, 9.76, 7.38, 8.82, 9.52]\n Curvature: [0.0, 0.0, -0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.69, 0.7, 0.67, -0.11, -0.87, 1.28, -1.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving steadily down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead is minimal.\n\n",
+    "BDDX_Processed/video/026501c2-16c41a5d_05136.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.72, 7.16, 7.63, 7.79, 8.16, 8.66, 9.93]\n Curvature: [-0.0, -0.02, 0.01, 0.0, -0.01, -0.01, 0.0]\n Acceleration: [0.51, 0.46, 0.29, 0.25, 0.43, 0.86, 1.24]\n Course: [0.0, 0.36, 0.16, 0.19, 0.09, 0.05, 0.32]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic is moving.\n\n",
+    "BDDX_Processed/video/18e7592e-9fdef3fb_16420.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.34, 12.02, 9.97, 7.25, 6.53, 5.24, 1.71]\n Curvature: [-0.01, -0.01, -0.0, 0.01, 0.01, 0.02, 0.09]\n Acceleration: [-2.43, -2.95, -3.03, -2.56, -2.72, -2.96, -3.12]\n Course: [0.0, 0.14, -0.17, -0.14, -0.12, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes as it approaches an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light ahead turns red.\n\n",
+    "BDDX_Processed/video/16aaf25d-dd1e0dfc_15385.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.86, 11.43, 11.87, 12.03, 12.25, 12.75, 12.51]\n Curvature: [-0.01, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.57, 0.49, 0.29, 0.18, 0.36, 0.14, -0.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.02, 0.01, 0.32]\nWhat is the action of ego car?\nAssistant: The car proceeds down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is clear of traffic.\n\n",
+    "BDDX_Processed/video/04488604-c87bc645_02764.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.41, 18.15, 17.89, 17.63, 17.37, 17.16, 16.94]\n Curvature: [-0.04, -0.04, -0.04, -0.05, -0.05, -0.03, -0.0]\n Acceleration: [-0.72, -0.77, -0.83, -0.88, -0.92, -0.85, -0.78]\n Course: [0.0, 0.02, 0.02, 0.02, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic in front of it is moving freely.\n\n",
+    "BDDX_Processed/video/010b0600-ae1f7be5_02633.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.61, 6.79, 6.95, 7.01, 6.9, 6.78, 7.21]\n Curvature: [0.01, 0.0, -0.0, -0.02, -0.06, -0.09, -0.06]\n Acceleration: [0.8, 0.51, 0.24, 0.15, 0.32, 0.5, 0.4]\n Course: [0.0, 0.56, 0.56, 0.84, 0.56, 0.84, 0.49]\nWhat is the action of ego car?\nAssistant: The car veers left\n\nHuman: Why does the ego car doing this?\nAssistant: to move around a stopped vehicle.\n\n",
+    "BDDX_Processed/video/10739276-cbf67694_11835.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.07, 4.09, 4.63, 4.98, 5.5, 6.43, 6.75]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.49, 0.76, 0.43, 0.43, 0.72, 0.59, 0.39]\n Course: [0.0, 0.0, 0.0, 0.03, 0.04, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car maintains a slow speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is heavy.\n\n",
+    "BDDX_Processed/video/0edbc246-3bbf3cd5_11237.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.18, 9.8, 8.37, 7.33, 6.61, 4.57, 2.71]\n Curvature: [0.01, 0.02, 0.01, 0.0, 0.01, 0.05, -0.01]\n Acceleration: [-1.5, -1.74, -1.5, -1.35, -1.74, -2.35, -1.63]\n Course: [0.0, -0.28, -0.35, -0.18, -0.14, -0.06, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has slowed down.\n\n",
+    "BDDX_Processed/video/007eddfc-528c4da4_04489.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [23.85, 23.02, 22.43, 22.61, 22.63, 22.19, 21.71]\n Curvature: [0.0, 0.02, -0.0, 0.0, -0.01, 0.03, -0.02]\n Acceleration: [-0.71, -0.65, -0.17, 0.12, -0.21, -0.39, -0.28]\n Course: [0.0, -0.49, 0.0, 0.18, -0.19, -0.19, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then merges left\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/04076a65-052f6027_01992.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.64, 2.77, 1.9, 1.16, 0.65, 0.09, 0.0]\n Curvature: [-0.1, -0.06, -0.02, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.58, -2.28, -1.99, -1.63, -1.22, -0.77, -0.47]\n Course: [0.0, 0.03, 0.03, 0.0, 0.0, 0.0, 0.03]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: after the light turned red.\n\n",
+    "BDDX_Processed/video/17b88142-15616867_15835.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.85, 5.7, 5.54, 5.39, 5.23, 5.08, 4.92]\n Curvature: [-0.07, -0.06, -0.05, -0.04, -0.03, -0.02, -0.02]\n Acceleration: [-1.26, -1.25, -1.24, -1.24, -1.23, -1.22, -1.21]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a red light ahead\n\n",
+    "BDDX_Processed/video/015f23ad-5049df48_03350.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.16, 12.63, 13.06, 13.39, 13.68, 13.81, 13.98]\n Curvature: [0.01, -0.0, -0.01, -0.01, -0.01, 0.02, 0.03]\n Acceleration: [0.49, 0.63, 0.74, 0.58, 0.44, 0.46, 0.47]\n Course: [0.0, -0.09, -0.13, -0.23, -0.1, 0.05, -0.02]\nWhat is the action of ego car?\nAssistant: The car accelerates to a constant speed and merges left\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear.\n\n",
+    "BDDX_Processed/video/106c8097-3639f424_11801.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.45, 6.32, 6.4, 6.87, 7.29, 7.3, 7.25]\n Curvature: [-0.0, 0.0, 0.0, 0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.86, 0.06, -0.01, 0.48, 0.07, -0.09, -0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the road at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic is clear.\n\n",
+    "BDDX_Processed/video/1e1ff90e-51151c15_18939.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.33, 11.64, 13.03, 14.72, 15.09, 16.88, 17.61]\n Curvature: [-0.0, 0.01, -0.01, -0.03, -0.04, -0.02, 0.0]\n Acceleration: [-2.52, 0.27, 0.69, 0.38, 0.41, 0.54, 0.39]\n Course: [0.0, -0.67, 0.0, 0.0, 4.04, 2.68, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates with the speed of traffic\n\nHuman: Why does the ego car doing this?\nAssistant: because the obstruction has cleared and the car in front is accelerating.\n\n",
+    "BDDX_Processed/video/0816a76e-156bcd83_07435.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 1.5, 2.89, 4.16, 4.6, 5.19, 6.21]\n Curvature: [0.0, -0.03, -0.04, -0.02, -0.0, 0.01, 0.01]\n Acceleration: [1.16, 1.8, 1.89, 1.31, 1.14, 1.11, 1.27]\n Course: [0.0, 0.11, 0.11, 0.11, 0.19, 0.06, 0.06]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is now green\n\n",
+    "BDDX_Processed/video/011ad6b2-1dfff443_04662.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.62, 10.28, 10.29, 10.38, 10.75, 11.42, 11.69]\n Curvature: [-0.01, 0.01, -0.01, -0.0, 0.0, -0.0, -0.01]\n Acceleration: [0.03, -0.17, 0.09, 0.13, 0.39, 0.39, 0.03]\n Course: [0.0, 0.0, -0.39, -0.06, 0.0, 0.06, 0.39]\nWhat is the action of ego car?\nAssistant: The car maintains speed\n\nHuman: Why does the ego car doing this?\nAssistant: as it drives down the road\n\n",
+    "BDDX_Processed/video/1f7ba5ab-05aecae8_19624.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.15, 8.56, 7.14, 5.93, 4.5, 3.14, 2.15]\n Curvature: [-0.01, 0.03, 0.03, 0.04, 0.04, 0.02, -0.0]\n Acceleration: [-1.61, -1.64, -1.45, -1.48, -1.5, -1.29, -1.08]\n Course: [0.0, 0.34, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves into the far right lane as it slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is clear and the light ahead is red.\n\n",
+    "BDDX_Processed/video/08ec5fe9-c9944382_07964.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.89, 1.35, 1.77, 2.37, 3.25, 4.13, 4.6]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.04, 1.29, 1.53, 1.69, 1.66, 1.63, 1.48]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it is pulling out into traffic.\n\n",
+    "BDDX_Processed/video/02b7b9ee-4cd88e31_02590.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.91, 8.12, 8.32, 8.47, 8.55, 8.63, 8.33]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.53, 0.46, 0.4, 0.24, -0.02, -0.32, -0.52]\n Course: [0.0, -0.29, -0.29, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/11589021-eb71270b_12439.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.7, 16.4, 16.95, 17.49, 18.1, 18.47, 18.74]\n Curvature: [-0.0, 0.01, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.44, 0.3, 0.32, 0.32, 0.28, 0.18, 0.11]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.28, -0.11]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the path is clear.\n\n",
+    "BDDX_Processed/video/14306a74-50cdd915_13837.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.65, 3.57, 4.73, 7.95, 8.62, 9.27, 10.65]\n Curvature: [-5.61, 0.03, -0.08, 0.05, 0.03, 0.05, -0.01]\n Acceleration: [0.61, 1.48, 1.5, 0.53, 0.05, 1.19, -0.4]\n Course: [0.0, -0.35, 0.0, 0.26, -0.0, -0.14, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates and continues to drive\n\nHuman: Why does the ego car doing this?\nAssistant: as the light has turned green.\n\n",
+    "BDDX_Processed/video/1ac41139-230fb4e1_17360.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.9, 11.31, 7.87, 3.02, 0.62, 0.63, 0.93]\n Curvature: [0.03, -0.02, 0.01, -0.15, 0.47, 0.56, 0.03]\n Acceleration: [-0.16, -1.5, -2.86, -2.55, -0.57, 0.04, 0.68]\n Course: [0.0, 0.14, 0.5, 1.41, 0.3, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a red light.\n\n",
+    "BDDX_Processed/video/15e7be3e-1a9fc847_14735.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.44, 1.27, 2.03, 2.83, 3.52, 4.28, 4.69]\n Curvature: [-0.0, -0.0, -0.0, 0.07, 0.22, 0.38, 0.36]\n Acceleration: [1.25, 1.55, 1.81, 1.88, 1.66, 1.41, 1.23]\n Course: [0.0, 0.0, 0.0, -3.11, -2.08, -3.11, -5.92]\nWhat is the action of ego car?\nAssistant: The car makes a left turn\n\nHuman: Why does the ego car doing this?\nAssistant: as there is no vehicle in front of it.\n\n",
+    "BDDX_Processed/video/167e3ac6-54189bd3_15174.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.56, 3.53, 3.57, 3.67, 4.0, 4.29, 4.47]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.04, 0.02, 0.15, 0.34, 0.39, 0.45, 0.56]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car steers left\n\nHuman: Why does the ego car doing this?\nAssistant: to go around a van parked on the right side of the street.\n\n",
+    "BDDX_Processed/video/12cf075a-6743fba3_13076.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.51, 3.03, 3.5, 3.98, 4.38, 4.81, 5.13]\n Curvature: [0.73, 0.14, -0.4, -0.62, -0.35, -0.05, -0.0]\n Acceleration: [1.5, 1.37, 1.26, 1.14, 1.04, 0.93, 0.66]\n Course: [0.0, -0.77, -0.77, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's pulling into the far right lane.\n\n",
+    "BDDX_Processed/video/1c734c25-39536e33_18111.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.71, 3.72, 4.7, 3.28, 2.32, 2.76, 2.01]\n Curvature: [-0.0, -0.0, 0.0, 0.01, 0.01, 0.0, -0.0]\n Acceleration: [-0.34, 0.79, -0.1, -0.76, -0.09, -0.03, -0.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car proceeds slowly down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead is moving slowly.\n\n",
+    "BDDX_Processed/video/0738f42e-fe0d895e_07080.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.39, 10.79, 11.2, 11.6, 11.91, 11.96, 12.02]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.62, 1.42, 1.22, 1.01, 0.81, 0.41, 0.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges left and accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: since they cleared the car blocking traffic on the left.\n\n",
+    "BDDX_Processed/video/1189ae68-4742a8fa_12593.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.42, 2.98, 3.49, 3.98, 4.38, 4.78, 4.93]\n Curvature: [0.05, 0.03, 0.0, -0.01, -0.02, -0.02, -0.03]\n Acceleration: [0.62, 0.85, 1.07, 1.12, 0.91, 0.69, 0.5]\n Course: [0.0, -0.77, -0.52, -0.31, -0.31, -0.31, 0.38]\nWhat is the action of ego car?\nAssistant: The car is merging into the lane to its left\n\nHuman: Why does the ego car doing this?\nAssistant: so that it can move past the slower traffic in its lane while the lane to its left is clear of impeding traffic.\n\n",
+    "BDDX_Processed/video/17dd182d-1df6245c_15907.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.15, 0.01, 0.0, 0.0, 0.0, 0.08, 0.09]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.15, -0.08, -0.01, 0.0, 0.05, 0.04, -0.03]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is reversing slowing\n\nHuman: Why does the ego car doing this?\nAssistant: because they are backing into a parking space\n\n",
+    "BDDX_Processed/video/031637c4-d2a9e7f0_04153.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.03, 0.69, 0.45, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-3.24, -0.11, 0.58, 0.58, 0.59, 0.59, 0.59]\n Acceleration: [-0.64, -0.62, -0.34, -0.17, 0.0, 0.0, 0.0]\n Course: [0.0, 0.25, -0.22, -0.11, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/110409ba-9fde6618_12184.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.17, 3.39, 3.61, 3.82, 4.08, 4.44, 4.79]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.55, 0.69, 0.83, 0.98, 1.12, 1.15, 1.18]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car maneuvers back to the center of the road\n\nHuman: Why does the ego car doing this?\nAssistant: as it has moved around the truck.\n\n",
+    "BDDX_Processed/video/04e55a30-fc0b249b_03792.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.54, 7.32, 7.07, 6.9, 6.53, 6.36, 6.22]\n Curvature: [0.0, -0.01, 0.04, 0.04, 0.03, 0.0, 0.01]\n Acceleration: [-0.17, -0.23, -0.2, -0.27, -0.26, -0.15, -0.1]\n Course: [0.0, 0.74, -0.2, -1.39, -1.32, -0.78, -0.41]\nWhat is the action of ego car?\nAssistant: The car slows even further\n\nHuman: Why does the ego car doing this?\nAssistant: because there is oncoming traffic and parked cars.\n\n",
+    "BDDX_Processed/video/002b485a-d1301c7c_00569.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.25, 7.81, 9.76, 9.97, 10.79, 11.83, 12.94]\n Curvature: [0.0, 0.03, -0.03, 0.05, -0.04, -0.03, -0.01]\n Acceleration: [-0.02, 0.61, 0.47, 0.05, 0.38, 0.73, 0.22]\n Course: [0.0, -0.73, 0.2, 0.65, -1.08, -0.03, -0.41]\nWhat is the action of ego car?\nAssistant: The car maintains speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/1d288c57-7852537a_18395.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [32.01, 31.51, 30.51, 30.1, 29.58, 29.8, 30.03]\n Curvature: [0.0, -0.01, -0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.08, -0.18, -0.09, -0.11, -0.11, -0.01, 0.16]\n Course: [0.0, 0.0, -0.34, 0.0, 0.0, 0.0, -0.02]\nWhat is the action of ego car?\nAssistant: The car is driving down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane ahead is clear and traffic is moving normally.\n\n",
+    "BDDX_Processed/video/114e578a-0cb4e102_12378.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [30.51, 31.02, 31.65, 31.64, 30.24, 30.84, 31.44]\n Curvature: [-0.01, -0.01, 0.01, 0.02, 0.05, 0.01, 0.01]\n Acceleration: [0.43, 0.12, 0.28, -0.4, -0.48, 0.48, -0.05]\n Course: [0.0, 0.71, 0.66, -0.48, -1.36, -3.21, -0.15]\nWhat is the action of ego car?\nAssistant: The car proceeds down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: as the roadway is clear.\n\n",
+    "BDDX_Processed/video/0cdb6d7e-55d7c1d9_10107.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.71, 14.4, 13.26, 11.95, 10.7, 9.82, 9.04]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-2.42, -2.87, -3.26, -3.71, -4.16, -4.64, -5.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to stop\n\nHuman: Why does the ego car doing this?\nAssistant: as the traffic light ahead has turned red.\n\n",
+    "BDDX_Processed/video/02d478d1-84df3ff0_03039.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.07, 0.13, 0.2, 0.33, 0.9, 1.4]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.13, 0.41, 0.66, 0.95, 1.23, 1.49, 1.71]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates and moves forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the light has turned green.\n\n",
+    "BDDX_Processed/video/07919504-00f3c313_02697.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.47, 2.11, -1.0, -1.0, -0.21, 0.34, 0.28]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-5.13, -4.56, -2.76, 0.35, 0.65, 0.49, 0.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a car doing a u-turn ahead\n\n",
+    "BDDX_Processed/video/00e42886-bd5ed543_02459.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.79, 2.25, 2.66, 3.15, 3.15, 3.47]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.25, 0.89, 0.62, 0.3, 0.17, 0.04, 0.33]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: due to traffic in front of it.\n\n",
+    "BDDX_Processed/video/06cbfcda-21eeb0d3_06712.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.03, 6.63, 6.19, 5.78, 5.33, 4.92, 4.98]\n Curvature: [0.04, 0.03, 0.02, 0.01, -0.01, -0.03, -0.15]\n Acceleration: [-1.0, -1.03, -1.07, -0.99, -0.67, -0.38, -0.02]\n Course: [0.0, -0.07, -0.11, -0.17, -0.25, -0.17, 0.18]\nWhat is the action of ego car?\nAssistant: The car is slowing and pulling to the right\n\nHuman: Why does the ego car doing this?\nAssistant: to allow a truck room to pass on a narrow street with cars parked on both sides\n\n",
+    "BDDX_Processed/video/0710389f-2a7ff2be_06917.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.17, 0.1, 0.04, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.08, -0.08, -0.08, -0.07, -0.04, -0.01, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: the car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a car stopped in front of it\n\n",
+    "BDDX_Processed/video/03af3912-36636dd5_05656.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.25, 8.07, 9.21, 9.95, 10.54, 11.23, 11.42]\n Curvature: [0.0, 0.0, -0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.4, 1.44, 0.92, 0.64, 0.63, 0.41, 0.37]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues to move on forward\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is seen and the car can safely move onward.\n\n",
+    "BDDX_Processed/video/0714456d-619b3a70_06949.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.15, 0.17, 0.43, 1.05, 2.04, 2.8, 3.65]\n Curvature: [-0.09, -0.1, -0.08, -0.03, 0.04, 0.03, 0.0]\n Acceleration: [0.09, 0.22, 0.58, 0.96, 1.13, 1.09, 0.75]\n Course: [0.0, 0.0, 0.0, 0.07, 0.1, 0.14, -0.28]\nWhat is the action of ego car?\nAssistant: The car is slowly accelerating.\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic in front of it has cleared and it is preparing to make a right turn.\n\n",
+    "BDDX_Processed/video/11a61ba2-adf5fd04_12621.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.54, 6.71, 5.38, 3.99, 2.89, 3.01, 3.17]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, -0.01, 0.06]\n Acceleration: [-0.91, -1.38, -1.66, -1.45, -0.63, 0.06, 0.11]\n Course: [0.0, 0.0, 0.0, 0.0, -3.11, -5.18, -3.4]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: to make a left turn.\n\n",
+    "BDDX_Processed/video/0ab1b9e8-b8d0530c_08954.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is standing still\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked on the side.\n\n",
+    "BDDX_Processed/video/00d15d58-9197cde5_02253.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.35, 4.86, 4.58, 4.6, 4.87, 6.02, 7.4]\n Curvature: [-0.0, 0.0, 0.0, -0.12, 0.03, 0.09, 0.02]\n Acceleration: [-1.05, -0.5, -0.16, 0.15, 0.81, 1.33, 1.36]\n Course: [0.0, 0.0, -0.15, 0.0, 0.02, -0.49, -0.35]\nWhat is the action of ego car?\nAssistant: The car stays to the left\n\nHuman: Why does the ego car doing this?\nAssistant: to get onto a road's onramp.\n\n",
+    "BDDX_Processed/video/13ff2e36-2301a60e_13731.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.74, 5.27, 5.87, 6.4, 7.01, 7.55, 8.18]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.97, 2.04, 2.1, 2.16, 2.21, 2.12, 2.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car corrects course\n\nHuman: Why does the ego car doing this?\nAssistant: as it continues down the road\n\n",
+    "BDDX_Processed/video/1e1ff90e-24d540ec_18933.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.7, 6.85, 7.79, 8.98, 9.21, 9.87, 10.84]\n Curvature: [0.06, -0.0, -0.02, -0.05, 0.03, 0.01, -0.02]\n Acceleration: [1.01, 1.01, 1.05, 0.69, 0.43, 0.79, 0.69]\n Course: [0.0, -1.36, -0.12, 0.46, 0.29, 0.25, 0.14]\nWhat is the action of ego car?\nAssistant: The car heads down the road at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/1337f4dd-ab3b187e_13310.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked in a lot.\n\n",
+    "BDDX_Processed/video/04904d8d-0ab5c90a_03500.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.98, 9.28, 8.51, 7.6, 6.42, 5.14, 4.04]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.55, -1.92, -2.32, -2.61, -2.8, -3.0, -2.68]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: due to the stop sign.\n\n",
+    "BDDX_Processed/video/0f7d9c3c-62316600_11434.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.16, 9.78, 12.31, 13.08, 13.69, 13.91, 14.21]\n Curvature: [0.0, -0.01, -0.0, 0.0, 0.01, 0.0, 0.0]\n Acceleration: [1.15, 1.13, 0.43, 0.19, 0.16, 0.19, -0.06]\n Course: [0.0, 0.08, 0.5, 0.0, -0.35, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is heading down the street at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is empty.\n\n",
+    "BDDX_Processed/video/171bc81e-9f3fd385_15610.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.35, 13.19, 13.62, 13.51, 13.39, 13.34, 13.44]\n Curvature: [-0.18, -0.06, 0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.88, 1.05, 0.35, -0.13, -0.09, -0.03, -0.03]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.05, -0.06]\nWhat is the action of ego car?\nAssistant: The car pulls into the lane to the left\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane merges onto another roadway.\n\n",
+    "BDDX_Processed/video/039f729b-9f86828d_00929.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [23.42, 22.63, 22.55, 22.96, 23.64, 24.25, 24.68]\n Curvature: [0.0, -0.01, -0.02, -0.02, -0.0, 0.0, 0.0]\n Acceleration: [-0.4, -0.38, 0.15, 0.66, 0.82, 0.67, 0.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: That car is going down the highway at a steady pace.\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady pace.\n\n",
+    "BDDX_Processed/video/156f9308-59b3b098_14553.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.04, 5.61, 4.28, 3.08, 1.68, 0.58, 0.1]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.29, -2.13, -2.04, -2.04, -1.71, -1.23, -0.52]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light ahead is red.\n\n",
+    "BDDX_Processed/video/1a7189e1-3f029941_17093.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.97, 9.46, 10.46, 11.27, 10.48, 11.18, 11.37]\n Curvature: [-0.01, 0.01, -0.01, 0.01, -0.01, 0.01, 0.01]\n Acceleration: [-1.22, -0.34, 0.88, -0.51, 0.14, 0.45, -0.29]\n Course: [0.0, -1.03, 0.05, 0.62, 1.12, -0.52, -0.61]\nWhat is the action of ego car?\nAssistant: The car moves forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the flow of traffic allows it.\n\n",
+    "BDDX_Processed/video/07184fbb-1bf770ba_01199.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.83, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: for an unknown reason.\n\n",
+    "BDDX_Processed/video/20a75ec6-357a0595_20207.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.27, 1.98, 5.21, 8.7, 12.0, 13.56, 13.44]\n Curvature: [0.22, 0.19, -0.04, 0.01, 0.02, -0.01, -0.01]\n Acceleration: [0.11, 2.13, 2.86, 2.95, 2.08, 0.53, -0.35]\n Course: [0.0, 0.06, 0.0, -0.21, 0.0, -0.21, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues straight through the intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no other cars at the intersection.\n\n",
+    "BDDX_Processed/video/004071a4-049b7b85_04353.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.18, 2.57, 2.01, 1.47, 1.06, 0.61, 0.35]\n Curvature: [-0.06, -0.11, -0.16, -0.21, -0.25, -0.3, -0.31]\n Acceleration: [-1.55, -1.46, -1.38, -1.24, -1.06, -0.86, -0.61]\n Course: [0.0, 0.95, 0.95, 0.84, 0.56, 0.84, 0.28]\nWhat is the action of ego car?\nAssistant: The car is making a right hand turn\n\nHuman: Why does the ego car doing this?\nAssistant: to enter another road.\n\n",
+    "BDDX_Processed/video/0b37664c-65995ce8_09216.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.01, 7.84, 7.67, 7.51, 7.34, 7.04, 6.74]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.51, -0.61, -0.72, -0.82, -0.9, -0.97, -1.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because a vehicle is stopped in its lane ahead.\n\n",
+    "BDDX_Processed/video/151db5e5-a93f50de_14266.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.58, 11.72, 11.85, 11.89, 11.71, 11.53, 10.95]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.92, 0.55, 0.21, -0.19, -0.62, -1.06, -1.5]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is going down the street at a steady speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is empty.\n\n",
+    "BDDX_Processed/video/0710389f-9edbc057_06926.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.12, 4.01, 3.9, 3.79, 3.7, 3.53, 3.36]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.55, -0.54, -0.54, -0.53, -0.52, -0.6, -0.68]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red and pedestrians are crossing.\n\n",
+    "BDDX_Processed/video/0e460dd3-471aad9b_10906.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.27, 4.37, 4.55, 4.79, 4.9, 4.9, 4.72]\n Curvature: [-0.02, -0.01, -0.0, -0.0, -0.01, -0.01, -0.0]\n Acceleration: [-0.3, 0.07, 0.27, 0.27, 0.07, -0.07, -0.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.4, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives slowly\n\nHuman: Why does the ego car doing this?\nAssistant: as the vehicles in front of it move slowly.\n\n",
+    "BDDX_Processed/video/1fdf9217-47d61d91_19877.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.64, 16.81, 16.92, 14.19, 12.83, 12.12, 12.96]\n Curvature: [0.0, -0.0, 0.0, 0.04, 0.1, 0.08, 0.1]\n Acceleration: [0.02, 0.16, -0.58, -0.83, -0.39, -0.02, 0.47]\n Course: [0.0, -0.39, -0.28, -2.72, -7.8, -8.39, -8.28]\nWhat is the action of ego car?\nAssistant: The car is driving in the left lane of a highway at a decent pace\n\nHuman: Why does the ego car doing this?\nAssistant: because there is moderate traffic.\n\n",
+    "BDDX_Processed/video/09e4ac3a-34ef3203_08568.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.15, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.46, -0.46, -0.46, -0.46, -0.46, -0.46, -0.47]\n Acceleration: [-0.08, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as the traffic light is red.\n\n",
+    "BDDX_Processed/video/1d5df664-5ccf76ae_18518.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.3, 2.12, 4.32, 4.44, 4.24, 4.4, 5.73]\n Curvature: [-0.0, 0.0, 0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.51, 1.33, 0.67, -0.27, 0.06, 0.33, 0.81]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins to move\n\nHuman: Why does the ego car doing this?\nAssistant: as it keeps up with the other traffic\n\n",
+    "BDDX_Processed/video/14df900d-f97805a4_14118.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.02, 5.92, 4.95, 3.85, 2.88, 2.88, 2.88]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.08, -2.07, -2.07, -2.07, -2.01, -1.55, -1.15]\n Course: [0.0, 0.0, 0.0, 0.0, -0.01, -0.01, -0.01]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has stopped.\n\n",
+    "BDDX_Processed/video/0dffa115-b8259b30_10820.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.62, 0.14, 0.56, 0.82, 0.43, 0.05, 0.21]\n Curvature: [1.25, 1.25, 0.07, -0.9, -1.04, -1.04, -0.99]\n Acceleration: [-0.16, 0.1, 0.22, -0.01, -0.45, -0.12, 0.56]\n Course: [0.0, -0.18, -0.56, -1.62, -1.28, -1.48, -0.21]\nWhat is the action of ego car?\nAssistant: The car is reversing and turning\n\nHuman: Why does the ego car doing this?\nAssistant: to get out of its parking spot\n\n",
+    "BDDX_Processed/video/0d207cff-6d499379_10394.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.41, 4.07, 3.74, 3.4, 3.08, 2.82, 2.57]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]\n Acceleration: [-1.09, -1.1, -1.12, -1.13, -1.14, -1.06, -0.98]\n Course: [0.0, 0.05, 0.05, 0.05, 0.01, -0.02, -0.02]\nWhat is the action of ego car?\nAssistant: The car is slowing down.\n\nHuman: Why does the ego car doing this?\nAssistant: There is an upcoming stop sign.\n\n",
+    "BDDX_Processed/video/173cc5bd-2a29ac8d_15670.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.88, 8.76, 8.52, 8.24, 8.26, 8.23, 8.32]\n Curvature: [0.06, 0.03, 0.0, 0.01, 0.01, 0.0, 0.01]\n Acceleration: [-0.29, -0.22, -0.28, -0.13, -0.03, 0.06, 0.25]\n Course: [0.0, -0.9, -0.63, -0.57, -0.16, -0.16, -0.14]\nWhat is the action of ego car?\nAssistant: The car slows slightly\n\nHuman: Why does the ego car doing this?\nAssistant: as a car merges into the lane ahead.\n\n",
+    "BDDX_Processed/video/01c3e19a-fbacb266_00049.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.62, 2.41, 4.18, 5.37, 5.8]\n Curvature: [-0.17, -0.19, -23.03, -7.65, -0.02, -0.0, -0.01]\n Acceleration: [0.0, 0.36, 1.29, 1.92, 1.65, 0.89, 0.19]\n Course: [0.0, 0.0, -0.08, 0.08, 0.48, 0.16, 0.4]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/21a968d4-5bdf562c_20659.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.23, 0.61, 1.27, 1.87, 2.18, 2.19, 1.02]\n Curvature: [-2.33, -1.8, -0.74, -0.01, -0.02, 0.05, 0.07]\n Acceleration: [0.36, 0.66, 0.74, 0.57, 0.09, -0.75, -0.9]\n Course: [0.0, -0.88, -2.45, -2.18, -1.09, 0.0, 0.61]\nWhat is the action of ego car?\nAssistant: The car pulls out slowly to the left\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a speed bump.\n\n",
+    "BDDX_Processed/video/03d34d05-4a61174c_05709.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [22.71, 22.65, 22.57, 22.48, 22.42, 22.39, 22.41]\n Curvature: [-0.0, 0.01, 0.01, -0.0, -0.02, -0.01, 0.03]\n Acceleration: [0.04, -0.06, -0.12, -0.11, -0.06, -0.02, 0.01]\n Course: [0.0, -0.52, 0.0, 0.0, -0.09, -0.12, -0.12]\nWhat is the action of ego car?\nAssistant: The car accelerates a little\n\nHuman: Why does the ego car doing this?\nAssistant: since the road is clear in front of it\n\n",
+    "BDDX_Processed/video/2105466b-b7803167_20362.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.71, 7.95, 7.28, 6.53, 5.79, 5.27, 4.81]\n Curvature: [0.0, 0.0, -0.0, -0.01, -0.01, -0.01, -0.0]\n Acceleration: [-2.75, -2.65, -2.58, -2.48, -2.39, -2.27, -2.17]\n Course: [0.0, 0.0, 0.0, 0.0, 0.04, 0.07, 0.04]\nWhat is the action of ego car?\nAssistant: The car gradually slows\n\nHuman: Why does the ego car doing this?\nAssistant: as it comes up on an intersection\n\n",
+    "BDDX_Processed/video/02d478d1-84df3ff0_05324.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.62, 3.12, 5.77, 7.7, 11.45, 13.85]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.13, 1.36, 2.2, 2.01, 2.31, 2.84, 1.16]\n Course: [0.0, 0.0, 0.02, 0.0, -0.08, 0.0, -0.01]\nWhat is the action of ego car?\nAssistant: The car begins driving down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is coming and can safely move forward.\n\n",
+    "BDDX_Processed/video/11919349-7f4d1a31_12607.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.43, 3.88, 3.28, 2.74, 2.85, 2.85, 2.95]\n Curvature: [0.0, 0.07, 0.09, 0.12, 0.19, 0.11, 0.01]\n Acceleration: [0.56, -0.18, -0.48, -0.36, 0.08, 0.08, 0.9]\n Course: [0.0, -4.01, -3.23, 65.32, 31.78, -1.76, -0.34]\nWhat is the action of ego car?\nAssistant: The car is turning left\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear.\n\n",
+    "BDDX_Processed/video/03a08882-3c5b8923_05636.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.84, 4.06, 7.15, 8.13, 4.62, 7.29, 10.08]\n Curvature: [-0.05, -0.21, -0.02, -0.01, -0.03, 0.01, 0.01]\n Acceleration: [-0.73, 0.7, -0.11, 0.6, -1.24, 1.32, -0.11]\n Course: [0.0, 22.73, 0.87, -0.71, 0.57, 0.52, -0.48]\nWhat is the action of ego car?\nAssistant: The car turns right and continues moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is seen and the car can safely move onward.\n\n",
+    "BDDX_Processed/video/0e947ab4-9e87f729_11110.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.58, 16.59, 17.15, 18.64, 19.19, 19.08, 19.61]\n Curvature: [0.0, -0.0, -0.0, -0.01, -0.02, -0.01, -0.02]\n Acceleration: [-0.12, -0.34, 0.55, 0.6, -0.03, -0.05, 0.41]\n Course: [0.0, 0.19, 0.2, 0.8, 1.37, 1.39, 0.8]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no nearby cars in its lane.\n\n",
+    "BDDX_Processed/video/1d45c2c6-05cc4100_18476.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.57, 4.06, 4.51, 4.87, 5.05, 5.23, 5.23]\n Curvature: [0.0, 0.18, 0.35, 0.2, -0.41, -1.02, -0.73]\n Acceleration: [0.47, 0.62, 0.76, 0.74, 0.48, 0.23, 0.28]\n Course: [0.0, 0.0, 0.0, -4.48, -4.48, -4.48, 21.48]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning to the left.\n\n",
+    "BDDX_Processed/video/061b081d-b5e37863_02168.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.17]\n Curvature: [0.11, 0.11, 0.11, 0.11, 0.11, 0.15, 0.45]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.01, 0.09, -0.02]\n Course: [0.0, -11.48, 0.0, 0.0, 6.12, 4.59, -0.07]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped.\n\n",
+    "BDDX_Processed/video/208be040-eee68442_20145.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.53, 1.77, 2.42, 3.04, 3.46, 4.22, 5.31]\n Curvature: [-0.03, 0.02, 0.0, -0.01, 0.01, 0.01, 0.0]\n Acceleration: [-0.06, 0.43, 0.62, 0.5, 0.57, 0.88, 0.96]\n Course: [0.0, 0.0, -0.05, -0.16, -0.12, -0.2, -0.04]\nWhat is the action of ego car?\nAssistant: The car is accelerating faster\n\nHuman: Why does the ego car doing this?\nAssistant: because the line of cars ahead are moving faster.\n\n",
+    "BDDX_Processed/video/1127aff5-188de959_12288.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.88, 13.58, 13.15, 12.61, 11.91, 11.3, 10.8]\n Curvature: [-0.03, -0.04, -0.08, -0.17, -0.11, -0.06, -0.05]\n Acceleration: [-0.2, -0.5, -0.75, -0.95, -0.93, -0.83, -0.59]\n Course: [0.0, 0.38, 2.0, 1.6, 2.68, 1.32, 1.65]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's making a sharp right turn.\n\n",
+    "BDDX_Processed/video/181589c6-8d66eb54_16049.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.2, 3.09, 2.79, 2.92, 3.34, 3.56, 4.1]\n Curvature: [-0.02, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.05, -0.15, -0.09, 0.22, 0.23, 0.14, 0.61]\n Course: [0.0, 0.0, 0.0, 0.0, 0.67, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges two lanes left\n\nHuman: Why does the ego car doing this?\nAssistant: since there is an opening in the lane.\n\n",
+    "BDDX_Processed/video/018f06cd-834da3b3_04838.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [24.39, 24.64, 23.63, 23.19, 19.18, 18.54, 16.53]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.01, 0.01, 0.03]\n Acceleration: [0.13, -0.02, -0.2, -0.37, -0.27, -0.75, 0.2]\n Course: [0.0, 0.09, 0.0, -0.27, 0.23, 0.12, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward along the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/0982e452-81d39a4e_08211.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [25.18, 25.17, 25.17, 25.16, 25.15, 25.15, 25.14]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.01, 0.0, 0.0]\n Acceleration: [-0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.05]\n Course: [0.0, -0.02, -0.02, -0.02, -0.02, -0.02, -0.02]\nWhat is the action of ego car?\nAssistant: The car continues to accelerate\n\nHuman: Why does the ego car doing this?\nAssistant: As the car continues going straight with traffic clear ahead of it.\n\n",
+    "BDDX_Processed/video/064bad4e-ae51d829_06525.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.37, 4.2, 2.78, 1.61, 0.94, 1.55, 1.83]\n Curvature: [0.03, -0.03, -0.01, 0.0, -0.03, 0.04, 0.0]\n Acceleration: [-1.26, -1.6, -1.58, -1.06, -0.09, 0.44, -0.04]\n Course: [0.0, -0.06, -0.21, -0.06, -0.07, -0.06, 0.2]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: for the red light ahead.\n\n",
+    "BDDX_Processed/video/1cd33ceb-eaae3ba7_18242.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [20.68, 19.81, 19.08, 18.63, 18.72, 18.93, 18.93]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.75, -0.7, -0.51, -0.16, 0.16, 0.08, 0.0]\n Course: [0.0, 0.03, 0.03, 0.0, 0.1, 0.0, -0.09]\nWhat is the action of ego car?\nAssistant: The car drives at a safe speed\n\nHuman: Why does the ego car doing this?\nAssistant: while heading down the highway\n\n",
+    "BDDX_Processed/video/21fd3bf5-1b0b302d_20822.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.96, 13.28, 12.58, 11.77, 11.31, 10.8, 10.17]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.84, -1.03, -1.08, -0.96, -0.87, -0.86, -1.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car reduces its speed\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a curve in the road, which is snowy.\n\n",
+    "BDDX_Processed/video/08d0f0fe-180a9442_07917.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.33, 8.19, 8.04, 7.9, 7.77, 7.57, 7.37]\n Curvature: [-0.03, -0.02, -0.02, -0.02, -0.02, -0.01, -0.01]\n Acceleration: [-0.76, -0.73, -0.7, -0.68, -0.66, -0.69, -0.72]\n Course: [0.0, 0.0, 0.0, 0.0, 0.1, 0.2, 0.2]\nWhat is the action of ego car?\nAssistant: The car brakes slightly\n\nHuman: Why does the ego car doing this?\nAssistant: as the car ahead is slowing down to make a turn.\n\n",
+    "BDDX_Processed/video/167e3ac6-78017dc6_15181.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.05, 1.03, 0.93, 0.75, 0.56, 0.22, 0.0]\n Curvature: [0.76, -0.13, -0.47, -0.55, -0.72, -0.73, -0.55]\n Acceleration: [0.34, -0.03, -0.15, -0.2, -0.29, -0.29, -0.14]\n Course: [0.0, 0.0, -0.15, 0.0, -0.15, -0.08, -0.14]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead is stopped.\n\n",
+    "BDDX_Processed/video/1d6ad9a0-a8974645_18571.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.48, 13.86, 14.53, 14.27, 13.9, 13.67, 13.64]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [2.51, 1.48, 0.47, -0.4, -0.38, -0.35, -0.47]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues straight ahead\n\nHuman: Why does the ego car doing this?\nAssistant: while heading to the left side of the road\n\n",
+    "BDDX_Processed/video/06ff7d61-45086936_00565.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.42, 1.32, 7.01, 10.74, 13.71, 9.94, 7.12]\n Curvature: [-0.01, -0.04, 0.05, -0.02, -0.0, -0.01, 0.03]\n Acceleration: [-0.81, 0.83, 2.15, 1.44, -0.64, -1.21, -0.3]\n Course: [0.0, 0.0, -0.52, -1.27, -0.23, -0.07, 0.43]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as there is no traffic or stop lights.\n\n",
+    "BDDX_Processed/video/1a032081-f7090d75_16899.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.63, 4.64, 5.43, 6.04, 6.6, 7.09, 7.39]\n Curvature: [0.0, -0.01, -0.01, -0.01, -0.01, -0.01, -0.0]\n Acceleration: [1.36, 1.28, 1.15, 0.92, 0.77, 0.55, 0.22]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.16, 0.16]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as the road ahead is clear.\n\n",
+    "BDDX_Processed/video/023d0f3c-564e6d31_05063.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.42, 7.7, 8.95, 10.57, 12.6, 15.03, 14.48]\n Curvature: [-0.02, 0.01, 0.0, -0.04, -0.0, 0.0, -0.02]\n Acceleration: [0.01, 1.41, 0.4, 0.92, 1.28, 0.71, -2.77]\n Course: [0.0, 0.18, 1.15, -0.18, -0.17, -0.53, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign\n\n",
+    "BDDX_Processed/video/1e077cf7-022ade8c_18889.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.81, 8.84, 8.14, 8.08, 8.22, 8.38, 7.98]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.63, -0.88, -0.43, 0.02, 0.16, -0.18, -0.66]\n Course: [0.0, -0.11, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car reduces its speed\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches slower traffic ahead.\n\n",
+    "BDDX_Processed/video/0d9ca9fa-debaa238_10613.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.04, 1.48, 0.98, 0.42, 0.0, 0.0, 0.0]\n Curvature: [-1.31, -1.31, -1.31, -1.31, -1.31, -1.31, -1.31]\n Acceleration: [-1.68, -1.5, -1.34, -1.16, -0.98, -0.66, -0.35]\n Course: [0.0, 0.02, 0.02, 0.02, 0.01, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car slows to a stop.\n\nHuman: Why does the ego car doing this?\nAssistant: There is a red light.\n\n",
+    "BDDX_Processed/video/1a07eecb-5d4efd09_16920.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.46, 18.55, 18.64, 18.68, 18.61, 18.55, 18.6]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.05, -0.02, 0.02, 0.04, 0.03, 0.02, 0.02]\n Course: [0.0, 0.01, 0.01, -0.04, -0.04, -0.04, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is empty.\n\n",
+    "BDDX_Processed/video/14859690-5c35db0c_13985.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.82, 0.73, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.26, -1.08, -0.11, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, -0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there's a stop sign.\n\n",
+    "BDDX_Processed/video/019c8305-3a3be0d6_04854.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.02, 6.76, 8.95, 11.87, 14.62, 16.47, 17.61]\n Curvature: [0.14, -0.15, -0.1, -0.08, -0.01, -0.01, -0.01]\n Acceleration: [1.69, 0.86, 1.68, 1.73, 1.33, 0.94, 0.44]\n Course: [0.0, 6.55, 9.25, 4.42, 0.0, -0.83, 2.55]\nWhat is the action of ego car?\nAssistant: The car continues to move on forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has begun moving forward.\n\n",
+    "BDDX_Processed/video/1aa6dd36-aa7f0108_17215.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.89, 11.27, 12.66, 12.62, 11.42, 11.96, 13.47]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [1.54, 1.46, 0.38, -0.61, -0.2, 0.6, 0.81]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.31, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the road at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/02eb43d9-ff055cbb_03379.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.56, 7.75, 10.84, 12.4, 13.25, 13.77, 14.69]\n Curvature: [-0.03, -0.08, -0.02, -0.0, 0.0, 0.0, 0.01]\n Acceleration: [1.53, 1.52, 1.02, 0.55, 0.3, 0.28, 0.35]\n Course: [0.0, 8.64, 1.25, 0.0, 0.0, -0.93, -0.48]\nWhat is the action of ego car?\nAssistant: The car accelerates to a constant speed\n\nHuman: Why does the ego car doing this?\nAssistant: to make a right turn.\n\n",
+    "BDDX_Processed/video/1c564ec4-88bca967_18028.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.17, 1.64, 2.06, 2.53, 3.0, 3.41, 3.78]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.48, 1.53, 1.57, 1.62, 1.66, 1.63, 1.6]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as the light has changed to green.\n\n",
+    "BDDX_Processed/video/16a1ea80-a4ed7617_15325.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.35, 6.39, 6.43, 6.59, 6.89, 7.21, 7.82]\n Curvature: [0.0, 0.03, 0.05, 0.06, 0.03, 0.01, 0.0]\n Acceleration: [0.11, 0.25, 0.37, 0.59, 0.87, 1.19, 1.4]\n Course: [0.0, 32.41, 32.41, -0.61, -0.4, -0.61, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning\n\nHuman: Why does the ego car doing this?\nAssistant: because the path is clear.\n\n",
+    "BDDX_Processed/video/010b0600-ae1f7be5_04642.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.12, 0.16, 0.0, 0.71, 1.46, 3.59, 6.03]\n Curvature: [-0.23, -0.17, -0.13, 0.48, -0.02, -0.15, -0.04]\n Acceleration: [-1.77, -0.13, 0.1, 0.29, 0.5, 1.08, 1.37]\n Course: [0.0, 0.0, 0.21, -0.6, -1.46, 7.97, 8.02]\nWhat is the action of ego car?\nAssistant: The car comes to a stop then turns right\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/0acc0c71-fb703219_09075.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.37, 10.23, 10.08, 9.94, 9.78, 9.64, 9.51]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.55, -0.55, -0.55, -0.55, -0.54, -0.54, -0.54]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drifts slightly to the left and back to the right\n\nHuman: Why does the ego car doing this?\nAssistant: Because there is another large truck parked on the side of the street and partially blocking the lane so it has to go around the truck.\n\n",
+    "BDDX_Processed/video/1270de59-e930f526_12930.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked on the side of the road.\n\n",
+    "BDDX_Processed/video/01d25bbc-a9f903ba_00097.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.58, 0.49, 0.36, 0.18, 0.07, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.33, -0.25, -0.21, -0.22, -0.14, -0.06, -0.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped on the side of the road\n\nHuman: Why does the ego car doing this?\nAssistant: for an unknown reason.\n\n",
+    "BDDX_Processed/video/0124dfa6-30a430dc_02953.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.36, 1.03, 2.15, 3.68, 4.07, 4.53, 5.27]\n Curvature: [0.19, 0.15, 0.1, 0.05, 0.19, 0.19, -0.08]\n Acceleration: [0.43, 1.26, 1.64, 1.44, 1.06, 0.8, 0.8]\n Course: [0.0, -0.56, -0.35, -0.35, 0.0, -8.55, -8.55]\nWhat is the action of ego car?\nAssistant: The car maintains a steady speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/1bc13f56-6d6df64d_17779.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/0ea059ef-ffb43396_11148.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.38, 3.05, 4.72, 6.19, 7.35, 8.37, 9.33]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [2.01, 2.31, 2.34, 2.04, 1.74, 1.45, 1.09]\n Course: [0.0, -0.09, 0.0, 0.0, 0.0, -0.09, -0.12]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: after the light turned green.\n\n",
+    "BDDX_Processed/video/005c4fd3-cb4d6287_01123.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.18, 0.59, 1.22, 0.78, 0.46, 0.69, 0.33]\n Curvature: [4.98, 1.1, 0.01, -0.04, -0.02, -0.01, 0.0]\n Acceleration: [0.02, 0.45, 0.05, -0.32, -0.04, 0.1, -0.15]\n Course: [0.0, -0.32, -1.73, -0.72, -0.15, -0.15, 0.12]\nWhat is the action of ego car?\nAssistant: The car reverses\n\nHuman: Why does the ego car doing this?\nAssistant: for an unknown reason.\n\n",
+    "BDDX_Processed/video/05584a52-590d4e59_06267.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.61, 8.45, 9.37, 10.18, 11.01, 11.78, 11.79]\n Curvature: [23.63, 14.65, 4.86, -0.03, -0.02, -0.0, -0.0]\n Acceleration: [1.99, 2.05, 2.12, 1.94, 1.42, 0.94, 0.27]\n Course: [0.0, 0.86, 1.3, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is empty.\n\n",
+    "BDDX_Processed/video/198d8f5e-5fc86c73_16754.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [27.93, 28.1, 28.1, 28.09, 27.84, 27.28, 27.43]\n Curvature: [0.0, 0.0, 0.02, 0.0, -0.0, -0.0, 0.01]\n Acceleration: [0.1, 0.08, -0.03, -0.09, -0.21, -0.03, -0.11]\n Course: [0.0, 0.0, -0.13, 0.23, 0.0, -0.14, -0.08]\nWhat is the action of ego car?\nAssistant: The car is driving down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/1fd756ef-0ac5401e_19863.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/1f225b07-a5d1fa2a_19382.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.54, 3.88, 2.21, 1.71, 1.51, 1.45, 2.07]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.05, -0.22, -0.45]\n Acceleration: [-1.2, -1.64, -1.03, -0.35, -0.12, 0.24, 0.98]\n Course: [0.0, 0.0, 0.0, 0.0, 4.48, 4.33, 2.95]\nWhat is the action of ego car?\nAssistant: The car very slowly turns right\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green but there are pedestrians in the cross walk\n\n",
+    "BDDX_Processed/video/1c734c25-39536e33_18110.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.14, 8.11, 8.08, 7.83, 7.14, 6.51, 5.36]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.16, -0.22, -0.63, -1.08, -1.71, -2.28, -2.2]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes and steers slightly to the right\n\nHuman: Why does the ego car doing this?\nAssistant: because another vehicle abruptly merges into the lane in front of it.\n\n",
+    "BDDX_Processed/video/076399f2-e2bc0747_02408.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.06]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped.\n\n",
+    "BDDX_Processed/video/0798a3a8-5a64671d_02848.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.67, 3.22, 2.81, 2.49, 2.36, 2.22, 2.16]\n Curvature: [0.0, -0.01, -0.01, -0.02, -0.01, -0.01, -0.02]\n Acceleration: [-1.12, -0.96, -0.82, -0.65, -0.46, -0.26, -0.18]\n Course: [0.0, 0.07, 0.05, 0.07, 0.07, 0.07, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because a car in front is about to turn into his lane.\n\n",
+    "BDDX_Processed/video/09e3f46e-28427585_08554.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.1, 17.77, 18.73, 18.04, 16.31, 14.98, 15.13]\n Curvature: [0.0, 0.03, -0.08, -0.01, 0.0, 0.0, 0.0]\n Acceleration: [-0.01, 0.01, 0.08, -0.61, -0.68, -0.27, 0.15]\n Course: [0.0, 0.06, 3.08, 0.5, 0.0, -0.27, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating.\n\nHuman: Why does the ego car doing this?\nAssistant: Because traffic is moving normally ahead of it and around it.\n\n",
+    "BDDX_Processed/video/1236ceb8-7c99a47a_12820.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.03, 2.58, 4.1, 5.57, 6.93, 7.8, 8.56]\n Curvature: [0.06, 0.0, -0.01, -0.0, 0.01, 0.01, 0.0]\n Acceleration: [1.42, 1.85, 1.93, 1.76, 1.44, 1.1, 0.81]\n Course: [0.0, -0.07, -0.07, 0.14, 0.2, 0.27, 0.07]\nWhat is the action of ego car?\nAssistant: The car accelerates and drives down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turns green.\n\n",
+    "BDDX_Processed/video/029ad1d1-8c9b02b1_05220.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.75, 1.4, 2.06, 2.82, 3.72, 4.63, 5.26]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [1.22, 1.53, 1.84, 2.0, 1.96, 1.93, 1.7]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because the line of cars ahead just started to move.\n\n",
+    "BDDX_Processed/video/049f2340-26fc3c56_06077.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.24, 8.68, 8.9, 8.83, 8.7, 8.21, 7.04]\n Curvature: [0.0, -0.0, -0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.5, 0.74, 0.16, -0.13, -0.67, -1.05, -1.19]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then turns left\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is seen and the car can safely move onward.\n\n",
+    "BDDX_Processed/video/1e1d4587-f9a27aea_18928.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.56, 6.49, 6.42, 6.34, 6.26, 5.98, 5.7]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.18, -0.32, -0.45, -0.58, -0.68, -0.7, -0.72]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drifts over the yellow line\n\nHuman: Why does the ego car doing this?\nAssistant: because a car in the right lane is blocking the left lane.\n\n",
+    "BDDX_Processed/video/1b130dc7-b7cb32db_17563.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped in a line of traffic in the left turn lane\n\nHuman: Why does the ego car doing this?\nAssistant: because oncoming traffic is heavy obstructing the left turn.\n\n",
+    "BDDX_Processed/video/1380820f-7f92f23a_13572.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.22, 1.48, 2.74, 3.91, 5.0, 5.57, 6.07]\n Curvature: [0.15, 0.07, -0.0, 0.0, -0.0, -0.01, -0.01]\n Acceleration: [1.34, 1.86, 2.33, 1.98, 1.62, 1.13, 0.61]\n Course: [0.0, 0.05, 0.12, 0.36, 0.32, 0.27, 0.1]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead moves forward.\n\n",
+    "BDDX_Processed/video/0d606092-6cb1ae91_10523.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.89, 3.22, 2.63, 1.96, 1.32, 0.96, 0.64]\n Curvature: [0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03]\n Acceleration: [-2.43, -2.3, -2.19, -2.06, -1.88, -1.37, -0.92]\n Course: [0.0, 0.0, 0.0, 0.0, 0.01, 0.02, 0.01]\nWhat is the action of ego car?\nAssistant: The car comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there's a stop sign.\n\n",
+    "BDDX_Processed/video/0d5cc7fb-45e17cc3_10512.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.67, 1.59, 3.26, 3.85, 4.28, 4.63, 5.14]\n Curvature: [0.02, 0.02, -0.01, -0.0, -0.0, 0.01, -0.02]\n Acceleration: [0.38, 0.92, 0.79, 0.4, 0.19, 0.44, 0.21]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is gradually accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic begins to move forward.\n\n",
+    "BDDX_Processed/video/07e29820-efa9e7da_07332.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.51, 1.36, 1.23, 1.08, 0.93, 0.74, 0.58]\n Curvature: [0.05, 0.04, 0.04, 0.03, 0.02, -0.01, -0.04]\n Acceleration: [-0.56, -0.58, -0.6, -0.61, -0.62, -0.56, -0.5]\n Course: [0.0, -0.02, -0.01, -0.02, -0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is red.\n\n",
+    "BDDX_Processed/video/0fe430bf-f1c58f7a_11589.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.68, 2.93, 1.34, 0.13, 0.0, 0.0, 0.0]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.37, -2.35, -1.92, -1.05, -0.41, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car has stopped\n\nHuman: Why does the ego car doing this?\nAssistant: The car has arrived at the intersection and is waiting for cross traffic to clear the intersection\n\n",
+    "BDDX_Processed/video/1f864b3f-e658daa2_19735.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.03, 8.05, 6.71, 5.02, 3.65, 2.5, 1.69]\n Curvature: [0.0, -0.01, 0.0, -0.01, 0.01, 0.0, 0.02]\n Acceleration: [-1.09, -1.29, -1.63, -1.65, -1.41, -1.08, -0.66]\n Course: [0.0, -0.08, -0.07, 0.08, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is decreasing speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has slowed\n\n",
+    "BDDX_Processed/video/02ac138c-6bc72d56_05250.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.09, 1.15, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-4.6, -1.57, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because it has reached a red light at an intersection.\n\n",
+    "BDDX_Processed/video/1f864b3f-d5130597_19722.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02]\n Course: [0.0, -0.5, -0.37, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is red.\n\n",
+    "BDDX_Processed/video/08f22787-8b51e932_07981.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.98, 4.24, 4.5, 4.75, 4.98, 5.22, 5.45]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.79, 0.83, 0.87, 0.91, 0.94, 0.94, 0.93]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is steering to the left\n\nHuman: Why does the ego car doing this?\nAssistant: to maneuver around a vehicle making a right turn in front of it.\n\n",
+    "BDDX_Processed/video/029e1042-d985bee1_02264.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.68, 3.88, 3.14, 2.42, 1.73, 0.78, 0.0]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.5, -1.47, -1.45, -1.52, -1.57, -1.2, -0.81]\n Course: [0.0, 0.0, -0.0, -0.02, -0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car veers right quickly and stops.\n\nHuman: Why does the ego car doing this?\nAssistant: for an unknown reason.\n\n",
+    "BDDX_Processed/video/1d5df664-626f8150_18524.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.49, 0.24, 0.21, 0.73, 1.75, 2.45, 2.51]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.02, -0.13, 0.21, 0.74, 0.84, 0.37, 0.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls forward and moves left one lane\n\nHuman: Why does the ego car doing this?\nAssistant: since there is an opening in traffic.\n\n",
+    "BDDX_Processed/video/1d092676-6c170b83_18308.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.29, 0.37, 0.45, 0.87, 1.77, 2.67, 3.29]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.25, 0.64, 1.0, 1.35, 1.64, 1.94, 1.56]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/03afa5eb-b47a1969_05657.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.3, 4.33, 6.44, 8.25, 9.38, 8.62, 6.45]\n Curvature: [-0.0, -0.0, 0.0, -0.01, -0.0, 0.0, -0.0]\n Acceleration: [1.68, 2.23, 2.17, 1.64, 0.19, -1.59, -2.46]\n Course: [0.0, 0.0, 0.0, 0.0, 0.29, -0.16, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/1d3825e0-6aafe66d_18450.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.34, 7.37, 5.33, 3.19, 1.65, 0.82, 1.12]\n Curvature: [0.0, -0.03, -0.05, -0.05, -0.05, -0.05, -0.05]\n Acceleration: [-2.07, -2.8, -3.08, -2.84, -1.69, -0.48, 0.74]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a near stop\n\nHuman: Why does the ego car doing this?\nAssistant: as it waits for a car to its left to cross lanes of traffic in front of it so that the other vehicle may turn right.\n\n",
+    "BDDX_Processed/video/00f42f6b-b1ca1f6c_04622.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.68, 5.22, 3.88, 2.84, 2.2, 1.64, 1.08]\n Curvature: [-0.05, -0.04, -0.0, 0.07, -0.01, -0.04, 0.02]\n Acceleration: [-2.49, -2.17, -1.76, -1.31, -1.03, -0.76, -0.43]\n Course: [0.0, 0.09, -0.12, -0.09, 0.0, -0.05, -0.06]\nWhat is the action of ego car?\nAssistant: The car brakes\n\nHuman: Why does the ego car doing this?\nAssistant: to allow a bus to merge onto the road.\n\n",
+    "BDDX_Processed/video/1564ef78-f192b81b_14506.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.73, 1.32, 1.99, 2.32, 1.68, 1.17, 1.33]\n Curvature: [0.17, -0.01, -0.03, -0.01, 0.04, -0.06, 0.03]\n Acceleration: [0.58, 0.12, 0.54, -0.32, -0.23, -0.05, 0.02]\n Course: [0.0, -0.87, 0.0, 0.0, 0.0, -0.94, 0.0]\nWhat is the action of ego car?\nAssistant: The car edges forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead begins to move.\n\n",
+    "BDDX_Processed/video/11f16188-3c9f07af_12756.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [27.19, 27.31, 27.43, 27.55, 27.64, 27.64, 27.64]\n Curvature: [-0.01, -0.01, -0.0, 0.0, 0.01, 0.0, 0.0]\n Acceleration: [0.22, 0.22, 0.22, 0.22, 0.22, 0.16, 0.1]\n Course: [0.0, 0.07, 0.07, 0.07, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward at a normal speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic in front of it is moving freely.\n\n",
+    "BDDX_Processed/video/1125356f-56500f04_12261.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.71, 12.01, 10.81, 9.44, 9.11, 9.34, 10.38]\n Curvature: [0.02, 0.0, -0.0, 0.0, 0.02, 0.01, 0.0]\n Acceleration: [-0.94, -0.92, -1.26, -0.83, -0.04, 0.61, 0.83]\n Course: [0.0, -0.24, 0.0, 0.0, -0.76, -0.57, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has become congested up ahead.\n\n",
+    "BDDX_Processed/video/022bf1ed-b3675f44_01021.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.72, 9.95, 9.19, 8.42, 7.71, 7.37, 7.03]\n Curvature: [0.02, 0.02, 0.01, 0.01, 0.0, -0.02, -0.05]\n Acceleration: [-1.75, -1.85, -1.95, -2.05, -2.14, -2.1, -2.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is approaching an intersection with a red light.\n\n",
+    "BDDX_Processed/video/1af2e457-53e4b09f_17518.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is parked at the curb on the right side of the road\n\nHuman: Why does the ego car doing this?\nAssistant: because it is at its destination.\n\n",
+    "BDDX_Processed/video/00516b75-7ac91661_04392.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [21.91, 21.94, 21.98, 22.01, 22.03, 21.94, 21.85]\n Curvature: [-0.03, -0.02, -0.02, -0.01, -0.01, -0.01, -0.0]\n Acceleration: [0.06, 0.01, -0.02, -0.07, -0.11, -0.13, -0.14]\n Course: [0.0, 0.31, 0.15, 0.31, 0.2, 0.08, 0.04]\nWhat is the action of ego car?\nAssistant: the car is moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is empty in front of it\n\n",
+    "BDDX_Processed/video/0c639b9e-69d332a8_09833.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.24, 1.73, 2.44, 2.65, 2.61, 2.31, 2.55]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, -0.01, -0.13]\n Acceleration: [1.09, 1.18, 0.67, 0.1, -0.18, 0.01, 0.67]\n Course: [0.0, 0.0, 0.0, 0.0, 2.04, 6.11, 2.46]\nWhat is the action of ego car?\nAssistant: The car is slowly accelerating and turning right\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light has turned green and it is making a right turn but there is a pedestrian crossing the street that it has just turned onto and it is waiting for the pedestrian to clear.\n\n",
+    "BDDX_Processed/video/07398e6d-65825072_01975.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.28, 1.94, 1.64, 1.31, 0.98, 0.75, 0.56]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.16, -1.14, -1.11, -1.08, -1.02, -0.69, -0.4]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/10fa8146-6f286be2_12158.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.0, 9.11, 8.3, 7.2, 5.67, 4.14, 2.97]\n Curvature: [-0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.1, -1.86, -2.55, -3.11, -3.25, -3.4, -2.94]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as the cars in the lane ahead have stopped.\n\n",
+    "BDDX_Processed/video/10739276-602cd73b_11820.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.14, 11.56, 12.4, 12.31, 10.86, 12.65, 12.55]\n Curvature: [0.0, -0.0, -0.03, -0.01, -0.0, 0.0, -0.0]\n Acceleration: [0.03, 0.13, -0.02, -0.17, 0.36, 0.24, 0.15]\n Course: [0.0, 0.52, -3.94, 3.0, 0.0, -0.07, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is clear and traffic is moving.\n\n",
+    "BDDX_Processed/video/17ffc2bb-e46a68f9_16041.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.55, 2.54, 2.47, 2.37, 2.22, 1.93, 1.27]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.04, -0.05, -0.09, -0.14, -0.24, -0.53, -0.79]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may park on the right side of the road.\n\n",
+    "BDDX_Processed/video/00091078-84635cf2_04262.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red\n\n",
+    "BDDX_Processed/video/1f13b7b2-4c1b6a58_19344.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.2, 3.97, 2.64, 1.44, 0.47, 0.42, 0.33]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [-0.88, -1.49, -1.61, -1.31, -0.68, -0.17, -0.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stopped car ahead.\n\n",
+    "BDDX_Processed/video/0ac3cbf4-ce89456d_09046.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.23, 0.25, 0.25, 0.24, 0.25, 0.31, 0.43]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.13, 0.05, 0.0, 0.0, 0.06, 0.22, 0.52]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may reenter the road and merge with traffic.\n\n",
+    "BDDX_Processed/video/1f0af85d-386af660_19333.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.95, 9.26, 9.78, 10.75, 11.23, 11.9, 11.16]\n Curvature: [0.0, 0.01, 0.01, 0.01, 0.01, -0.0, 0.01]\n Acceleration: [-0.58, 0.04, 0.37, 0.37, 0.23, -0.02, -0.38]\n Course: [0.0, -0.61, -0.89, -0.63, -0.89, -0.19, -1.11]\nWhat is the action of ego car?\nAssistant: The car is heading down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/1e7af489-44f98c39_19114.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.74, 2.2, 1.72, 1.18, 0.71, 0.83, 0.92]\n Curvature: [-0.01, 0.0, 0.02, 0.03, 0.04, 0.03, 0.02]\n Acceleration: [-2.19, -1.82, -1.5, -1.13, -0.75, -0.28, 0.13]\n Course: [0.0, 0.0, 0.0, 0.0, -0.01, -0.02, -0.01]\nWhat is the action of ego car?\nAssistant: The car stops briefly\n\nHuman: Why does the ego car doing this?\nAssistant: because there's a stop sign.\n\n",
+    "BDDX_Processed/video/0b08fd9d-9a192a23_09150.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.66, 1.85, 2.81, 4.11, 5.55, 6.28]\n Curvature: [-0.0, -0.56, -0.04, -0.01, 0.01, -0.0, 0.04]\n Acceleration: [0.17, 0.52, 0.52, 0.52, 0.78, 0.66, 0.55]\n Course: [0.0, 0.68, 0.17, -0.17, 0.53, 0.0, -0.7]\nWhat is the action of ego car?\nAssistant: The car accelerates forward\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead begins moving.\n\n",
+    "BDDX_Processed/video/01ad8d8a-49bb0c93_04870.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.26, 0.16, 0.06, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, -0.05, -0.1, -0.11, -0.06, -0.01, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/1e3c65ba-f847b4df_19008.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.16, 16.28, 16.38, 16.49, 16.61, 16.83, 17.06]\n Curvature: [0.16, 0.1, 0.05, -0.01, -0.05, -0.03, -0.01]\n Acceleration: [0.86, 0.8, 0.74, 0.68, 0.64, 0.76, 0.88]\n Course: [0.0, -0.15, -0.15, -0.15, 0.09, 0.19, 0.19]\nWhat is the action of ego car?\nAssistant: The car pulls into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: to pass a car that stopped at the light.\n\n",
+    "BDDX_Processed/video/1cbc652a-1e2ab11e_18190.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.95, 8.46, 8.97, 9.37, 9.72, 9.69, 9.58]\n Curvature: [-0.02, -0.01, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.86, 0.87, 0.87, 0.6, 0.33, -0.19, -0.7]\n Course: [0.0, 0.05, 0.06, 0.09, 0.06, 0.05, -0.02]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/200407a9-43e2484d_19930.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.04, 2.61, 3.09, 3.9, 5.23, 6.68, 7.69]\n Curvature: [-0.13, -0.15, -0.17, -0.18, -0.1, -0.04, -0.01]\n Acceleration: [0.41, 0.56, 0.72, 1.19, 1.52, 1.34, 0.88]\n Course: [0.0, 2.62, 4.15, 5.72, 4.76, 1.56, 0.21]\nWhat is the action of ego car?\nAssistant: The car is now completing the right turn\n\nHuman: Why does the ego car doing this?\nAssistant: because the bus has cleared and it is now safe to completed the turn.\n\n",
+    "BDDX_Processed/video/1746ef96-34e24f75_15675.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.81, 10.54, 9.62, 4.81, 4.0, 6.36, 5.92]\n Curvature: [0.0, -0.0, -0.01, 0.01, -0.03, -0.0, -0.0]\n Acceleration: [2.42, 0.74, -1.45, -0.76, -0.03, 1.19, -0.18]\n Course: [0.0, 0.19, 0.34, 0.0, 2.43, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is heading down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/1c71863d-894a6b30_18098.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.82, 7.71, 8.16, 8.37, 8.34, 8.67, 8.7]\n Curvature: [-0.0, 0.0, 0.0, 0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.75, 0.57, 0.32, 0.04, 0.13, 0.18, -0.28]\n Course: [0.0, -0.03, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car proceeds down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead is moving normally.\n\n",
+    "BDDX_Processed/video/02e948bb-53fcbba0_03362.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.09, 1.02, 0.95, 1.05, 1.38, 1.71, 1.38]\n Curvature: [-0.13, -0.18, -0.22, -0.21, -0.1, 0.01, 0.15]\n Acceleration: [-0.38, -0.09, 0.17, 0.25, 0.03, -0.19, -0.42]\n Course: [0.0, 0.04, 0.02, 0.0, 0.0, 0.0, 0.02]\nWhat is the action of ego car?\nAssistant: The car is coming to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: since there is a pedestrian crossing the road\n\n",
+    "BDDX_Processed/video/05266828-a6ee9545_06222.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.86, 10.98, 10.21, 9.49, 8.96, 7.21, 5.63]\n Curvature: [0.0, -0.01, 0.01, 0.0, 0.0, 0.04, -0.02]\n Acceleration: [0.28, -0.25, -0.64, -0.26, -0.93, -1.25, -1.25]\n Course: [0.0, 0.25, 0.12, -0.18, -0.12, -0.19, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then merges left\n\nHuman: Why does the ego car doing this?\nAssistant: because a car is sitting in the road.\n\n",
+    "BDDX_Processed/video/16ee2f26-fae11a89_15511.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.95, 1.99, 2.03, 2.19, 2.51, 2.84, 2.92]\n Curvature: [-0.01, -0.0, 0.0, 0.0, -0.01, -0.01, -0.01]\n Acceleration: [0.1, 0.25, 0.38, 0.46, 0.47, 0.48, 0.37]\n Course: [0.0, -0.04, -0.02, -0.04, -0.04, -0.04, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has applied their brakes.\n\n",
+    "BDDX_Processed/video/0b3d47ed-6cb90089_09239.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped with its windshield wipers on\n\nHuman: Why does the ego car doing this?\nAssistant: as it snows and the car sits at the red light.\n\n",
+    "BDDX_Processed/video/0634e4d9-cbdf926d_02414.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.81, 11.11, 11.53, 13.04, 12.64, 11.61, 10.78]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.68, -0.19, 0.88, 0.4, -0.6, -0.35, -0.46]\n Course: [0.0, 0.0, 0.0, -0.13, 0.0, 0.0, 0.17]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/05f41c11-94e167fc_01864.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is stopped at an intersection with a red light.\n\n",
+    "BDDX_Processed/video/07afa351-aa604b11_07260.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.55, 1.26, 1.92, 2.69, 3.53, 4.16, 4.63]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.64, 0.83, 0.95, 1.0, 0.96, 0.72, 0.66]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as the traffic ahead begins to move after the light changed green.\n\n",
+    "BDDX_Processed/video/076399f2-ec0a8baa_07163.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.0, 1.58, 1.3, 0.47, 1.06, 0.3, 0.16]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.56, 0.02, -0.4, -0.05, 0.16, -0.46, -0.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: the car continues to inch slowly forward, stopping periodically\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is also moving slowly, and stopping every so often due to traffic.\n\n",
+    "BDDX_Processed/video/1c5c6a4f-65279a68_18039.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.09, 4.36, 5.31, 5.95, 6.34, 5.94, 5.17]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.61, 1.17, 1.05, 0.58, -0.01, -0.69, -1.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear\n\n",
+    "BDDX_Processed/video/15335947-9c5cd1ed_14332.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.02, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped behind a garbage truck on a narrow road\n\nHuman: Why does the ego car doing this?\nAssistant: because the garbage truck is stopped while workers fill it with trash.\n\n",
+    "BDDX_Processed/video/055e339a-1c6cb352_06318.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.08, 10.14, 11.63, 13.02, 13.72, 14.3, 14.72]\n Curvature: [-0.02, 0.0, -0.02, 0.01, -0.0, 0.03, 0.01]\n Acceleration: [2.35, 1.37, 0.59, 0.74, 0.2, 0.44, -0.35]\n Course: [0.0, 0.0, 0.0, -0.75, -0.16, -0.01, -0.16]\nWhat is the action of ego car?\nAssistant: The car continues to move forward\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is coming and can safely move forward.\n\n",
+    "BDDX_Processed/video/10425a11-febf988c_11785.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.38, -0.67, 0.0, 0.0, 0.01, 0.08, 0.16]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a police car and pedestrians.\n\n",
+    "BDDX_Processed/video/1bcf2906-50102fca_17804.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.34, 5.89, 5.47, 4.98, 4.43, 3.87, 3.29]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.96, -1.09, -1.2, -1.31, -1.41, -1.5, -1.56]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows and stops\n\nHuman: Why does the ego car doing this?\nAssistant: while waiting for a car to pass in the opposite direction\n\n",
+    "BDDX_Processed/video/1189ae68-1a8ad3c6_12588.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.63, 9.8, 9.81, 9.6, 9.21, 8.71, 8.09]\n Curvature: [-0.0, 0.0, 0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.65, 0.22, -0.15, -0.45, -0.67, -0.8, -0.78]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car veers to the left and changes lanes\n\nHuman: Why does the ego car doing this?\nAssistant: since new cars are starting to merge onto the highway.\n\n",
+    "BDDX_Processed/video/15e7be3e-ef0227a7_14769.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because there's a problem (hazard lights are flashing)\n\n",
+    "BDDX_Processed/video/1d81a9da-0b142a6b_18580.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [20.01, 20.08, 20.18, 20.3, 20.28, 20.31, 20.46]\n Curvature: [0.0, 0.0, 0.01, 0.01, 0.0, 0.0, 0.03]\n Acceleration: [-0.0, 0.1, 0.13, 0.08, 0.08, 0.09, 0.09]\n Course: [0.0, 0.06, -0.11, -0.11, -0.17, 0.17, 0.17]\nWhat is the action of ego car?\nAssistant: The car is merging all the way right\n\nHuman: Why does the ego car doing this?\nAssistant: to get to the exit lane\n\n",
+    "BDDX_Processed/video/171bc81e-9f3fd385_15607.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.57, 12.77, 12.81, 13.03, 13.75, 14.46, 13.28]\n Curvature: [0.0, -0.0, -0.01, -0.01, -0.0, -0.01, -0.04]\n Acceleration: [-0.69, 0.0, -0.01, 0.25, 0.32, -0.03, -0.3]\n Course: [0.0, 0.0, 0.0, 2.6, 0.0, 1.0, 0.16]\nWhat is the action of ego car?\nAssistant: The car travels quickly down a city street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving well.\n\n",
+    "BDDX_Processed/video/029ad1d1-8c9b02b1_05224.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.0, 5.49, 4.03, 3.24, 2.48, 1.92, 1.44]\n Curvature: [-0.15, -0.07, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-1.94, -2.09, -2.2, -1.72, -1.27, -0.93, -0.58]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car turns right quickly\n\nHuman: Why does the ego car doing this?\nAssistant: because they are cutting off another driver and don't want to get hit.\n\n",
+    "BDDX_Processed/video/036f3b5f-ee1521de_00373.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/07482c65-a96de461_02131.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.3, 10.71, 10.95, 12.51, 14.51, 16.12, 16.68]\n Curvature: [0.09, 0.13, 0.01, -0.0, -0.0, -0.0, -0.01]\n Acceleration: [1.47, 0.17, 0.66, 1.23, 1.38, 0.72, 0.39]\n Course: [0.0, -7.22, -2.31, 0.02, 0.0, 0.0, 0.97]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear of traffic.\n\n",
+    "BDDX_Processed/video/13296fd2-942df7f9_13278.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01]\nWhat is the action of ego car?\nAssistant: The car is stationary\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked in a parking lot.\n\n",
+    "BDDX_Processed/video/227c4a9a-4be0048f_21061.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.26, 0.26, 0.26, 0.25, 0.25, 0.25, 0.25]\n Acceleration: [-0.34, -0.03, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.07, -0.08, 0.0, 0.08, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stationary\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is red.\n\n",
+    "BDDX_Processed/video/04d49b8a-f0e342cb_06100.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.12, 8.64, 9.16, 9.68, 10.18, 10.57, 10.95]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.95, 1.9, 1.85, 1.8, 1.74, 1.64, 1.53]\n Course: [0.0, 0.03, 0.03, 0.03, 0.02, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges back into its original lane\n\nHuman: Why does the ego car doing this?\nAssistant: as it is past the stopped vehicle.\n\n",
+    "BDDX_Processed/video/1534d7b4-d694dd2a_14348.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.95, 4.03, 10.4, 11.83, 12.52, 12.13, 11.66]\n Curvature: [0.0, -0.05, 0.03, -0.02, 0.04, -0.04, -0.03]\n Acceleration: [1.92, 3.19, 1.29, 0.2, 0.28, -0.11, -0.44]\n Course: [0.0, 64.24, 0.0, 0.0, 0.08, 0.0, 1.69]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is clear and traffic is moving steadily\n\n",
+    "BDDX_Processed/video/0b2867ef-da37e1d5_09207.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.04, 8.81, 9.23, 9.17, 8.99, 8.8, 8.5]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [1.18, 0.46, 0.16, -0.15, -0.11, -0.22, -0.13]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.02]\nWhat is the action of ego car?\nAssistant: The car proceeds down the road at a steady speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is empty.\n\n",
+    "BDDX_Processed/video/07398e6d-7b0dc522_02009.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.21, 0.02, 0.14, 0.25, 1.01, 1.58, 2.05]\n Curvature: [-0.02, -0.02, -0.02, -0.02, -0.02, -0.01, -0.0]\n Acceleration: [-0.19, -0.04, 0.11, 0.5, 0.68, 0.62, 1.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.34, 0.0]\nWhat is the action of ego car?\nAssistant: The car is steering to the right and driving forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: to merge into the lane to its right with less traffic.\n\n",
+    "BDDX_Processed/video/018f06cd-834da3b3_04070.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.54, 18.73, 18.91, 19.09, 19.25, 19.41, 19.51]\n Curvature: [0.01, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.45, 0.45, 0.44, 0.42, 0.38, 0.34, 0.31]\n Course: [0.0, 0.0, 0.0, 0.04, 0.04, 0.04, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates up to normal speed\n\nHuman: Why does the ego car doing this?\nAssistant: as there is nothing in its lane to impede him.\n\n",
+    "BDDX_Processed/video/05ca0197-6c0b4628_06403.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.18, 3.63, 3.9, 4.16, 4.47, 4.36, 4.18]\n Curvature: [0.0, 0.11, 0.19, 0.21, 0.18, 0.24, 0.01]\n Acceleration: [0.59, 0.45, 0.38, 0.3, 0.13, -0.15, -0.33]\n Course: [0.0, -4.28, -5.41, -3.86, -5.16, -4.75, -6.12]\nWhat is the action of ego car?\nAssistant: The car is turning left\n\nHuman: Why does the ego car doing this?\nAssistant: because that is the direction to leave the parking lot and enter the road.\n\n",
+    "BDDX_Processed/video/0062e803-38c0a33a_04410.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.44, 1.0, 0.56, 0.26, 0.21, 0.16, 0.17]\n Curvature: [-0.03, -0.02, -0.01, -0.01, -0.02, -0.02, -0.03]\n Acceleration: [-1.15, -0.96, -0.77, -0.54, -0.3, -0.07, -0.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is still stopped in front of it\n\n",
+    "BDDX_Processed/video/10f71853-4da52f52_12152.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.41, 10.56, 9.32, 7.53, 5.49, 3.51, 1.87]\n Curvature: [-0.0, -0.0, 0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [-0.61, -0.89, -1.27, -1.69, -1.73, -1.57, -1.13]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is braking\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light ahead is red.\n\n",
+    "BDDX_Processed/video/076399f2-d397b12e_07156.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.13, 0.07, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.1, -0.09, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: the is standing still\n\nHuman: Why does the ego car doing this?\nAssistant: since it is parked\n\n",
+    "BDDX_Processed/video/09fe84f2-5f2228d2_08637.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.76, 7.53, 8.89, 9.94, 10.06, 9.7, 9.14]\n Curvature: [-0.0, -0.0, 0.01, -0.02, -0.0, -0.0, 0.01]\n Acceleration: [1.27, 0.9, 0.75, 0.25, -0.02, -0.31, -0.4]\n Course: [0.0, -0.42, -0.02, -0.01, -0.46, 0.14, -0.3]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no other cars on the road.\n\n",
+    "BDDX_Processed/video/029e1042-66968a89_02247.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.21, 8.05, 7.89, 7.73, 7.6, 7.66, 7.73]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.6, -0.49, -0.39, -0.28, -0.17, -0.06, 0.05]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: to make a right turn.\n\n",
+    "BDDX_Processed/video/108c616c-d1e8cd93_11850.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.01, 2.74, 1.61, 0.39, 0.0, 0.0, 0.0]\n Curvature: [-0.07, 0.82, 4.35, 12.23, 3.19, 3.1, 2.49]\n Acceleration: [0.77, -0.17, -0.98, -0.6, -0.12, 0.0, 0.0]\n Course: [0.0, 3.26, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car parks in a parking spot\n\nHuman: Why does the ego car doing this?\nAssistant: as there is an open spot.\n\n",
+    "BDDX_Processed/video/08a18849-e16aacd4_07798.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.24, 10.97, 11.75, 12.55, 12.77, 12.83, 13.03]\n Curvature: [0.01, -0.04, -0.01, -0.02, -0.0, -0.0, -0.02]\n Acceleration: [0.97, 0.63, 0.71, 0.43, 0.11, 0.11, 0.32]\n Course: [0.0, 0.62, 0.51, 0.21, 0.27, 0.0, -0.1]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: since the traffic is moving freely.\n\n",
+    "BDDX_Processed/video/0d9ca9fa-ee783c7c_10615.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.14, 10.94, 10.55, 10.35, 10.22, 9.38, 7.57]\n Curvature: [0.0, 0.0, 0.01, 0.0, -0.0, -0.07, -0.11]\n Acceleration: [-1.17, -0.76, -0.3, -0.15, -0.45, -1.3, -1.38]\n Course: [0.0, 0.0, -0.0, 0.03, 0.04, 0.0, 7.83]\nWhat is the action of ego car?\nAssistant: The car slows down a little bit\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning to the right.\n\n",
+    "BDDX_Processed/video/16504799-d9e25a40_15075.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.23, 9.17, 9.11, 9.04, 8.99, 9.03, 9.06]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.38, -0.3, -0.21, -0.13, -0.05, -0.0, 0.05]\n Course: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\nWhat is the action of ego car?\nAssistant: The car continues forward\n\nHuman: Why does the ego car doing this?\nAssistant: since the lane has cleared\n\n",
+    "BDDX_Processed/video/0f2b20fc-0adc2e33_11324.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.31, 0.94, 1.64, 2.44, 3.65, 4.76, 5.59]\n Curvature: [-0.03, -0.06, -0.08, -0.11, -0.13, -0.15, -0.07]\n Acceleration: [1.02, 1.5, 2.02, 2.34, 2.37, 2.39, 2.2]\n Course: [0.0, 0.16, 0.25, 0.63, 0.95, 0.63, 0.91]\nWhat is the action of ego car?\nAssistant: The car is making a right hand turn\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may enter another road.\n\n",
+    "BDDX_Processed/video/07380221-064f762b_01848.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.85, 5.89, 5.93, 5.97, 6.02, 6.09, 6.16]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.15, 0.17, 0.19, 0.2, 0.22, 0.24, 0.26]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is braking, signaled by its brake lights.\n\n",
+    "BDDX_Processed/video/1735f7ed-7c811b0a_15650.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.87, 10.63, 11.39, 12.2, 12.95, 13.38, 13.69]\n Curvature: [-0.03, -0.01, 0.0, 0.02, 0.03, 0.02, 0.01]\n Acceleration: [1.0, 1.27, 1.5, 1.34, 1.13, 0.63, 0.08]\n Course: [0.0, 0.27, 0.2, 0.0, -0.25, -0.5, -0.23]\nWhat is the action of ego car?\nAssistant: The car pulls into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: to pass the car in front of it.\n\n",
+    "BDDX_Processed/video/00e02d60-54df99d1_02383.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.32, 3.34, 3.27, 3.16, 3.06, 2.91, 2.04]\n Curvature: [0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.14, -0.07, -0.1, -0.14, -0.2, -0.67, -1.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: Traffic ahead of the car is stopped.\n\n",
+    "BDDX_Processed/video/0d22eba4-90037346_10414.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.4, 1.96, 3.16, 3.95, 4.27, 4.84, 4.84]\n Curvature: [-0.29, -0.05, -0.01, -0.01, 0.06, 0.03, -0.03]\n Acceleration: [1.21, 1.6, 1.28, 0.78, 0.53, 0.33, 0.12]\n Course: [0.0, 0.07, 0.14, 0.21, 0.0, -0.14, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in front of it accelerates after the light turned green.\n\n",
+    "BDDX_Processed/video/0719a2ec-45b781f4_07024.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.63, 4.9, 7.0, 9.37, 10.03, 10.92, 10.77]\n Curvature: [-0.0, -0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.77, 1.62, 2.19, 1.46, 0.76, 0.38, -0.56]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the car blocking the lane has finished parking.\n\n",
+    "BDDX_Processed/video/1462e08b-8ee18629_13930.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.74, 11.49, 12.4, 13.23, 13.96, 14.7, 15.28]\n Curvature: [0.0, -0.0, 0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [1.28, 1.16, 1.08, 1.02, 0.92, 0.84, 0.58]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is empty.\n\n",
+    "BDDX_Processed/video/1f44d8f2-df677a54_19455.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.13, 8.47, 9.45, 8.85, 6.41, 3.19, 1.38]\n Curvature: [0.0, -0.01, 0.01, 0.0, 0.01, -0.0, -0.04]\n Acceleration: [-0.12, 0.66, 0.21, -1.51, -2.77, -2.39, -1.48]\n Course: [0.0, 0.32, -0.05, -0.15, -0.12, -0.13, -0.02]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is slowing down.\n\n",
+    "BDDX_Processed/video/0d588ff8-22e9ba3a_10502.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.62, 6.97, 7.2, 8.55, 10.08, 11.4, 11.87]\n Curvature: [-0.01, 0.0, 0.01, 0.01, -0.0, -0.01, -0.0]\n Acceleration: [1.39, 0.75, 0.74, 1.4, 1.41, 0.89, -0.4]\n Course: [0.0, 0.04, -0.26, -0.05, 0.0, 0.06, 0.08]\nWhat is the action of ego car?\nAssistant: The car is picking up speed and driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: since the traffic ahead has now turned off the street\n\n",
+    "BDDX_Processed/video/22663f09-b6484ede_21022.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.97, 4.12, 3.32, 3.24, 3.26, 3.92, 4.66]\n Curvature: [0.0, -0.1, -0.21, -0.53, -0.77, -0.34, -0.0]\n Acceleration: [-2.76, -1.79, -0.85, -0.09, 0.64, 1.22, 1.72]\n Course: [0.0, 5.38, 5.59, 6.21, 3.11, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car makes a right turn\n\nHuman: Why does the ego car doing this?\nAssistant: because the turn is safe to make.\n\n",
+    "BDDX_Processed/video/0308ce2b-8fc462fd_03783.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.67, 3.32, 2.6, 1.67, 1.5, 1.89, 3.22]\n Curvature: [-0.06, -0.02, -0.02, -0.03, -0.02, -0.02, -0.02]\n Acceleration: [0.4, -0.23, -0.77, -0.71, 0.21, 1.08, 1.64]\n Course: [0.0, 1.26, 0.87, 0.0, 0.0, 0.0, 0.59]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because he's waiting on traffic.\n\n",
+    "BDDX_Processed/video/0d79cdd7-468fbd93_10547.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.62, 10.66, 10.68, 10.48, 10.1, 9.63, 7.51]\n Curvature: [-0.02, -0.01, -0.02, -0.05, -0.03, -0.01, 0.0]\n Acceleration: [-0.89, -0.45, -0.08, -0.28, -0.42, -1.26, -1.98]\n Course: [0.0, 0.34, 0.48, 1.46, 1.14, 0.2, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down and coming to a brief stop\n\nHuman: Why does the ego car doing this?\nAssistant: since there is a stop sign ahead\n\n",
+    "BDDX_Processed/video/0ce5101a-0280b606_10140.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [23.89, 23.57, 23.45, 23.59, 23.56, 23.48, 23.33]\n Curvature: [-0.0, -0.0, -0.0, 0.0, -0.0, 0.0, 0.02]\n Acceleration: [-0.09, -0.12, -0.08, 0.06, -0.06, -0.11, -0.02]\n Course: [0.0, -0.22, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car switches back one lane over from left to right to the lane it was previously in\n\nHuman: Why does the ego car doing this?\nAssistant: As it approaches another slower vehicle in the left lane up ahead and is looking to pass it by as well.\n\n",
+    "BDDX_Processed/video/04076a65-8c1a44ba_02137.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.69, 6.84, 6.06, 5.16, 4.25, 3.26, 2.35]\n Curvature: [0.03, 0.02, 0.01, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-1.69, -1.93, -2.15, -2.31, -2.33, -2.35, -2.32]\n Course: [0.0, -0.07, -0.07, -0.07, -0.05, -0.07, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/19906a63-3976be3a_16771.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.61, 12.18, 11.8, 11.37, 10.9, 10.15, 9.5]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-1.24, -1.5, -1.72, -1.98, -2.25, -2.6, -2.91]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because the bus in front of it is turning.\n\n",
+    "BDDX_Processed/video/20dc0dc1-7df7f3fb_20257.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.37, 8.77, 8.85, 8.51, 7.97, 7.34, 6.03]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.2, 0.19, -0.1, -0.25, -0.37, -0.5, -1.3]\n Course: [0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is following another vehicle down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic is moving.\n\n",
+    "BDDX_Processed/video/06924f46-7f03bcdc_03565.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.93, 10.19, 9.44, 8.7, 8.04, 7.27, 6.5]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-2.97, -2.95, -2.93, -2.91, -2.85, -2.53, -2.2]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: in order to turn in an intersection.\n\n",
+    "BDDX_Processed/video/20ed775f-b93a37e6_20298.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.12, 12.88, 13.0, 13.62, 14.08, 14.16, 13.33]\n Curvature: [-0.0, -0.0, -0.0, 0.07, -0.04, -0.03, 0.02]\n Acceleration: [-0.24, -0.06, 0.3, 0.51, 0.2, -0.31, -1.2]\n Course: [0.0, 0.0, 0.0, 0.0, -1.02, 0.53, 0.21]\nWhat is the action of ego car?\nAssistant: The car is heading down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear.\n\n",
+    "BDDX_Processed/video/0a8aa157-cbfd9075_08853.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.17, 1.19, 1.33, 0.59, 0.45, 0.46, 0.43]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.07, 0.11, -0.22, -0.43, 0.01, -0.03, 0.29]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: while it waits for cross traffic to pass through an intersection.\n\n",
+    "BDDX_Processed/video/0453391a-f15e9e30_02935.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.97, 15.94, 16.79, 18.42, 18.54, 18.74, 18.85]\n Curvature: [-0.0, 0.01, 0.0, -0.0, -0.05, 0.01, 0.0]\n Acceleration: [-0.4, -0.15, 0.66, 0.47, 0.08, -0.08, 0.22]\n Course: [0.0, -0.7, -0.38, -1.7, 1.48, -0.01, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: due to traffic flowing freely.\n\n",
+    "BDDX_Processed/video/1bf71c4f-8a713c58_17885.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.34, 1.24, 1.14, 1.06, 1.01, 0.97, 0.92]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [-0.13, -0.16, -0.18, -0.18, -0.15, -0.12, -0.19]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may park.\n\n",
+    "BDDX_Processed/video/05fa6494-8f436d04_02018.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.58, 17.5, 18.18, 18.21, 18.38, 18.42, 18.47]\n Curvature: [0.0, -0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.74, 0.59, 0.19, 0.06, 0.08, -0.02, 0.39]\n Course: [0.0, 0.0, 0.0, 0.0, -0.07, 0.02, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates to a constant speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/076399f2-3cc0e470_07126.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.46, 2.57, 2.67, 2.71, 2.67, 2.62, 2.6]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.75, 0.48, 0.23, 0.05, -0.01, -0.07, -0.16]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: because they have executed the turn and the road is clear.\n\n",
+    "BDDX_Processed/video/162995f1-691e9bb1_14980.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [24.27, 24.3, 24.53, 24.82, 24.56, 23.56, 22.46]\n Curvature: [-0.01, 0.0, -0.01, -0.0, -0.0, 0.01, -0.01]\n Acceleration: [-0.21, 0.11, 0.15, 0.08, -0.4, -0.65, -0.63]\n Course: [0.0, 0.29, 0.15, 0.0, 0.54, 0.16, 0.12]\nWhat is the action of ego car?\nAssistant: The car continues straight\n\nHuman: Why does the ego car doing this?\nAssistant: while still staying on the road\n\n",
+    "BDDX_Processed/video/06d065cc-bbf101e6_04157.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.17, 7.95, 8.77, 9.43, 10.07, 10.47, 10.8]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.04, 0.08]\n Acceleration: [1.6, 1.52, 1.43, 1.21, 1.0, 0.73, 0.55]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.94]\nWhat is the action of ego car?\nAssistant: The car is turning left\n\nHuman: Why does the ego car doing this?\nAssistant: The car is moving through an intersection.\n\n",
+    "BDDX_Processed/video/1d97c075-dac6540f_18651.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.16, 4.14, 4.11, 4.09, 4.07, 4.11, 4.15]\n Curvature: [-0.0, -0.18, -0.36, -0.55, -0.66, -0.48, -0.31]\n Acceleration: [-0.31, -0.22, -0.13, -0.04, 0.03, 0.06, 0.09]\n Course: [0.0, 2.53, 2.53, 2.53, 1.38, 2.76, 2.76]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning to the right.\n\n",
+    "BDDX_Processed/video/00e02d60-54df99d1_04602.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.97, 3.14, 3.31, 3.47, 3.62, 3.62, 3.62]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.84, 0.71, 0.57, 0.44, 0.32, 0.2, 0.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car creeps forward once more\n\nHuman: Why does the ego car doing this?\nAssistant: to progress further down the road as far as the other vehicles allow.\n\n",
+    "BDDX_Processed/video/191fbcc1-3dbb976d_16614.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.61, 6.17, 6.77, 7.34, 7.93, 8.39, 8.9]\n Curvature: [0.14, 0.11, 0.08, 0.04, 0.0, 0.0, 0.0]\n Acceleration: [1.2, 1.16, 1.13, 1.08, 1.03, 1.01, 0.96]\n Course: [0.0, -2.36, -2.5, -0.42, -0.26, 0.04, 0.01]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's making a left turn.\n\n",
+    "BDDX_Processed/video/1c5ec9cb-ac0cc8ac_18042.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.75, 9.17, 10.33, 11.04, 10.89, 10.44, 9.41]\n Curvature: [-0.0, -0.0, -0.01, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.35, 1.23, 0.91, 0.27, -0.29, -0.71, -1.44]\n Course: [0.0, 0.0, 0.15, 0.2, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green and there is no nearby traffic in front of it.\n\n",
+    "BDDX_Processed/video/13796ade-ec1a9a69_13514.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.5, 9.86, 9.7, 9.06, 8.21, 8.1, 8.84]\n Curvature: [0.0, 0.0, -0.0, 0.0, 0.0, 0.01, 0.01]\n Acceleration: [1.34, 0.3, -0.47, -0.81, -0.57, 0.36, 1.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.35]\nWhat is the action of ego car?\nAssistant: The car pulls back into the middle lane\n\nHuman: Why does the ego car doing this?\nAssistant: because that lane is clear.\n\n",
+    "BDDX_Processed/video/0b88820f-3a647f1d_09424.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.46, 3.82, 3.91, 4.13, 4.64, 4.97, 5.92]\n Curvature: [-0.0, -0.0, -0.0, -0.18, -0.53, -0.18, -0.04]\n Acceleration: [0.55, 0.3, 0.29, 0.41, 0.59, 0.87, 1.0]\n Course: [0.0, 0.0, 0.0, 7.69, 7.14, 9.89, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning right\n\nHuman: Why does the ego car doing this?\nAssistant: to enter another road.\n\n",
+    "BDDX_Processed/video/0e05e1d7-51f68c07_10835.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.11, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.2, -0.04, -0.01, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic in front of it is stopped.\n\n",
+    "BDDX_Processed/video/1aa6dd36-83b93284_17201.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.53, 8.91, 8.33, 7.72, 7.17, 6.57, 6.0]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.53, -0.93, -1.3, -1.52, -1.49, -1.46, -1.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes\n\nHuman: Why does the ego car doing this?\nAssistant: because a vehicle begins to merge into the lane in front of it.\n\n",
+    "BDDX_Processed/video/06a93a2c-64127de3_06664.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.07, 4.08, 4.92, 3.2, 3.19, 4.63, 5.31]\n Curvature: [0.0, -0.08, -0.03, 0.04, 0.01, 0.02, 0.04]\n Acceleration: [0.04, 0.14, -0.84, 0.17, 0.17, 0.08, 0.28]\n Course: [0.0, 2.13, 2.64, 0.88, 0.22, 0.0, -0.35]\nWhat is the action of ego car?\nAssistant: The car moves forward along the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving slowly\n\n",
+    "BDDX_Processed/video/04056725-91d0bd08_05794.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car rolls slowly forward\n\nHuman: Why does the ego car doing this?\nAssistant: as it is waiting for the light to turn green.\n\n",
+    "BDDX_Processed/video/07afa351-aa604b11_03047.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0dffa115-ae68677e_10818.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.44, 1.05, 1.59, 2.2, 2.74, 3.31, 3.88]\n Curvature: [0.15, 0.09, 0.04, -0.02, -0.07, -0.05, -0.03]\n Acceleration: [1.37, 1.59, 1.78, 1.99, 2.18, 1.94, 1.7]\n Course: [0.0, 0.02, 0.02, 0.02, 0.01, 0.07, 0.07]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in front of it moves after the light turned green.\n\n",
+    "BDDX_Processed/video/1bbd0b9b-d81304c5_17778.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.35, 1.58, 2.79, 4.54, 5.66, 5.66, 6.01]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.01, -0.01, 0.0]\n Acceleration: [0.38, 0.74, 0.86, 0.83, 0.3, -0.03, 0.19]\n Course: [0.0, 0.0, 0.0, 0.57, 0.42, 0.52, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates and drives down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turns green.\n\n",
+    "BDDX_Processed/video/0378d8dc-62d8e85a_05570.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.61, 5.58, 5.54, 5.77, 6.35, 6.94, 7.34]\n Curvature: [-0.05, -0.02, 0.01, -0.19, -0.71, -1.23, -0.83]\n Acceleration: [-0.32, 0.08, 0.48, 0.8, 0.99, 1.19, 1.14]\n Course: [0.0, 4.37, 4.37, 0.44, 0.44, 0.44, 0.0]\nWhat is the action of ego car?\nAssistant: The car makes a right turn\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear\n\n",
+    "BDDX_Processed/video/029556a0-0ec9fa6f_02165.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.64, 5.27, 6.54, 8.55, 7.14, 8.63, 9.19]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [-0.21, -0.89, 0.19, 0.38, -1.39, 0.86, 0.35]\n Course: [0.0, 0.0, 0.0, -0.33, 0.0, 0.0, -0.14]\nWhat is the action of ego car?\nAssistant: The car maintains a slow steady speed\n\nHuman: Why does the ego car doing this?\nAssistant: because it is a school zone and there are speed bumps.\n\n",
+    "BDDX_Processed/video/115d9e12-3d8bfc10_12462.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.81, 9.41, 8.17, 8.01, 7.9, 8.05, 8.32]\n Curvature: [0.0, 0.19, 0.34, 0.11, -0.08, -0.1, -0.11]\n Acceleration: [-2.63, -2.01, -1.42, -0.64, 0.04, 0.42, 0.74]\n Course: [0.0, -43.82, -32.57, 1.18, 0.88, 1.17, 1.69]\nWhat is the action of ego car?\nAssistant: The car exits right off a main road\n\nHuman: Why does the ego car doing this?\nAssistant: as the path is clear.\n\n",
+    "BDDX_Processed/video/21825b6e-7981d5c8_20614.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is parked next to the sidewalk\n\nHuman: Why does the ego car doing this?\nAssistant: because it is okay to park there.\n\n",
+    "BDDX_Processed/video/1a4f9512-07be9b07_17042.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.1, 4.38, 3.0, 2.54, 1.25, 0.77, 0.43]\n Curvature: [-0.01, -0.0, 0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.71, -1.62, -0.66, -0.69, -0.7, -0.35, -0.12]\n Course: [0.0, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because a car in front of it is turning.\n\n",
+    "BDDX_Processed/video/0b4c8138-a1953ae5_09272.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.12, 9.63, 10.59, 11.69, 12.12, 12.56, 13.28]\n Curvature: [-0.0, 0.0, -0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [1.26, 0.83, 0.78, 0.57, 0.24, 0.51, -0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear.\n\n",
+    "BDDX_Processed/video/0ac3cbf4-2bbc1544_09033.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.91, 6.03, 6.14, 6.26, 6.38, 6.48, 6.57]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.14, 0.21, 0.28, 0.35, 0.43, 0.52, 0.61]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car swerves to the left and back\n\nHuman: Why does the ego car doing this?\nAssistant: as it attempts to merge into the other lane.\n\n",
+    "BDDX_Processed/video/1accee17-60f12c98_17401.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.17, 1.12, 2.72, 4.68, 6.19, 7.39]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.01, -0.01]\n Acceleration: [0.0, 0.42, 0.98, 1.44, 1.4, 1.03, 0.91]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.66, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is speeding up.\n\n",
+    "BDDX_Processed/video/1746ef96-cc2be6e9_15682.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.9, 1.1, 1.24, 0.81, 0.45, 0.35, 0.24]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.37, 0.06, -0.23, -0.38, -0.48, -0.34, -0.22]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward and slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as the light ahead is red.\n\n",
+    "BDDX_Processed/video/1cc77c32-f18a71c4_18233.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.44, 1.33, 1.85, 1.97, 1.45]\n Curvature: [-0.54, -0.54, -0.51, -0.06, -0.17, -0.31, -0.61]\n Acceleration: [0.0, 0.24, 0.71, 0.75, 0.38, -0.24, -0.77]\n Course: [0.0, 0.0, 0.0, 0.0, 0.14, 0.16, 0.16]\nWhat is the action of ego car?\nAssistant: The car pulls forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped for a red light.\n\n",
+    "BDDX_Processed/video/135a9651-48e55e5c_13387.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.5, 8.01, 6.61, 4.43, 2.41, 1.44, 0.64]\n Curvature: [-0.02, -0.0, 0.01, 0.01, -0.0, -0.0, -0.0]\n Acceleration: [-2.02, -2.78, -3.49, -3.2, -2.84, -1.94, -1.18]\n Course: [0.0, 0.09, 0.07, 0.05, 0.02, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light ahead turns red.\n\n",
+    "BDDX_Processed/video/0718ab33-bbd7e0ae_07005.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.12, 12.44, 12.58, 12.47, 12.84, 13.49, 14.84]\n Curvature: [0.0, -0.0, 0.01, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [1.32, 0.36, -0.05, 0.32, -0.0, 0.56, 0.77]\n Course: [0.0, -0.27, 1.0, 0.21, -0.32, 0.0, 0.2]\nWhat is the action of ego car?\nAssistant: The car moves slowly along the road\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/17ebfeeb-52459a76_15929.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.89, 2.61, 3.32, 3.83, 4.05, 4.26, 4.76]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.78, 1.56, 1.33, 1.17, 1.09, 1.0, 0.54]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving forward.\n\n",
+    "BDDX_Processed/video/1698c1ee-5ac17e4e_15280.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.78, 15.3, 16.01, 15.69, 15.56, 15.74, 15.51]\n Curvature: [0.0, -0.0, 0.0, -0.01, 0.0, -0.0, -0.01]\n Acceleration: [-0.26, 0.02, 0.18, -0.12, 0.0, 0.02, -0.14]\n Course: [0.0, 0.7, -0.2, 0.0, -0.11, -0.19, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street ahead is clear.\n\n",
+    "BDDX_Processed/video/0b01ca80-852faffa_09134.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [27.4, 27.05, 26.74, 26.39, 26.02, 25.5, 25.06]\n Curvature: [-0.01, 0.01, 0.02, 0.04, 0.05, 0.03, 0.01]\n Acceleration: [-1.01, -1.17, -1.32, -1.48, -1.61, -1.53, -1.45]\n Course: [0.0, 0.05, 0.02, 0.05, 0.02, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges onto the exit ramp to its right\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may enter another road.\n\n",
+    "BDDX_Processed/video/1b276558-040abe79_17591.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.12, 13.75, 14.68, 15.85, 16.33, 16.86, 17.02]\n Curvature: [0.03, 0.01, -0.02, 0.02, -0.02, 0.02, 0.0]\n Acceleration: [0.89, 0.41, 0.76, 0.58, 0.24, 0.28, -0.13]\n Course: [0.0, -0.54, 0.04, -0.14, 0.0, 0.42, -0.24]\nWhat is the action of ego car?\nAssistant: The car accelerates down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear.\n\n",
+    "BDDX_Processed/video/1e6a6965-a1405cba_19087.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.48, 0.1, 0.14, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, 0.01, -0.07, -0.07, -0.02, -0.0, -0.0]\n Acceleration: [-0.86, -0.12, -0.04, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.02, -0.01, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has stopped.\n\n",
+    "BDDX_Processed/video/0fe430bf-f1c58f7a_11583.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.86, 3.26, 1.66, 1.82, 2.09, 3.1, 4.2]\n Curvature: [-0.0, -0.0, -0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.12, -1.78, -1.36, -0.06, 1.23, 1.69, 2.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating and moving through the intersection\n\nHuman: Why does the ego car doing this?\nAssistant: The car has failed to stop and is performing a rolling stop through the intersection\n\n",
+    "BDDX_Processed/video/022ff54c-3cb58ec1_05025.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.4, 8.31, 8.24, 8.15, 8.03, 7.67, 7.35]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.2, -0.37, -0.52, -0.69, -0.87, -1.1, -1.3]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front of it is slowing to turn right.\n\n",
+    "BDDX_Processed/video/2090b4bf-cadb8062_20166.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.63, 4.83, 5.93, 6.66, 7.38, 8.03, 8.63]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [2.25, 2.03, 1.82, 1.56, 1.33, 1.25, 1.18]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.03]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: because it the light is green.\n\n",
+    "BDDX_Processed/video/1a33995e-45d650ea_17002.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.75, 5.02, 5.19, 5.46, 5.65, 6.03, 6.39]\n Curvature: [-0.03, 0.01, -0.01, -0.0, 0.02, -0.02, -0.03]\n Acceleration: [-0.15, 0.21, 0.21, 0.22, 0.28, 0.35, 0.43]\n Course: [0.0, 0.0, 0.0, -0.02, -0.03, -0.08, -0.03]\nWhat is the action of ego car?\nAssistant: The car gradually accelerates and continues down the road\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead moves forward.\n\n",
+    "BDDX_Processed/video/1e7225d5-64710d9a_19103.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.84, 9.46, 8.17, 6.85, 5.74, 5.43, 5.28]\n Curvature: [-0.0, -0.0, 0.02, 0.34, 0.58, 0.45, 0.3]\n Acceleration: [-1.87, -2.22, -2.5, -1.96, -1.42, -0.6, 0.11]\n Course: [0.0, 0.0, -1.28, -5.12, 17.97, 38.49, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates fast and makes a left turn through a red light\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no oncoming cars.\n\n",
+    "BDDX_Processed/video/21e621dd-b02f46d5_20709.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.22, 9.17, 9.65, 9.98, 10.04, 9.82, 9.36]\n Curvature: [0.02, -0.0, -0.01, 0.0, 0.01, -0.01, -0.01]\n Acceleration: [1.22, 0.81, 0.47, 0.22, -0.08, -0.39, -0.72]\n Course: [0.0, -0.07, 0.24, -0.16, -0.14, 0.16, 0.08]\nWhat is the action of ego car?\nAssistant: The car accelerates down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the van is gone.\n\n",
+    "BDDX_Processed/video/12ac7475-69905215_13029.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.06, 1.78, 1.5, 1.22, 0.98, 0.76, 0.54]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.05, -1.02, -1.0, -0.97, -0.94, -0.82, -0.71]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowly moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the path is clear.\n\n",
+    "BDDX_Processed/video/0070bc56-8d8cfd82_01443.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.51, 2.35, 1.42, 0.96, 0.59, 0.2, 0.08]\n Curvature: [-0.12, -0.03, -0.11, 0.04, 0.22, 0.3, 0.47]\n Acceleration: [-1.13, -1.14, -0.76, -0.48, -0.41, -0.26, -0.01]\n Course: [0.0, 0.07, 0.0, 0.0, 0.0, 0.0, 0.08]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front of it is stopping.\n\n",
+    "BDDX_Processed/video/1e82c7e7-d2744df9_19157.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.44, 9.25, 8.1, 7.07, 6.29, 5.85, 5.49]\n Curvature: [0.0, 0.01, 0.02, -0.02, -0.0, 0.01, 0.0]\n Acceleration: [-0.96, -1.26, -1.21, -1.01, -0.68, -0.45, -0.26]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the road again\n\nHuman: Why does the ego car doing this?\nAssistant: because the road goes forward freely.\n\n",
+    "BDDX_Processed/video/06ff7d61-dc1c9107_00695.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.04, 4.88, 6.66, 8.51, 9.99, 10.54, 10.16]\n Curvature: [-0.07, 0.04, 0.02, 0.01, 0.02, 0.02, 0.02]\n Acceleration: [1.21, 1.93, 2.01, 1.83, 1.12, 0.12, -0.7]\n Course: [0.0, 0.16, -0.07, -0.56, -0.82, -0.9, -0.42]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: as the light turned green.\n\n",
+    "BDDX_Processed/video/073dc97d-7a3e1436_02090.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.78, 8.15, 6.64, 7.15, 7.23, 7.16, 8.93]\n Curvature: [0.0, 0.0, -0.01, -0.03, 0.03, 0.0, 0.01]\n Acceleration: [0.03, -0.92, -0.24, 0.05, 0.04, 0.45, 1.23]\n Course: [0.0, -0.3, 0.4, 0.27, -0.42, -0.96, -0.27]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/0cf6f074-33fb55d1_10222.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.14, 0.64, 1.52, 2.39, 3.16, 3.82]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.02, 0.52, 0.97, 1.33, 1.25, 1.08, 0.79]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: since the light has turned green\n\n",
+    "BDDX_Processed/video/198d8f5e-2b4d439d_16753.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.71, 6.38, 6.0, 5.58, 5.2, 4.8, 4.62]\n Curvature: [-0.05, 0.01, 0.0, 0.0, 0.0, -0.02, 0.0]\n Acceleration: [-0.76, -0.43, -0.44, -0.44, -0.43, -0.3, -0.07]\n Course: [0.0, 0.32, 0.21, 0.24, 0.08, 0.08, 0.0]\nWhat is the action of ego car?\nAssistant: The car proceeds down the road somewhat slowly\n\nHuman: Why does the ego car doing this?\nAssistant: as the car enters a school speed zone.\n\n",
+    "BDDX_Processed/video/05584a52-590d4e59_00381.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.99, 7.89, 7.78, 7.66, 7.49, 7.33, 7.17]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.26, -0.29, -0.33, -0.35, -0.38, -0.4, -0.51]\n Course: [0.0, 0.0, 0.0, 0.05, 0.07, 0.05, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/1fb82715-555f2ad2_19814.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.92, 10.43, 8.12, 7.18, 6.37, 5.67, 4.28]\n Curvature: [-0.0, 0.06, 0.07, -0.01, -0.04, 0.03, 0.1]\n Acceleration: [1.2, -0.92, -1.19, -0.62, -0.64, -0.69, -1.43]\n Course: [0.0, 0.5, -0.11, 0.0, 0.0, 0.23, 0.23]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the roadway is unobstructed.\n\n",
+    "BDDX_Processed/video/0aa458f0-e93896a2_08869.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.35, 12.9, 11.67, 12.39, 12.89, 13.09, 12.05]\n Curvature: [0.0, 0.01, -0.01, -0.01, 0.03, 0.03, 0.02]\n Acceleration: [-1.29, -0.44, -0.15, 0.25, 0.14, -0.43, -0.16]\n Course: [0.0, 0.16, -0.07, -0.02, 0.71, -0.51, 0.69]\nWhat is the action of ego car?\nAssistant: The car drives down a street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving and lights are green.\n\n",
+    "BDDX_Processed/video/057f2887-a81b8339_01018.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.59, 6.19, 6.07, 6.58, 6.51, 6.29, 6.29]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.01, 0.0]\n Acceleration: [-0.19, -0.22, 0.16, 0.17, -0.11, -0.09, -0.06]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.49]\nWhat is the action of ego car?\nAssistant: The car is slowing down and proceeding with caution\n\nHuman: Why does the ego car doing this?\nAssistant: because there are brake lights ahead, signaling that traffic is slowing.\n\n",
+    "BDDX_Processed/video/17adc5ac-8bd58412_15824.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.21, 11.03, 10.84, 10.66, 10.48, 10.29, 10.11]\n Curvature: [0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.0]\n Acceleration: [-1.11, -1.15, -1.19, -1.23, -1.27, -1.31, -1.35]\n Course: [0.0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is slowing down.\n\n",
+    "BDDX_Processed/video/00268999-a4b8e39d_00435.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is red and cross-traffic is heavy.\n\n",
+    "BDDX_Processed/video/021c4a16-1c50e818_00820.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.4, 7.86, 7.37, 6.84, 6.37, 5.86, 5.19]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.3, -1.31, -1.31, -1.34, -1.42, -1.5, -1.62]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a rolling stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/2091adbb-a941aa9e_20180.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.9, 8.11, 8.76, 8.23, 8.26, 8.7, 9.46]\n Curvature: [-0.0, 0.01, -0.0, -0.01, -0.01, 0.01, -0.0]\n Acceleration: [-0.4, 0.09, 0.06, -0.22, 0.22, 0.25, -0.14]\n Course: [0.0, -0.27, -0.52, 0.75, 0.0, -0.52, 1.0]\nWhat is the action of ego car?\nAssistant: The car is steadily moving forward on the one lane\n\nHuman: Why does the ego car doing this?\nAssistant: because there was a sequence of green lights.\n\n",
+    "BDDX_Processed/video/2105466b-b7803167_20357.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.94, 4.22, 3.58, 2.86, 2.29, 2.04, 1.79]\n Curvature: [0.0, 0.0, 0.0, 0.01, 0.01, 0.04, 0.08]\n Acceleration: [-2.62, -2.39, -2.18, -1.95, -1.73, -1.38, -1.04]\n Course: [0.0, 0.0, 0.0, 0.0, -0.04, -0.07, -0.07]\nWhat is the action of ego car?\nAssistant: The car crawls forward\n\nHuman: Why does the ego car doing this?\nAssistant: while waiting to turn\n\n",
+    "BDDX_Processed/video/1659f092-84218f24_15089.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.56, 0.34, 0.14, 0.04, 0.17, 0.3, 0.22]\n Curvature: [-0.07, -0.07, -0.07, -0.06, -0.03, -0.0, -0.0]\n Acceleration: [-1.19, -0.78, -0.4, -0.1, -0.06, -0.01, -0.05]\n Course: [0.0, 0.03, 0.02, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as the area is clear.\n\n",
+    "BDDX_Processed/video/1aa41265-a89ad4f3_17165.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.26, 8.21, 8.79, 9.21, 9.28, 9.46, 9.9]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.87, 0.83, 0.56, 0.26, 0.17, 0.33, 0.48]\n Course: [0.0, 0.0, 0.0, 0.23, 0.0, -0.01, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is clear.\n\n",
+    "BDDX_Processed/video/227829d9-6a5c7405_21052.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.95, 7.96, 5.33, 4.23, 2.38, 0.99, 0.57]\n Curvature: [0.0, 0.0, 0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [-1.55, -2.26, -1.79, -1.44, -1.59, -0.88, -0.23]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: upon reaching a stop sign.\n\n",
+    "BDDX_Processed/video/0d405525-3d6d563b_10488.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.27, 2.01, 2.69, 3.34, 3.88, 4.42, 4.76]\n Curvature: [0.0, -0.31, -0.59, -0.66, -0.42, -0.17, -0.1]\n Acceleration: [1.32, 1.43, 1.53, 1.52, 1.33, 1.15, 1.07]\n Course: [0.0, 3.43, 2.29, 5.59, 5.59, 5.59, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as it is completing the turn\n\n",
+    "BDDX_Processed/video/09ae119b-ba122e1a_08442.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.92, 4.32, 3.71, 3.49, 3.17, 2.38, 1.56]\n Curvature: [0.02, 0.01, -0.01, -0.0, 0.0, -0.02, -0.04]\n Acceleration: [-1.27, -1.05, -0.83, -0.92, -1.04, -1.29, -1.49]\n Course: [0.0, -0.04, -0.04, 0.0, 0.02, 0.04, 0.01]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red\n\n",
+    "BDDX_Processed/video/00825605-9dcec7d0_01694.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [21.16, 20.85, 20.09, 20.63, 20.78, 20.01, 18.99]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.01]\n Acceleration: [-0.3, -0.15, -0.11, 0.51, -0.23, -0.22, 0.08]\n Course: [0.0, 0.48, 0.0, -0.04, 0.85, -0.38, 0.2]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: to maintain speed limit on the highway\n\n",
+    "BDDX_Processed/video/077192f8-4d59e803_07174.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.0, 6.84, 6.68, 6.7, 6.96, 7.22, 6.91]\n Curvature: [-0.11, -0.23, -0.34, -0.34, -0.18, -0.01, 0.09]\n Acceleration: [-0.32, -0.15, 0.03, 0.07, -0.06, -0.19, -0.84]\n Course: [0.0, 5.17, 5.17, -1.53, -1.53, -1.53, 1.26]\nWhat is the action of ego car?\nAssistant: The car turns right\n\nHuman: Why does the ego car doing this?\nAssistant: so that it can drive on another road.\n\n",
+    "BDDX_Processed/video/19f52469-c5f61fe6_16871.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.18, 1.05, 2.41, 2.24, 2.0, 2.31, 3.97]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.26, 1.03, 0.4, -0.24, -0.03, 0.73, 1.22]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates and continues to drive\n\nHuman: Why does the ego car doing this?\nAssistant: as the light has turned green.\n\n",
+    "BDDX_Processed/video/1a5dc090-f105114a_17078.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.58, 0.82, 0.97, 1.35, 0.86, 0.45, 0.71]\n Curvature: [0.0, -0.0, 0.0, 0.0, -0.0, 0.0, -0.0]\n Acceleration: [-0.36, -0.23, 0.35, -0.09, -0.36, -0.05, 0.3]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves slowly forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving slowly forward.\n\n",
+    "BDDX_Processed/video/1af21ce1-97d4a34b_17499.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.37, 9.98, 9.59, 9.2, 8.84, 8.66, 8.48]\n Curvature: [0.05, 0.04, 0.04, 0.04, 0.03, -0.01, -0.06]\n Acceleration: [-1.42, -1.34, -1.25, -1.17, -1.12, -1.32, -1.51]\n Course: [0.0, -0.69, -0.69, -0.69, 0.19, 1.07, 1.07]\nWhat is the action of ego car?\nAssistant: The car steers to its left\n\nHuman: Why does the ego car doing this?\nAssistant: to merge into the lane to its left as its lane ends.\n\n",
+    "BDDX_Processed/video/1125356f-edb7f7c8_12269.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.92, 5.14, 5.58, 5.8, 5.95, 6.51, 8.15]\n Curvature: [-0.0, 0.0, -0.0, -0.29, -0.65, -0.23, -0.01]\n Acceleration: [-0.93, 0.29, 0.3, 0.13, 0.31, 0.98, 1.25]\n Course: [0.0, 0.0, 0.0, 12.0, 6.12, 9.14, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning right.\n\n",
+    "BDDX_Processed/video/1ecebfa3-eb93af1d_19255.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.54, 7.22, 6.92, 6.55, 6.14, 5.7, 5.39]\n Curvature: [0.0, 0.02, 0.05, 0.06, 0.06, 0.07, 0.07]\n Acceleration: [-0.81, -0.87, -0.92, -0.95, -0.93, -0.91, -0.8]\n Course: [0.0, 0.03, 0.03, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/21825b6e-c209dfa6_20625.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.95, 3.26, 3.57, 3.88, 4.16, 4.59, 5.02]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.31, 1.34, 1.36, 1.39, 1.42, 1.48, 1.55]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves around the other car\n\nHuman: Why does the ego car doing this?\nAssistant: as it makes its way down the street\n\n",
+    "BDDX_Processed/video/0ddb2323-6d74c64d_10759.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.34, 4.44, 3.62, 2.77, 2.06, 1.29, 0.84]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.0, -2.04, -2.07, -1.98, -1.75, -1.49, -1.13]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: for the red light.\n\n",
+    "BDDX_Processed/video/06c58952-170a4d8f_06687.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.51, 7.16, 5.5, 3.4, 2.67, 0.66, 2.14]\n Curvature: [0.0, -0.01, -0.0, -0.0, -0.02, 0.08, 0.52]\n Acceleration: [-0.01, -0.78, -0.8, -0.79, -0.72, -0.13, 0.49]\n Course: [0.0, 0.0, 0.0, 0.65, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then turns left\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/1f156136-534f9c01_19358.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.92, 4.54, 5.61, 5.51, 2.87, 0.85, 0.56]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.26, 0.37, 0.35, -0.78, -1.12, -0.73, 0.41]\n Course: [0.0, 0.0, -0.01, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving slowly ahead\n\nHuman: Why does the ego car doing this?\nAssistant: because cars are staggered around from being stopped at lights.\n\n",
+    "BDDX_Processed/video/101002a3-af1f7874_11698.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.08, 12.02, 11.97, 11.98, 12.08, 12.18, 12.54]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-0.05, -0.01, 0.03, 0.16, 0.4, 0.65, 0.84]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car goes through an intersection.\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/11ecaf4a-8586f43d_12744.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.63, 5.28, 4.98, 4.63, 4.26, 3.75, 3.3]\n Curvature: [-0.0, -0.01, -0.01, -0.01, -0.01, -0.01, 0.0]\n Acceleration: [-1.22, -1.32, -1.41, -1.52, -1.58, -1.37, -1.19]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing.\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a wooden arm that is down at the toll booth.\n\n",
+    "BDDX_Processed/video/0478d053-88a940c9_03262.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, -0.07, 0.0, 0.28, 0.0, -0.29, -0.23]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/05c6dd3d-0ecc1cb7_01451.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.45, 6.23, 5.97, 4.98, 3.98, 2.84, 1.73]\n Curvature: [0.0, -0.0, -0.01, 0.0, 0.0, -0.1, -0.19]\n Acceleration: [-0.81, -0.99, -1.19, -1.65, -2.06, -2.06, -1.94]\n Course: [0.0, 0.05, 0.03, 0.0, 0.0, 0.0, 0.03]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/06d501fd-a9ffc960_06752.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.86, 7.53, 6.73, 6.32, 5.28, 4.48, 3.39]\n Curvature: [0.0, -0.0, 0.01, 0.02, 0.01, 0.05, 0.06]\n Acceleration: [-0.21, -0.44, -0.5, -0.53, -0.76, -0.7, -0.86]\n Course: [0.0, 0.0, 0.0, -0.9, -0.42, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges right then moves to the turning lane\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is seen and the car can safely move onward.\n\n",
+    "BDDX_Processed/video/165a8caf-e1ce6f38_15106.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0]\n Curvature: [0.36, 0.36, 0.36, 0.4, 0.45, 0.45, 0.45]\n Acceleration: [0.0, 0.0, 0.0, 0.07, -0.09, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.15, 0.0]\nWhat is the action of ego car?\nAssistant: The car remains at a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: As it waits at the traffic light which remains red.\n\n",
+    "BDDX_Processed/video/09ac7416-6b722ba9_08327.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.31, 2.69, 3.06, 3.44, 3.8, 4.07, 4.33]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.03, 0.23, 0.43]\n Acceleration: [1.69, 1.58, 1.46, 1.35, 1.15, 0.43, -0.3]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins turning left\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/18de0e20-9c9d86bc_16404.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.02, 0.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: Car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: The car has stopped because of traffic ahead.\n\n",
+    "BDDX_Processed/video/1987c0d9-38de7ec0_16740.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.68, 13.83, 14.82, 17.99, 20.44, 20.95, 18.36]\n Curvature: [0.0, 0.01, 0.01, 0.0, 0.0, -0.0, -0.01]\n Acceleration: [1.64, 0.18, 1.2, 1.25, 0.83, -0.63, -1.12]\n Course: [0.0, 0.12, -0.2, -0.2, -0.2, 0.0, 0.28]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is clear.\n\n",
+    "BDDX_Processed/video/15f3d7e1-85cf2263_14828.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.94, 4.7, 4.5, 5.05, 5.67, 6.82, 7.95]\n Curvature: [-0.03, -0.02, -0.01, -0.04, -0.05, 0.01, 0.06]\n Acceleration: [-1.1, -0.38, 0.34, 1.04, 1.68, 1.92, 2.11]\n Course: [0.0, -0.05, -0.03, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because there's a speed bump.\n\n",
+    "BDDX_Processed/video/043026cb-f72901db_02532.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.78, 0.26, 0.02, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.03, -0.12, -0.09, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped with its wipers on\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red and it is raining.\n\n",
+    "BDDX_Processed/video/1d4c2467-b3826487_18493.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [24.95, 23.66, 21.54, 20.39, 20.02, 19.14, 18.26]\n Curvature: [0.0, 0.01, -0.03, -0.02, -0.06, -0.05, -0.05]\n Acceleration: [-0.56, -1.18, -1.25, -0.36, -0.39, -0.59, -0.57]\n Course: [0.0, 0.68, 0.58, 1.22, 2.0, 2.99, 2.04]\nWhat is the action of ego car?\nAssistant: The car heads down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/1cbc652a-535dd150_18204.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.34, 2.26, 2.18, 2.1, 2.02, 1.94, 1.86]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.0, 0.0, 0.0]\n Acceleration: [-1.65, -1.52, -1.38, -1.25, -1.12, -0.99, -0.85]\n Course: [0.0, -0.07, -0.07, -0.07, -0.07, -0.07, -0.07]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a stop sign at an intersection.\n\n",
+    "BDDX_Processed/video/03a3f4e6-caeaf05d_01031.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [30.4, 30.41, 30.46, 30.55, 30.45, 30.29, 30.04]\n Curvature: [-0.0, 0.01, 0.02, 0.02, 0.02, 0.02, 0.02]\n Acceleration: [0.1, 0.09, 0.05, -0.01, -0.18, -0.25, -0.13]\n Course: [0.0, -0.4, -0.3, -0.24, -0.23, -0.23, -0.29]\nWhat is the action of ego car?\nAssistant: The car is merging into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: as it has no cars in it.\n\n",
+    "BDDX_Processed/video/20652cf8-657281a8_20055.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.17, 1.38, 0.08, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.95, -0.86, -0.18, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a halt\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic ahead is backed up behind a red light.\n\n",
+    "BDDX_Processed/video/2070bc4c-85b87247_20117.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.07, 1.25, 1.57, 1.36, 1.14, 1.02, 0.13]\n Curvature: [-0.01, -0.42, 0.39, -0.15, 0.01, -0.35, -1.15]\n Acceleration: [-0.27, -0.64, 0.14, -0.2, -0.04, -0.19, -0.42]\n Course: [0.0, 0.0, 0.0, 0.39, -0.33, -0.16, 0.0]\nWhat is the action of ego car?\nAssistant: The car backs up and stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turned red and it was blocking the crosswalk.\n\n",
+    "BDDX_Processed/video/1481f91e-5647c157_13971.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.2, 10.02, 11.0, 11.19, 11.21, 10.56, 9.99]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.29, 0.81, 0.33, 0.1, -0.24, -0.36, -0.52]\n Course: [0.0, 0.0, -0.01, 0.0, 0.0, 0.0, 0.03]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/04095551-ef1928db_02185.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.13, 15.22, 15.31, 15.4, 15.49, 15.58, 15.67]\n Curvature: [-0.01, -0.01, -0.01, -0.01, -0.0, 0.01, 0.01]\n Acceleration: [0.54, 0.49, 0.44, 0.39, 0.34, 0.33, 0.31]\n Course: [0.0, 0.11, 0.11, 0.11, 0.08, 0.05, 0.05]\nWhat is the action of ego car?\nAssistant: The car is drifting left.\n\nHuman: Why does the ego car doing this?\nAssistant: Because the driver is switching lanes from right to left.\n\n",
+    "BDDX_Processed/video/1698c1ee-02b63e18_15260.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.45, 4.01, 1.92, 0.5, 0.52, 0.75, 1.02]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-3.53, -3.22, -2.43, -1.11, -0.21, 0.41, 0.54]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because a pedestrian is crossing the street.\n\n",
+    "BDDX_Processed/video/1ad82020-197ae63b_17460.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [28.31, 27.7, 26.0, 24.55, 24.82, 23.72, 23.62]\n Curvature: [0.0, 0.0, -0.01, -0.01, -0.0, 0.0, 0.01]\n Acceleration: [-0.07, -0.37, -0.88, 0.11, -0.41, -0.41, -0.08]\n Course: [0.0, 0.0, -0.24, -0.48, -0.24, 0.0, -1.37]\nWhat is the action of ego car?\nAssistant: The car travels in the right lane of the highway at night\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving well.\n\n",
+    "BDDX_Processed/video/20ed775f-4d9f4da5_20283.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.39, 1.42, 2.53, 3.67, 4.81, 5.97, 7.06]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.03, -0.05]\n Acceleration: [1.19, 1.68, 2.14, 2.19, 2.23, 2.2, 2.16]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.03]\nWhat is the action of ego car?\nAssistant: The car carries on down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned green.\n\n",
+    "BDDX_Processed/video/02a17951-3d88d95e_02386.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.39, 11.27, 11.16, 11.04, 10.86, 10.34, 9.88]\n Curvature: [-0.01, 0.0, 0.02, 0.03, 0.04, 0.02, 0.01]\n Acceleration: [-0.8, -0.91, -1.01, -1.12, -1.21, -1.17, -1.14]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The vehicle brakes slightly\n\nHuman: Why does the ego car doing this?\nAssistant: because the cars in front of it are braking for traffic.\n\n",
+    "BDDX_Processed/video/1ab50992-a88d0328_17312.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.72, 12.01, 12.32, 12.68, 13.04, 13.28, 13.3]\n Curvature: [-0.0, 0.0, 0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.43, 0.48, 0.52, 0.55, 0.4, 0.19, -0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.01, -0.02]\nWhat is the action of ego car?\nAssistant: The car is steering to the left\n\nHuman: Why does the ego car doing this?\nAssistant: after it has passed the cars in that lane.\n\n",
+    "BDDX_Processed/video/167e3ac6-11c74158_15172.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.57, 5.18, 5.89, 6.65, 7.51, 8.29, 9.04]\n Curvature: [-0.04, -0.04, -0.01, 0.04, 0.05, 0.04, 0.03]\n Acceleration: [1.5, 1.25, 1.15, 1.25, 1.24, 1.07, 0.6]\n Course: [0.0, 1.03, -0.23, -0.18, -2.4, -0.56, -0.7]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear.\n\n",
+    "BDDX_Processed/video/1125356f-56500f04_12262.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.92, 12.34, 12.81, 11.92, 11.35, 11.43, 11.43]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, -0.0]\n Acceleration: [0.56, 0.5, -0.27, -0.71, -0.3, 0.02, -0.04]\n Course: [0.0, -0.47, -0.59, 0.0, -0.71, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car proceeds forward at a slower pace\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is still a bit backed up.\n\n",
+    "BDDX_Processed/video/0378d8dc-45d0c6c6_05566.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.19, 8.14, 7.23, 7.39, 8.52, 9.63, 10.18]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.08, -0.17, -0.32, 0.25, 0.55, 0.26, 0.11]\n Course: [0.0, 0.0, 0.0, 0.0, 1.23, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in front of it drives forward.\n\n",
+    "BDDX_Processed/video/1aa41265-a89ad4f3_17168.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.76, 2.31, 2.86, 3.41, 3.89, 4.44, 5.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.81, 1.89, 1.97, 2.05, 2.11, 2.12, 2.13]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/0d606092-486670ee_10517.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.36, 1.05, 1.89, 2.81, 3.79, 4.63]\n Curvature: [0.04, -0.05, -0.07, -0.01, 0.11, -0.04, -0.04]\n Acceleration: [0.23, 0.63, 0.96, 1.15, 1.2, 1.15, 1.1]\n Course: [0.0, -0.06, -0.22, -0.15, -0.25, -0.53, -0.15]\nWhat is the action of ego car?\nAssistant: The car accelerates through the intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the intersection is clear.\n\n",
+    "BDDX_Processed/video/06836e19-0798237e_06632.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.64, 6.4, 6.74, 7.3, 8.1, 9.14, 9.81]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.08, 0.08, 0.27, 0.41, 0.45, 0.52, 0.37]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/20ed775f-4d9f4da5_20281.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.64, 10.04, 6.57, 3.21, 2.55, 2.88, 4.53]\n Curvature: [-0.0, -0.07, -0.28, -0.13, -0.11, -0.52, 0.69]\n Acceleration: [-1.81, -2.94, -3.35, -1.95, -0.18, 0.98, 1.82]\n Course: [0.0, 1.34, 4.52, 6.66, 1.17, 11.88, -48.89]\nWhat is the action of ego car?\nAssistant: The car makes a hard right\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/04e9173a-d978b34d_06120.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [28.06, 30.13, 28.22, 22.87, 22.91, 25.38, 23.16]\n Curvature: [-0.0, 0.0, 0.0, -0.0, 0.01, 0.01, 0.02]\n Acceleration: [0.84, 0.13, -1.55, -1.29, 1.0, 0.83, -1.42]\n Course: [0.0, 0.02, -0.29, 0.0, -0.6, -0.91, -0.34]\nWhat is the action of ego car?\nAssistant: The car moves forward then merges right\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving forward and clear\n\n",
+    "BDDX_Processed/video/1564ef78-6cd81da9_14497.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.35, 5.08, 8.71, 9.48, 9.77, 9.74, 9.67]\n Curvature: [0.0, 0.04, -0.02, -0.0, 0.0, 0.01, -0.04]\n Acceleration: [1.5, 2.12, 1.26, 0.25, 0.15, -0.09, -0.07]\n Course: [0.0, 0.0, 0.0, 0.31, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light is green and there are no cars ahead of it.\n\n",
+    "BDDX_Processed/video/190bb329-2d95a35f_16541.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.61, 6.73, 6.83, 7.0, 7.26, 7.52, 7.3]\n Curvature: [0.09, 0.08, 0.07, 0.05, -0.01, -0.07, -0.06]\n Acceleration: [0.13, 0.26, 0.39, 0.4, 0.16, -0.08, -0.58]\n Course: [0.0, -0.82, -0.54, -0.35, -0.35, -0.35, 0.05]\nWhat is the action of ego car?\nAssistant: The car moves to the left\n\nHuman: Why does the ego car doing this?\nAssistant: because it has passed the car in the middle and there is a pedestrian ahead on the right.\n\n",
+    "BDDX_Processed/video/1371f6a6-eeb284f1_13450.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.45, 5.62, 4.55, 3.66, 2.97, 2.25, 1.32]\n Curvature: [0.29, 0.28, 0.01, -0.05, -5.53, -4.44, -0.0]\n Acceleration: [-0.51, -1.01, -1.07, -0.89, -0.79, -0.93, -1.0]\n Course: [0.0, 0.0, -2.79, 0.0, 0.89, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls over to the right side of the road and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as there is an open area to park.\n\n",
+    "BDDX_Processed/video/1726919b-18c3e190_15614.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/115d9e12-17808939_12461.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.83, 2.42, 2.09, 2.44, 2.78, 3.17, 3.57]\n Curvature: [0.02, 0.01, -0.01, -0.03, -0.06, -0.08, -0.09]\n Acceleration: [-1.11, -0.54, -0.02, 0.38, 0.7, 0.74, 0.76]\n Course: [0.0, 0.05, 0.04, 0.0, 0.21, 0.42, 2.22]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning to the right.\n\n",
+    "BDDX_Processed/video/1136797c-862a53c6_12336.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.73, 1.85, 1.63, 1.07, 0.85, 0.65, 0.95]\n Curvature: [0.0, 0.02, -0.02, 0.0, 0.0, 0.04, 0.1]\n Acceleration: [-1.87, -1.01, -0.39, -0.38, -0.21, 0.03, 0.52]\n Course: [0.0, 0.01, 0.07, 0.0, 0.0, 0.0, -0.08]\nWhat is the action of ego car?\nAssistant: The car is moving slowly forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the car in front of it is moving slowly forward.\n\n",
+    "BDDX_Processed/video/0d034e14-638e9c7f_10276.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.23, 9.02, 7.88, 6.41, 5.01, 3.08, 1.36]\n Curvature: [-0.05, -0.05, -0.05, -0.04, -0.04, -0.02, -0.0]\n Acceleration: [-2.09, -2.33, -2.55, -2.89, -3.16, -2.83, -2.38]\n Course: [0.0, 0.95, 0.95, 0.98, 0.56, 0.28, 0.07]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a car stopped ahead and the traffic light is red.\n\n",
+    "BDDX_Processed/video/1572db6a-1bcc874c_14563.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.71, 16.94, 17.15, 17.28, 17.27, 17.26, 17.27]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.47, 0.39, 0.32, 0.23, 0.12, 0.01, 0.03]\n Course: [0.0, 0.07, 0.05, 0.0, 0.0, 0.0, 0.07]\nWhat is the action of ego car?\nAssistant: The car is drifting to the right\n\nHuman: Why does the ego car doing this?\nAssistant: Because it is switching one lane over from left to right.\n\n",
+    "BDDX_Processed/video/10c13e86-9ae3c059_11968.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.13, 0.25, 0.38, 0.48, 0.6, 0.71]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.23, 0.29, 0.35, 0.41, 0.45, 0.45, 0.45]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins to pull back onto the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the street is clear.\n\n",
+    "BDDX_Processed/video/11e3b091-1184028d_12711.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.15, 12.13, 12.12, 12.41, 13.14, 13.87, 14.16]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.03]\n Acceleration: [0.53, 0.68, 0.83, 0.97, 1.07, 1.18, 0.94]\n Course: [0.0, 0.04, 0.04, 0.0, 0.0, 0.0, -0.01]\nWhat is the action of ego car?\nAssistant: The car continues to accelerate\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear of traffic.\n\n",
+    "BDDX_Processed/video/11b47a8a-24a77ef8_12650.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.54, 1.25, 1.94, 2.98, 3.96, 5.0, 5.97]\n Curvature: [-0.1, 0.0, 0.09, 0.01, -0.05, -0.04, -0.03]\n Acceleration: [0.82, 1.26, 1.66, 1.84, 1.96, 1.8, 1.62]\n Course: [0.0, -0.09, -0.06, 0.05, 0.15, 0.27, 0.63]\nWhat is the action of ego car?\nAssistant: The car is driving forward again\n\nHuman: Why does the ego car doing this?\nAssistant: because the car pulls to the right.\n\n",
+    "BDDX_Processed/video/0832b2c1-59228fb8_07496.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.58, 1.29, 1.2, 0.87, 0.69, 0.81, 1.13]\n Curvature: [0.18, 0.93, 1.29, 1.37, 1.37, 1.38, 1.38]\n Acceleration: [0.7, 0.33, -0.18, -0.27, -0.03, 0.24, 0.5]\n Course: [0.0, 0.0, -7.88, -2.71, -1.89, -1.48, -1.97]\nWhat is the action of ego car?\nAssistant: The car is turning left\n\nHuman: Why does the ego car doing this?\nAssistant: since the pedestrians have finished crossing\n\n",
+    "BDDX_Processed/video/20ed775f-831b83b3_20291.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.74, 12.2, 11.93, 11.37, 10.31, 10.53, 11.41]\n Curvature: [-0.0, 0.01, 0.01, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.15, -0.14, -0.24, -0.58, 0.04, 0.16, 0.71]\n Course: [0.0, -0.09, 0.21, -0.2, -0.19, 0.2, -0.41]\nWhat is the action of ego car?\nAssistant: The car continues straight at a moderate pace\n\nHuman: Why does the ego car doing this?\nAssistant: because the lights are green but there are some cars ahead.\n\n",
+    "BDDX_Processed/video/1d052365-9c94d0a4_18267.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.41, 1.93, 2.73, 3.36, 4.2, 5.2, 6.06]\n Curvature: [0.75, 2.01, 1.46, -0.48, -3.24, -5.61, -2.13]\n Acceleration: [1.18, 1.28, 1.06, 0.95, 1.18, 1.18, 1.29]\n Course: [0.0, 0.0, 0.0, 6.09, 8.48, 10.86, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning to the right.\n\n",
+    "BDDX_Processed/video/11318349-2868691f_12320.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.55, 12.82, 12.68, 12.04, 11.73, 11.29, 11.47]\n Curvature: [0.01, -0.05, -0.02, -0.05, -0.06, -0.11, -0.05]\n Acceleration: [-0.72, -0.41, -0.38, -0.46, -0.38, -0.12, 0.02]\n Course: [0.0, 0.78, 1.32, 2.06, 2.54, 2.22, 2.15]\nWhat is the action of ego car?\nAssistant: The car drives down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear.\n\n",
+    "BDDX_Processed/video/0d0e69b6-507e6590_10298.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: There is a red light.\n\n",
+    "BDDX_Processed/video/2222be72-7cb9859e_20880.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.01, 0.12, 0.02, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.12, -1.8, -10.51, -10.63, -10.75, -10.87, -10.99]\n Acceleration: [-2.96, -0.07, -0.1, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, -0.22, -0.2, -0.12, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops fast\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is red\n\n",
+    "BDDX_Processed/video/206a28b8-e4154a94_20096.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.65, 6.21, 7.6, 8.48, 8.76, 8.97, 9.14]\n Curvature: [-0.02, -0.0, 0.02, 0.02, 0.0, 0.0, 0.0]\n Acceleration: [2.1, 1.91, 1.4, 0.8, 0.31, 0.25, 0.02]\n Course: [0.0, 0.68, -0.61, -0.48, -0.33, -0.14, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues to move forward\n\nHuman: Why does the ego car doing this?\nAssistant: since the cars in front are moving again.\n\n",
+    "BDDX_Processed/video/13fbfc19-d10661aa_13709.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: Because it is parked on the right side of the street.\n\n",
+    "BDDX_Processed/video/190aeadf-778449d8_16529.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.89, 6.22, 6.51, 6.85, 7.21, 7.74, 8.21]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.57, 1.59, 1.6, 1.62, 1.63, 1.59, 1.56]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls over to the right lane.\n\nHuman: Why does the ego car doing this?\nAssistant: because the driver wants to pass the car in front.\n\n",
+    "BDDX_Processed/video/0f89108b-3ab59119_11453.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.54, 4.68, 4.83, 4.97, 5.4, 5.73, 5.79]\n Curvature: [-0.0, -0.01, 0.0, 0.05, 0.06, 0.05, 0.02]\n Acceleration: [0.22, 0.22, 0.3, 0.46, 0.44, 0.32, 0.02]\n Course: [0.0, -0.42, -0.53, -0.42, -0.85, -0.7, -0.88]\nWhat is the action of ego car?\nAssistant: The car is moving forward at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/10af6ba8-167c93c2_11908.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21]\n Acceleration: [-0.66, -0.57, -0.49, -0.4, -0.32, -0.23, -0.15]\n Course: [0.0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]\nWhat is the action of ego car?\nAssistant: The car is stopped..\n\nHuman: Why does the ego car doing this?\nAssistant: because there is another pedestrian crossing the drive.\n\n",
+    "BDDX_Processed/video/144bd77c-0a461478_13871.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.33, 9.91, 10.54, 10.94, 11.07, 11.55, 11.55]\n Curvature: [0.0, -0.0, 0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.95, 0.76, 0.32, 0.08, 0.26, 0.2, -0.33]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the lane at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear.\n\n",
+    "BDDX_Processed/video/1e6a6965-4b3d39fe_19075.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.36, 4.06, 3.83, 3.93, 4.33, 4.84, 4.99]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.66, -0.86, -0.13, 0.27, 0.49, 0.34, 0.14]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because the grey car ahead is driving at a slow constant speed.\n\n",
+    "BDDX_Processed/video/1532b0d8-91c5e136_14324.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.85, 11.76, 11.67, 11.31, 10.57, 9.83, 8.24]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.04]\n Acceleration: [-0.03, -0.43, -0.8, -1.42, -2.32, -3.22, -3.77]\n Course: [0.0, -0.04, -0.02, -0.07, -0.07, -0.07, -0.09]\nWhat is the action of ego car?\nAssistant: The car abruptly slows down to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light it is approaching suddenly turns from green to yellow to red.\n\n",
+    "BDDX_Processed/video/09ac7416-8fe666f5_08367.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: for a red light.\n\n",
+    "BDDX_Processed/video/0ce5101a-9f7e7fd8_10165.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.79, 5.04, 5.27, 5.46, 5.54, 5.63, 5.28]\n Curvature: [-0.06, -0.05, -0.05, -0.03, -0.01, 0.01, 0.02]\n Acceleration: [0.72, 0.6, 0.49, 0.28, -0.07, -0.43, -0.98]\n Course: [0.0, 0.53, 0.35, 1.05, 1.05, 1.05, 0.32]\nWhat is the action of ego car?\nAssistant: The car is accelerating and steering to the left and back\n\nHuman: Why does the ego car doing this?\nAssistant: to get around a vehicle parked on the right side of the road.\n\n",
+    "BDDX_Processed/video/0cf6f074-33fb55d1_10225.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.1, 8.57, 8.1, 7.57, 7.0, 6.2, 5.49]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.96, -1.37, -1.73, -2.14, -2.47, -2.19, -1.95]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes suddenly\n\nHuman: Why does the ego car doing this?\nAssistant: because a SUV pulls out in the road in front of it\n\n",
+    "BDDX_Processed/video/1aa6dd36-aa7f0108_17218.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.11, 7.82, 7.56, 7.45, 7.59, 7.74, 8.2]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-1.4, -0.91, -0.46, -0.0, 0.36, 0.76, 0.78]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's going over a speed bump.\n\n",
+    "BDDX_Processed/video/0ea102ca-03a24c44_11151.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.19, 0.86, 0.36, 0.14, 0.0, 0.0, 0.0]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.33, -0.28, -0.2, -0.12, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The is waiting at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/1f2adee2-cf47182b_19401.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.94, 6.49, 6.1, 5.8, 5.68, 5.76, 6.08]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.82, 0.99, 0.13]\n Acceleration: [-1.31, -0.84, -0.5, -0.32, -0.01, 0.33, 0.72]\n Course: [0.0, 0.0, 0.0, 0.0, -6.77, 49.23, 49.23]\nWhat is the action of ego car?\nAssistant: The car slows and makes a left\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green and there are no obstructions.\n\n",
+    "BDDX_Processed/video/17fb67eb-28a14e3e_15995.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.25, 0.66, 2.25, 5.32, 8.48, 9.18, 8.46]\n Curvature: [-0.0, -0.0, 0.0, -0.04, -0.0, 0.09, 0.02]\n Acceleration: [-2.0, 0.59, 0.86, 1.73, 0.91, -0.39, 0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.57, 1.8, -1.23]\nWhat is the action of ego car?\nAssistant: The car is driving through a parking lot area\n\nHuman: Why does the ego car doing this?\nAssistant: because the area is clear.\n\n",
+    "BDDX_Processed/video/10af5310-535c5f15_11897.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.2, -0.04, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/06ebf6cd-79235c8d_00161.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-6.43, -6.5, -6.57, -6.64, -6.71, -6.78, -6.85]\n Acceleration: [-0.32, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.15, 0.0, 0.01, 0.0, 0.14, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0cfd4cb3-5bc4199e_10248.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.25, 0.17, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.05, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03]\n Acceleration: [-1.11, -0.42, -0.03, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.09, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/18f3b2d1-535737f9_16469.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.87, 8.48, 7.1, 5.86, 4.67, 4.41, 4.32]\n Curvature: [-0.01, -0.09, -0.16, -0.21, -0.24, -0.19, -0.14]\n Acceleration: [-2.3, -2.44, -2.55, -2.0, -1.37, -0.41, 0.49]\n Course: [0.0, 0.73, 1.4, 3.43, 4.63, 4.13, 3.01]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it is turning to the right.\n\n",
+    "BDDX_Processed/video/127f8da7-6380cec8_12942.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.22, 1.33, 1.43, 1.54, 1.65, 1.75, 1.84]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.42, 0.41, 0.41, 0.4, 0.4, 0.38, 0.36]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: as there is a car on the shoulder making a maneuver to enter the lane.\n\n",
+    "BDDX_Processed/video/06d501fd-73489e78_04223.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.93, 3.76, 2.57, 1.5, 0.77, 0.37, 0.41]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.58, -1.7, -1.65, -1.39, -0.84, -0.41, -0.21]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is red.\n\n",
+    "BDDX_Processed/video/2090b4bf-81f652f3_20162.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.63, 16.81, 16.25, 15.41, 15.57, 16.28, 17.02]\n Curvature: [-0.0, 0.0, -0.0, -0.0, 0.01, -0.0, 0.01]\n Acceleration: [0.25, -0.13, -0.58, -0.3, 0.39, 0.55, 0.54]\n Course: [0.0, -0.18, -0.11, -0.23, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the highway at a modest pace\n\nHuman: Why does the ego car doing this?\nAssistant: because the highway has some traffic.\n\n",
+    "BDDX_Processed/video/1f082727-d2d0ba27_19320.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.84, 3.54, 4.37, 5.43, 6.8, 7.98, 8.81]\n Curvature: [0.45, 0.03, 0.01, -0.01, 0.06, -0.04, 0.04]\n Acceleration: [1.52, 0.81, 0.52, 0.8, 0.83, 0.62, 0.61]\n Course: [0.0, 1.69, 0.15, -1.41, -0.7, 0.42, -0.34]\nWhat is the action of ego car?\nAssistant: The car pulls on to the road and heads straight\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green and the road is clear.\n\n",
+    "BDDX_Processed/video/1e7ef910-f0c71d3d_19142.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.83, 4.78, 4.74, 4.67, 4.57, 4.46, 4.53]\n Curvature: [-0.01, -0.0, 0.0, 0.0, -0.01, -0.01, -0.01]\n Acceleration: [0.21, 0.05, -0.1, -0.16, -0.09, -0.01, 0.18]\n Course: [0.0, -0.04, -0.03, 0.04, 0.03, 0.04, 0.07]\nWhat is the action of ego car?\nAssistant: The car moved at a steady speed\n\nHuman: Why does the ego car doing this?\nAssistant: because a red moving truck is ahead of it.\n\n",
+    "BDDX_Processed/video/1183ef80-67676319_12575.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.88, 2.27, 7.53, 10.64, 11.78, 12.7, 12.69]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.55, 1.17, 2.51, 0.27, 0.87, 0.19, -0.4]\n Course: [0.0, 0.0, -0.04, 0.0, 0.0, 0.0, -0.01]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the intersection is clear.\n\n",
+    "BDDX_Processed/video/14143b8a-92df0f1d_13797.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.06, 9.82, 8.66, 6.02, 3.61, 1.94, 0.52]\n Curvature: [0.01, 0.01, 0.01, -0.03, -0.07, -0.08, -0.09]\n Acceleration: [-1.39, -2.57, -3.68, -3.87, -3.97, -2.8, -1.7]\n Course: [0.0, -0.09, -0.09, 0.0, 0.01, 0.05, 0.01]\nWhat is the action of ego car?\nAssistant: The car is slowing down to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light at the inter\n\n",
+    "BDDX_Processed/video/094a88ce-225be062_08143.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.8, 9.93, 10.06, 10.19, 10.32, 10.45, 10.58]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.26, 1.22, 1.19, 1.16, 1.13, 1.1, 1.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins merging right\n\nHuman: Why does the ego car doing this?\nAssistant: as the right lane is open.\n\n",
+    "BDDX_Processed/video/0d4c22af-47d33da7_10498.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.6, 2.93, 3.25, 3.58, 3.88, 4.04, 4.19]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.03, 1.01, 0.98, 0.96, 0.92, 0.84, 0.76]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward slowly with its windshield wipers on\n\nHuman: Why does the ego car doing this?\nAssistant: because it is raining and there is no traffic in front of it.\n\n",
+    "BDDX_Processed/video/1717e233-36ade135_15588.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.09, 0.31, 0.55, 0.91, 1.48]\n Curvature: [-0.0, -0.0, -7.04, -24.64, -18.23, -9.19, -1.21]\n Acceleration: [0.0, 0.11, 0.22, 0.34, 0.51, 0.69, 0.91]\n Course: [0.0, 0.06, 0.06, 0.06, -0.09, 59.36, 59.36]\nWhat is the action of ego car?\nAssistant: The car accelerates as it pulls onto the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear of traffic.\n\n",
+    "BDDX_Processed/video/13620abf-62b6685f_13405.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.08, 0.16, 0.25, 0.34, 0.43, 0.8]\n Curvature: [-0.2, -0.27, -0.34, -0.41, -0.47, -0.54, -0.41]\n Acceleration: [0.03, 0.11, 0.18, 0.3, 0.45, 0.62, 0.62]\n Course: [0.0, -0.11, -0.11, -0.25, -0.16, -0.25, -0.39]\nWhat is the action of ego car?\nAssistant: The car slowly pulls into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: as there are no cars driving in that lane.\n\n",
+    "BDDX_Processed/video/0e7ecff9-214db317_11009.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/2218c04b-c93771df_20865.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.51, 12.87, 13.27, 12.73, 12.82, 12.6, 12.38]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.01, -0.01, -0.01]\n Acceleration: [0.3, 0.35, -0.1, -0.18, 0.06, -0.17, -0.13]\n Course: [0.0, 0.27, -0.2, 0.14, -0.14, 0.0, 0.65]\nWhat is the action of ego car?\nAssistant: The car proceeds ahead down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear ahead.\n\n",
+    "BDDX_Processed/video/13796ade-b4bcd515_13494.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.53, 7.26, 7.9, 8.49, 9.08, 9.43, 9.36]\n Curvature: [0.0, -0.0, 0.08, 0.1, -0.0, -0.0, -0.0]\n Acceleration: [0.95, 0.87, 0.81, 0.73, 0.58, 0.18, -0.6]\n Course: [0.0, 0.0, 0.0, 0.05, 0.02, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward while decelerating\n\nHuman: Why does the ego car doing this?\nAssistant: because it is coming up behind a small SUV that is stopped at a red light.\n\n",
+    "BDDX_Processed/video/0814c9ab-a1ba9412_07430.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.77, 0.62, 0.49, 0.34, 0.2, 0.15, 0.1]\n Curvature: [0.1, 0.08, 0.07, 0.05, 0.04, 0.04, 0.04]\n Acceleration: [-0.36, -0.36, -0.37, -0.38, -0.38, -0.3, -0.24]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is still red.\n\n",
+    "BDDX_Processed/video/031637c4-655be58e_05481.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.19, 0.41, 1.34, 2.22, 3.23, 4.2]\n Curvature: [0.06, 0.09, 0.12, 0.09, 0.07, 0.01, -0.03]\n Acceleration: [0.17, 0.64, 1.07, 1.48, 1.82, 1.78, 1.73]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.23]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turned green and the road is clear.\n\n",
+    "BDDX_Processed/video/06924f46-1708b96f_06637.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.9, 3.94, 3.05, 2.32, 1.96, 1.56, 1.26]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-2.41, -2.13, -1.87, -1.56, -1.25, -0.9, -0.63]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has stopped.\n\n",
+    "BDDX_Processed/video/21efbb21-b31b9f50_20742.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.17, 7.25, 7.36, 7.98, 8.54, 8.95, 9.37]\n Curvature: [0.58, 0.79, 0.95, 0.42, -0.01, -0.01, -0.0]\n Acceleration: [-0.23, 0.23, 0.67, 0.85, 0.99, 0.96, 0.92]\n Course: [0.0, -6.59, -4.72, -5.68, -2.84, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The vehicle crosses the left lane as it makes a left turn\n\nHuman: Why does the ego car doing this?\nAssistant: because there was no traffic in its way of making the turn.\n\n",
+    "BDDX_Processed/video/13d62f3f-49dd7dbf_13681.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.23, 8.28, 8.29, 7.94, 7.42, 7.03, 7.38]\n Curvature: [0.0, 0.0, -0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.26, 0.03, -0.16, -0.43, -0.46, -0.01, 0.4]\n Course: [0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues to drive down the street and passes a final green light\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no obstacles in its path.\n\n",
+    "BDDX_Processed/video/02d478d1-cfb83eb8_03209.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.36, 1.27, 2.25, 3.07, 3.79, 4.45, 5.4]\n Curvature: [-0.07, -0.06, -0.05, -0.03, -0.01, 0.01, 0.01]\n Acceleration: [1.41, 1.67, 1.94, 2.11, 2.11, 2.11, 2.08]\n Course: [0.0, -0.21, -0.32, -0.05, -0.07, -0.05, 0.04]\nWhat is the action of ego car?\nAssistant: The car is accelerating and moving into the roadway\n\nHuman: Why does the ego car doing this?\nAssistant: The car is leaving its parking space.\n\n",
+    "BDDX_Processed/video/128a6c14-7c985c35_12974.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.36, 0.22, 0.0, 0.0, 0.13, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.01, -0.11, -0.07, 0.02, -0.02, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at a crosswalk\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red and pedestrians are crossing the street.\n\n",
+    "BDDX_Processed/video/02f767b2-c435cd50_05407.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.86, 3.02, 3.29, 3.62, 4.01, 4.84, 6.08]\n Curvature: [0.0, -0.0, -0.09, -1.16, -0.0, -0.0, 0.0]\n Acceleration: [0.09, 0.21, 0.29, 0.34, 0.58, 1.0, 1.0]\n Course: [0.0, 0.0, 2.51, 11.32, 6.72, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning right and moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: since the lane and intersection are clear.\n\n",
+    "BDDX_Processed/video/0c140522-57d3aac5_09716.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.27, 0.29, 0.31, 0.33, 0.35, 0.37, 0.38]\n Curvature: [-0.55, -0.61, -0.67, -0.74, -0.78, -0.64, -0.52]\n Acceleration: [0.1, 0.09, 0.08, 0.08, 0.08, 0.13, 0.17]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: at the red light.\n\n",
+    "BDDX_Processed/video/129b62fc-44b12a9c_13003.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.57, 0.42, 0.0, 0.0, 0.22, 0.46, 0.18]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.99, -0.13, -0.09, 0.0, 0.14, 0.03, -0.14]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/11b47a8a-b54f88dd_12666.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.22, 11.72, 11.89, 11.7, 11.72, 11.85, 11.68]\n Curvature: [-0.01, -0.0, -0.07, -0.16, -0.03, 0.47, -0.3]\n Acceleration: [0.63, 0.31, -0.02, -0.08, 0.07, -0.01, -0.32]\n Course: [0.0, 0.0, 0.0, -0.3, 10.94, 0.0, -12.21]\nWhat is the action of ego car?\nAssistant: The car proceeds down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the street is clear.\n\n",
+    "BDDX_Processed/video/0a765e5c-ecfdc16f_08800.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.71, 10.68, 10.36, 9.54, 9.28, 8.99, 8.76]\n Curvature: [0.0, -0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.55, -0.16, -0.56, -0.52, -0.27, -0.24, -0.54]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is slowing down.\n\n",
+    "BDDX_Processed/video/160a76cc-b5993b9d_14887.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.07, 4.2, 4.26, 3.92, 3.29, 2.35, 1.56]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [1.7, 1.14, -0.09, -0.55, -0.85, -0.93, -0.79]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates in the center of the road\n\nHuman: Why does the ego car doing this?\nAssistant: to wait for oncoming traffic to pass.\n\n",
+    "BDDX_Processed/video/1221ce36-0146b627_12808.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.3, 0.34, 0.26, 0.35, 0.45, 0.6, 0.69]\n Curvature: [-0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 0.0]\n Acceleration: [0.04, -0.02, 0.02, 0.09, 0.15, 0.13, -0.01]\n Course: [0.0, 0.0, 0.0, -1.0, 1.21, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving slowly in reverse\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear.\n\n",
+    "BDDX_Processed/video/08b99c7c-e3815fd5_07888.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.85, 6.93, 7.02, 7.1, 7.19, 7.27, 7.35]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.62, 0.62, 0.62, 0.63, 0.63, 0.64, 0.64]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car turns slightly to the right\n\nHuman: Why does the ego car doing this?\nAssistant: to get back into it's lane\n\n",
+    "BDDX_Processed/video/0f21e7e2-0fa41159_11307.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.53, 5.52, 5.93, 6.14, 5.54, 4.02, 3.35]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.35, -0.13, -0.21, 0.34, -0.49, -0.32, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving slowly down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because there is moderate traffic on the road ahead.\n\n",
+    "BDDX_Processed/video/0a14a567-fdd08dd2_08656.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.88, 0.86, 0.08, 1.52, 1.63, 0.0, 0.0]\n Curvature: [0.0, 0.21, 0.06, -0.02, 0.17, 0.58, 0.23]\n Acceleration: [0.02, -0.62, 0.23, 0.54, -0.42, -0.15, 0.0]\n Course: [0.0, 0.23, 0.0, -0.06, 0.14, -0.17, -0.21]\nWhat is the action of ego car?\nAssistant: The car pulls forward slowly and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is an open area to park on the left side of the road.\n\n",
+    "BDDX_Processed/video/0d1fc580-61bbac5d_10375.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.25, 0.15, 0.06, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.13, -0.13, -0.13, -0.1, -0.05, -0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car inches forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there is still some space behind the truck.\n\n",
+    "BDDX_Processed/video/04629c39-faefdb18_06004.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.74, 1.49, 2.49, 2.83, 3.13, 3.32, 3.94]\n Curvature: [0.83, 0.75, 0.28, 0.27, 0.26, 0.14, 0.01]\n Acceleration: [0.79, 0.97, 0.72, 0.4, 0.26, 0.48, 0.66]\n Course: [0.0, -5.48, -15.59, 72.16, -8.44, -5.41, 1.43]\nWhat is the action of ego car?\nAssistant: The car pulls a U-turn onto the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/11b47a8a-24a77ef8_12647.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.08, 5.09, 7.79, 10.7, 13.76, 16.11, 16.58]\n Curvature: [0.0, -0.0, 0.0, -0.07, 0.03, -0.01, -0.0]\n Acceleration: [1.13, 1.27, 1.78, 1.83, 1.68, 0.8, 0.01]\n Course: [0.0, 0.0, 0.0, -0.09, 0.3, -0.14, 0.16]\nWhat is the action of ego car?\nAssistant: The car is driving forward.\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/08c82c63-2d9a0088_07906.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.09, 11.92, 11.86, 11.77, 11.79, 11.96, 12.2]\n Curvature: [0.0, 0.0, -0.01, 0.01, -0.0, 0.0, 0.0]\n Acceleration: [-0.08, -0.07, -0.05, -0.03, 0.06, 0.13, 0.22]\n Course: [0.0, 0.0, 0.18, 0.01, -0.14, -0.29, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green and there are no nearby vehicles in its lane.\n\n",
+    "BDDX_Processed/video/0ee642f9-a93248b5_11258.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.66, 4.69, 4.71, 4.74, 4.7, 4.18, 3.73]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.12, -0.16, -0.4, -0.67, -0.94, -1.17, -1.38]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving slowly under an overpass\n\nHuman: Why does the ego car doing this?\nAssistant: because there is traffic.\n\n",
+    "BDDX_Processed/video/109d5675-ccf79e0e_11856.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.09, 10.61, 7.85, 4.96, 4.91, 5.01, 2.53]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.75]\n Acceleration: [-0.81, -0.95, -1.19, -0.63, 0.17, -0.38, -1.46]\n Course: [0.0, 0.17, 0.69, 0.0, 0.0, 0.0, -0.65]\nWhat is the action of ego car?\nAssistant: The car is slowing down.\n\nHuman: Why does the ego car doing this?\nAssistant: because there is traffic and a red light.\n\n",
+    "BDDX_Processed/video/10ced427-af655277_12012.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.28, 0.6, 0.91, 1.23, 1.55, 2.17, 2.79]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.54, 0.87, 1.21, 1.54, 1.81, 1.97, 2.13]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no traffic in the intersection.\n\n",
+    "BDDX_Processed/video/045cb354-470666ec_05973.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [27.13, 27.36, 27.59, 27.83, 28.03, 28.1, 28.18]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]\n Acceleration: [0.73, 0.69, 0.66, 0.62, 0.59, 0.52, 0.45]\n Course: [0.0, -0.11, -0.11, -0.11, -0.06, -0.14, -0.14]\nWhat is the action of ego car?\nAssistant: The car merges into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no cars in that lane ahead of it.\n\n",
+    "BDDX_Processed/video/01e05a91-2c9e154d_00292.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [35.56, 35.63, 35.7, 35.74, 35.73, 35.73, 35.62]\n Curvature: [0.0, 0.0, 0.01, 0.01, 0.01, 0.01, 0.01]\n Acceleration: [0.18, 0.14, 0.1, 0.04, -0.05, -0.15, -0.26]\n Course: [0.0, 0.0, 0.0, 0.03, 0.02, 0.03, -0.07]\nWhat is the action of ego car?\nAssistant: The car is merging into the next lane to the right.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is changing lanes to allow faster traffic to move by on the left\n\n",
+    "BDDX_Processed/video/12da8593-780d421a_13100.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.6, 12.17, 12.62, 13.0, 13.34, 13.67, 14.09]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, 0.02, -0.01]\n Acceleration: [0.72, 0.64, 0.55, 0.47, 0.45, 0.49, 0.49]\n Course: [0.0, 0.36, 0.14, 0.07, 0.14, 0.21, -0.14]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road ahead is clear.\n\n",
+    "BDDX_Processed/video/05acab14-b78b76c7_01328.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.19, 9.03, 8.88, 8.02, 6.34, 4.51, 2.86]\n Curvature: [0.01, 0.01, 0.01, -0.01, -0.02, -0.04, 0.25]\n Acceleration: [-0.51, -1.3, -2.03, -2.79, -3.46, -4.19, -3.45]\n Course: [0.0, -0.07, -0.07, 0.14, 0.09, 0.14, 0.04]\nWhat is the action of ego car?\nAssistant: The car comes to a quick stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has stopped.\n\n",
+    "BDDX_Processed/video/1f4b8e7d-82f7e836_19479.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.75, 14.7, 13.52, 12.3, 10.3, 8.57, 7.41]\n Curvature: [-0.01, -0.02, -0.01, 0.04, 0.01, -0.02, -0.01]\n Acceleration: [-0.54, -1.35, -2.04, -2.51, -2.52, -2.45, -2.41]\n Course: [0.0, 0.0, 0.3, 0.24, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light ahead is red.\n\n",
+    "BDDX_Processed/video/0714456d-88d85a8c_01076.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.29, 11.17, 13.67, 13.54, 10.92, 11.64, 11.94]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.0, 0.03, -0.01]\n Acceleration: [0.11, 1.16, 0.8, -1.35, -0.21, 0.4, -0.02]\n Course: [0.0, 0.09, -0.23, 0.18, -0.19, -0.75, -1.03]\nWhat is the action of ego car?\nAssistant: The car maintains a steady speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/0d22eba4-3ef57ba7_10407.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.97, 8.98, 8.98, 8.88, 8.78, 8.61, 8.45]\n Curvature: [0.03, 0.02, 0.02, -0.0, -0.02, -0.01, 0.0]\n Acceleration: [0.03, -0.04, -0.1, -0.18, -0.26, -0.27, -0.41]\n Course: [0.0, -0.23, -0.18, 0.0, 0.0, 0.0, -0.18]\nWhat is the action of ego car?\nAssistant: The car is merging into the lane to its left\n\nHuman: Why does the ego car doing this?\nAssistant: due to there being no traffic to its left.\n\n",
+    "BDDX_Processed/video/06257efc-9aba5724_06491.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.05, 10.26, 8.39, 6.77, 5.99, 5.84, 5.81]\n Curvature: [0.0, -0.0, -0.01, -0.01, -0.01, 0.0, -0.01]\n Acceleration: [-1.03, -1.74, -1.98, -1.56, -0.61, -0.16, -0.15]\n Course: [0.0, -0.12, 0.0, 0.07, 0.13, 0.21, 0.21]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: to match highway speed\n\n",
+    "BDDX_Processed/video/01ab0f6d-b74b888d_04229.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.28, 0.18, 0.0, 0.14, 0.0, 0.0, 0.0]\n Curvature: [0.66, 0.71, 0.71, 5.24, 5.96, 6.02, 6.08]\n Acceleration: [0.02, -0.14, -0.01, 0.01, -0.07, 0.0, 0.0]\n Course: [0.0, -0.09, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped.\n\n",
+    "BDDX_Processed/video/16504799-1e495b19_15046.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.92, 7.82, 7.72, 7.44, 6.93, 6.41, 5.62]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.79, 0.16, -0.41, -0.95, -1.36, -1.77, -2.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is empty.\n\n",
+    "BDDX_Processed/video/150c0f61-39d07de7_14221.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.56, 3.66, 2.59, 2.79, 3.61, 3.78, 3.68]\n Curvature: [0.0, 0.02, -0.11, 0.01, -0.06, -0.07, -0.2]\n Acceleration: [0.31, -0.5, -0.38, 0.46, 0.44, -0.01, 0.16]\n Course: [0.0, -0.02, 0.28, 0.87, 1.36, 3.19, 4.56]\nWhat is the action of ego car?\nAssistant: The car is making a right turn\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear to turn.\n\n",
+    "BDDX_Processed/video/09ac7416-d0e852a3_08399.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.99, 5.24, 4.55, 3.94, 3.57, 3.17, 3.2]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.81, -1.66, -1.53, -1.26, -0.87, -0.43, 0.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: as there is a stop sign ahead.\n\n",
+    "BDDX_Processed/video/13081d41-6bc6d1e4_13200.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.6, 10.28, 9.96, 9.93, 9.9, 9.79, 9.53]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.64, -0.49, -0.34, -0.23, -0.19, -0.6, -1.03]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road ahead is clear.\n\n",
+    "BDDX_Processed/video/07363609-5a0aaaf2_01782.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.62, 4.31, 3.99, 3.33, 2.62, 2.05, 2.4]\n Curvature: [0.0, -0.0, -0.02, -0.0, 0.02, 0.01, 0.02]\n Acceleration: [-0.05, -0.31, -0.47, -0.65, -0.63, -0.1, 0.85]\n Course: [0.0, -0.16, 0.05, 0.3, 0.14, 0.32, -0.06]\nWhat is the action of ego car?\nAssistant: The car moves forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a vehicle blocking part of the lane.\n\n",
+    "BDDX_Processed/video/1e9e8d92-69576065_19180.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.15, 11.74, 12.27, 12.54, 12.77, 12.89, 13.02]\n Curvature: [-0.0, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01]\n Acceleration: [1.28, 1.02, 0.79, 0.55, 0.36, 0.33, 0.29]\n Course: [0.0, 0.05, 0.08, 0.19, -0.19, -0.47, -0.27]\nWhat is the action of ego car?\nAssistant: The car moves over to the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no one in that lane.\n\n",
+    "BDDX_Processed/video/023a70d5-cde2a67a_01259.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.77, 5.01, 5.5, 4.07, 5.62, 8.27, 8.99]\n Curvature: [0.0, 0.0, 0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.09, 0.2, -0.52, 0.0, 1.12, 0.47, 0.18]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is travelling slowly down the road\n\nHuman: Why does the ego car doing this?\nAssistant: The car is keeping pace with the traffic ahead.\n\n",
+    "BDDX_Processed/video/1f4bbfc7-cf8e32e6_19515.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.11, 0.4, 0.93, 0.41, 0.09, 0.32]\n Curvature: [-0.13, 0.28, 0.51, 0.48, 0.48, 0.44, 0.34]\n Acceleration: [0.09, 0.34, 0.34, -0.05, -0.22, -0.2, 0.08]\n Course: [0.0, 0.06, -0.06, -0.06, 0.06, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves up a few feet\n\nHuman: Why does the ego car doing this?\nAssistant: because the truck in front has also moved up a bit.\n\n",
+    "BDDX_Processed/video/06cb522f-aaed5ecf_04010.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.7, 17.51, 17.33, 17.22, 17.23, 17.24, 17.34]\n Curvature: [0.11, 0.08, 0.05, 0.06, 0.13, 0.2, 0.14]\n Acceleration: [-0.28, -0.26, -0.24, -0.17, -0.01, 0.14, 0.19]\n Course: [0.0, -1.12, -1.12, -0.68, -0.91, -0.68, -0.89]\nWhat is the action of ego car?\nAssistant: The car veers left\n\nHuman: Why does the ego car doing this?\nAssistant: to merge into a faster moving lane.\n\n",
+    "BDDX_Processed/video/2221b5cf-b7a4e670_20874.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.46, 2.35, 3.92, 4.8, 7.11, 9.4, 14.18]\n Curvature: [0.0, -0.02, 0.28, 0.24, 0.0, 0.01, 0.01]\n Acceleration: [0.91, 0.54, 0.66, 0.16, 1.51, 1.17, 0.34]\n Course: [0.0, 0.26, -0.11, 0.0, 0.39, -0.32, 0.02]\nWhat is the action of ego car?\nAssistant: The car is driving at different speeds\n\nHuman: Why does the ego car doing this?\nAssistant: since the road changes.\n\n",
+    "BDDX_Processed/video/17ef9f60-965dcf54_15988.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.99, 13.62, 14.1, 14.13, 13.79, 13.34, 12.84]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.03, 0.49, 0.25, -0.16, -0.33, -0.42, -0.38]\n Course: [0.0, 0.0, 0.0, 0.1, 0.02, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives ahead down the street\n\nHuman: Why does the ego car doing this?\nAssistant: since the road is clear ahead.\n\n",
+    "BDDX_Processed/video/1e7b5f2c-91c36b1a_19131.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.31, 9.84, 8.24, 6.25, 4.48, 4.17, 4.05]\n Curvature: [0.01, 0.01, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-2.05, -2.72, -3.35, -2.75, -2.05, -0.71, 0.42]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows considerably\n\nHuman: Why does the ego car doing this?\nAssistant: to cross another speed bump.\n\n",
+    "BDDX_Processed/video/21efbb21-b31b9f50_20745.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.6, 1.07, 0.71, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.37, 0.15, -0.57, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The vehicle remains stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is at a red light.\n\n",
+    "BDDX_Processed/video/0c5cdb09-421eaaa8_09792.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.12, 8.11, 8.09, 8.08, 8.07, 8.03, 8.0]\n Curvature: [0.25, 0.12, -0.02, -0.16, -0.28, -0.28, -0.28]\n Acceleration: [0.07, 0.03, -0.01, -0.05, -0.09, -0.1, -0.1]\n Course: [0.0, 0.01, 0.01, 0.01, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows slightly\n\nHuman: Why does the ego car doing this?\nAssistant: for a bump in an intersection.\n\n",
+    "BDDX_Processed/video/0fc4a683-42d61e17_11554.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.47, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: for an unknown reason.\n\n",
+    "BDDX_Processed/video/0b08fd9d-2fdfb6e0_09147.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.39, 9.1, 8.48, 3.58, -1.0, -1.0, -0.03]\n Curvature: [0.05, 0.26, 0.46, 0.26, 0.07, 0.03, -0.0]\n Acceleration: [-1.18, -3.19, -5.19, -5.04, -4.25, 0.62, 4.36]\n Course: [0.0, 29.82, 29.82, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning doing a u turn in the left turn lane\n\nHuman: Why does the ego car doing this?\nAssistant: to change directions on the street\n\n",
+    "BDDX_Processed/video/0308ce2b-1c804293_03738.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.31, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.36, -0.15, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car has stopped in the roadway\n\nHuman: Why does the ego car doing this?\nAssistant: It is unclear why the driver has stopped.\n\n",
+    "BDDX_Processed/video/0c91b387-10ef9e01_09924.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.05, 6.77, 7.56, 8.22, 8.78, 9.39, 9.94]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.91, 1.84, 1.76, 1.68, 1.6, 1.51, 1.37]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward.\n\nHuman: Why does the ego car doing this?\nAssistant: The path is clear.\n\n",
+    "BDDX_Processed/video/13d62f3f-49dd7dbf_13679.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.91, 8.21, 8.3, 8.02, 7.36, 6.96, 7.04]\n Curvature: [-0.09, -0.01, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.48, 0.23, -0.1, -0.5, -0.55, -0.2, 0.16]\n Course: [0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the road\n\nHuman: Why does the ego car doing this?\nAssistant: as there are no obstructions in its path\n\n",
+    "BDDX_Processed/video/1eb1e178-839f6473_19205.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.18, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.18, 0.04, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0b3d47ed-6cb90089_09238.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.66, 8.11, 6.32, 4.44, 3.48, 2.46, 0.76]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.14, -2.53, -2.57, -2.18, -1.97, -1.78, -1.53]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: for the red light ahead.\n\n",
+    "BDDX_Processed/video/043026cb-62b088fd_02487.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.64, 2.35, 3.07, 3.29, 3.54, 3.81, 4.05]\n Curvature: [-0.12, -0.1, -0.08, -0.15, -0.22, -0.2, -0.17]\n Acceleration: [0.75, 0.84, 0.92, 0.7, 0.48, 0.4, 0.25]\n Course: [0.0, 1.05, 1.62, 1.69, 2.95, 2.74, 2.71]\nWhat is the action of ego car?\nAssistant: The car is making a right hand turn.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is rolling through the stop sign without properly stopping and is making a turn through the intersection.\n\n",
+    "BDDX_Processed/video/02d478d1-e5ed0eb1_05350.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.96, 4.23, 8.38, 12.48, 14.13, 13.28, 10.55]\n Curvature: [-0.22, -0.16, -0.02, 0.01, -0.0, -0.0, -0.0]\n Acceleration: [-1.79, 1.36, 2.04, 1.38, 0.24, -0.97, -1.54]\n Course: [0.0, 12.06, 2.46, -0.39, -0.09, 0.11, 0.05]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/14c2bf46-4c584358_14052.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.65, 5.65, 5.65, 5.65, 4.73, 3.32, 1.38]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, -0.24, -0.76, -1.68, -2.14, -1.61]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The rolls to a stop at an intersection with crosswalks\n\nHuman: Why does the ego car doing this?\nAssistant: because the street light is red.\n\n",
+    "BDDX_Processed/video/05640976-d1ce1090_00727.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.81, 8.41, 8.17, 7.24, 6.96, 6.53, 6.33]\n Curvature: [0.0, -0.0, 0.0, -0.0, -0.0, -0.01, -0.01]\n Acceleration: [0.08, -0.19, -0.21, -0.38, -0.02, -0.33, -0.05]\n Course: [0.0, 0.0, -0.04, 0.0, 0.0, 0.0, 0.28]\nWhat is the action of ego car?\nAssistant: The car slows steadily\n\nHuman: Why does the ego car doing this?\nAssistant: because the vehicle in front slows.\n\n",
+    "BDDX_Processed/video/02065c11-194478e8_00601.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.14, 5.86, 6.52, 6.84, 6.78, 6.8, 6.71]\n Curvature: [-0.03, 0.0, 0.01, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [1.02, 0.9, 0.58, 0.22, -0.04, -0.05, -0.41]\n Course: [0.0, 0.16, -0.1, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car speeds up slightly but maintains an slow speed\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a bump.\n\n",
+    "BDDX_Processed/video/1da45647-4d28c085_18677.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.36, 0.36, 0.36, 0.35, 0.33, 0.33, 0.26]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, -0.01, -0.02, -0.02, -0.05, -0.13]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: because it is at a red light.\n\n",
+    "BDDX_Processed/video/1edd9c50-bc27fa60_19270.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.77, 1.04, 0.22, 0.03, 0.0]\n Curvature: [-0.27, -0.27, 0.37, 0.55, -0.2, -0.2, -0.2]\n Acceleration: [0.0, 0.55, 0.4, -0.16, -0.66, -0.2, 0.05]\n Course: [0.0, 0.56, -0.35, 1.12, 0.56, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The camera is moved out of view\n\nHuman: Why does the ego car doing this?\nAssistant: for unknown reasons.\n\n",
+    "BDDX_Processed/video/06d501fd-94c914c9_04234.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.44, 8.48, 9.93, 11.01, 11.82, 12.32, 12.01]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.23, 1.04, 0.76, 0.54, 0.43, 0.05, -0.26]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: since the turn is finished\n\n",
+    "BDDX_Processed/video/160f6a0b-85d4230f_14899.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.4, 8.65, 6.44, 3.92, 1.46, 0.0, 0.0]\n Curvature: [-0.06, -0.06, -0.05, -0.03, -0.03, -0.03, -0.03]\n Acceleration: [-2.08, -2.9, -3.51, -3.79, -2.62, -1.41, -0.24]\n Course: [0.0, 1.12, 1.05, 0.84, 0.35, 0.05, 0.06]\nWhat is the action of ego car?\nAssistant: The car comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because it has reached the intersection\n\n",
+    "BDDX_Processed/video/11e3b091-665cd768_12718.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.06]\n Curvature: [-1.43, -1.43, -1.43, -1.43, -1.43, -1.43, -1.18]\n Acceleration: [-0.2, -0.13, -0.05, 0.01, 0.05, 0.08, 0.11]\n Course: [0.0, 0.1, 0.1, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/081a0da6-a7cdac9c_07447.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.39, 3.34, 5.07, 6.49, 7.29, 7.32, 7.63]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.29, 1.12, 0.85, 0.82, 0.12, 0.18, 0.14]\n Course: [0.0, 0.0, 0.0, 0.26, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the vehicle in front of it accelerates.\n\n",
+    "BDDX_Processed/video/0be60f15-e346bd2c_09602.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 1.64, 4.05, 6.43, 8.5, 10.36, 11.87]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.01, -0.01, -0.0]\n Acceleration: [0.91, 2.12, 2.59, 2.46, 2.19, 1.86, 1.52]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.32, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as the light has turned green.\n\n",
+    "BDDX_Processed/video/1c71863d-d02c9321_18106.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.9, 1.95, 2.64, 1.77, 1.01, 0.88, 0.44]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [-1.04, 0.79, -0.19, -0.36, -0.23, -0.46, 0.3]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car enters a gas station parking lot and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as there is an open area to park.\n\n",
+    "BDDX_Processed/video/17fb67eb-ec0ce6f2_16003.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.77, 2.48, 3.18, 3.71, 4.17, 4.42, 4.66]\n Curvature: [0.0, -0.01, -0.06, -0.33, -0.53, -0.33, -0.15]\n Acceleration: [1.34, 1.26, 1.16, 0.94, 0.73, 0.57, 0.4]\n Course: [0.0, 0.0, -20.19, -40.38, -7.3, 5.59, 1.4]\nWhat is the action of ego car?\nAssistant: The car is turning to the right\n\nHuman: Why does the ego car doing this?\nAssistant: to enter another road.\n\n",
+    "BDDX_Processed/video/15f559a7-933465c8_14849.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.52, 0.58, 0.63, 0.44, 0.28, 0.35, 0.42]\n Curvature: [-0.11, -0.09, -0.08, -0.06, -0.05, -0.04, -0.03]\n Acceleration: [0.12, -0.0, -0.12, -0.12, -0.1, 0.01, 0.08]\n Course: [0.0, 0.0, 0.01, 0.04, 0.02, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: because it is pulling up behind another car.\n\n",
+    "BDDX_Processed/video/0104eb19-718b1a9c_04635.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.65, 3.92, 3.23, 2.21, 1.3, 0.56, 0.03]\n Curvature: [-0.03, -0.01, 0.01, 0.08, 0.15, 0.15, 0.24]\n Acceleration: [-3.53, -2.52, -1.63, -1.64, -1.57, -1.0, -0.48]\n Course: [0.0, -0.05, -0.02, 0.05, 0.01, 0.0, -0.05]\nWhat is the action of ego car?\nAssistant: The car starts to slow down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic in front of it is slowing down\n\n",
+    "BDDX_Processed/video/1746ef96-0801aa07_15671.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.87, 9.02, 9.15, 10.35, 8.76, 7.39, 8.06]\n Curvature: [0.0, -0.0, 0.0, 0.0, 0.0, -0.02, 0.01]\n Acceleration: [1.34, 0.42, 0.43, -0.12, -1.05, 0.22, 0.14]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the road ahead is clear.\n\n",
+    "BDDX_Processed/video/00be7020-457a6db4_04556.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.97, 5.07, 5.08, 5.0, 4.98, 5.09, 4.92]\n Curvature: [0.04, -0.0, 0.01, -0.01, -0.0, 0.01, -0.0]\n Acceleration: [0.58, 0.35, -0.14, 0.0, 0.03, 0.04, -0.29]\n Course: [0.0, -1.23, -0.34, 0.35, 0.0, 0.0, -0.53]\nWhat is the action of ego car?\nAssistant: The car drives slowly through a parking lot\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear of traffic.\n\n",
+    "BDDX_Processed/video/04f64678-c012d1e3_06135.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.07, 0.93, 0.79, 0.64, 0.52, 0.71, 0.9]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-1.35, -0.98, -0.61, -0.24, 0.08, 0.19, 0.29]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving slowly through the intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because a pedestrian is crossing the road ahead\n\n",
+    "BDDX_Processed/video/0b0b8474-ef07f89f_09163.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.4, 9.38, 8.1, 10.25, 12.29, 13.38, 13.83]\n Curvature: [-0.01, 0.01, -0.01, 0.01, 0.0, -0.0, 0.01]\n Acceleration: [0.83, -1.04, 0.45, 1.67, 1.08, 0.5, -0.46]\n Course: [0.0, -0.13, -0.39, -0.13, -0.47, 0.0, -0.39]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no other cars in its lane nearby.\n\n",
+    "BDDX_Processed/video/04056725-14e1b11d_01913.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.37, 6.4, 6.44, 6.48, 6.51, 6.48, 6.44]\n Curvature: [0.15, 0.05, -0.06, -0.15, -0.24, -0.18, -0.11]\n Acceleration: [-0.36, -0.27, -0.17, -0.08, 0.0, -0.04, -0.08]\n Course: [0.0, 0.26, 0.52, 0.26, 0.26, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: as there is a car in front of it moving more slowly.\n\n",
+    "BDDX_Processed/video/224a4127-992c5d2d_20969.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.54, 6.54, 6.54, 6.62, 6.85, 7.06, 7.29]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.12, 0.03, 0.19, 0.33, 0.44, 0.54, 0.52]\n Course: [0.0, 0.0, 0.0, 0.02, 0.03, 0.02, -0.09]\nWhat is the action of ego car?\nAssistant: The car switches to the right hand lane\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is empty.\n\n",
+    "BDDX_Processed/video/022ff54c-3cb58ec1_01049.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.4, 8.2, 7.72, 6.81, 5.52, 4.08, 2.47]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.2, -0.61, -1.07, -1.65, -2.05, -2.02, -1.29]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows quickly and veers left\n\nHuman: Why does the ego car doing this?\nAssistant: to pass a car turning left.\n\n",
+    "BDDX_Processed/video/1c5ec9cb-9665857f_18041.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked on the right side of the street.\n\n",
+    "BDDX_Processed/video/18c4d009-1f58e210_16345.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.27, 6.87, 7.91, 8.81, 8.58, 8.34, 8.93]\n Curvature: [-0.0, -0.03, -0.02, -0.01, 0.01, -0.02, -0.0]\n Acceleration: [0.48, 0.57, 0.87, 0.17, -0.23, 0.09, 0.63]\n Course: [0.0, 0.57, 0.31, 1.36, 0.85, 0.45, -0.2]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as road conditions are good and traffic is moving normally.\n\n",
+    "BDDX_Processed/video/1f4b8e7d-82f7e836_19480.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.34, 0.26, 0.18, 0.08, 0.0, 0.0, 0.0]\n Curvature: [0.02, 0.15, 0.27, 0.31, 0.35, 0.51, 0.65]\n Acceleration: [-1.96, -1.01, -0.17, -0.13, -0.09, -0.04, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/01306b58-a09ba743_03068.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.43, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: The car has arrived at an intersection with a red light.\n\n",
+    "BDDX_Processed/video/147a3073-a093381f_13964.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.45, 1.05, 1.19, 1.11, 2.6, 3.99, 4.43]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.14, 0.27, 0.03, 0.25, 0.64, 0.43, 0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car gradually accelerates down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic begins to move forward.\n\n",
+    "BDDX_Processed/video/05acab14-b78b76c7_01331.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.25, 0.46, 0.48, 0.44, 0.16, 0.0, 0.0]\n Curvature: [-1.19, 1.77, 2.84, 2.93, 2.9, 2.89, 2.89]\n Acceleration: [0.16, 0.12, 0.0, -0.18, -0.22, -0.1, 0.04]\n Course: [0.0, 0.08, -0.08, 0.0, 0.0, 0.0, 0.08]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped.\n\n",
+    "BDDX_Processed/video/04076a65-79b349d4_05818.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.39, 12.19, 14.24, 15.44, 15.77, 15.59, 15.4]\n Curvature: [0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.03]\n Acceleration: [2.2, 1.14, 0.86, 0.39, -0.02, -0.12, -0.07]\n Course: [0.0, 0.0, -0.38, 0.0, 0.0, 0.0, -1.87]\nWhat is the action of ego car?\nAssistant: The car is driving at normal speed\n\nHuman: Why does the ego car doing this?\nAssistant: as there is no traffic.\n\n",
+    "BDDX_Processed/video/04c6b8fa-4abd5e9f_06088.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.8, 14.29, 15.12, 16.03, 16.69, 16.47, 15.78]\n Curvature: [0.0, -0.0, 0.0, -0.01, -0.0, 0.01, 0.0]\n Acceleration: [0.71, 0.66, 0.42, 0.47, 0.08, -0.23, -0.41]\n Course: [0.0, -0.17, 0.0, 0.0, 0.17, 0.0, 0.17]\nWhat is the action of ego car?\nAssistant: The car follows the road in the left hand lane\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no traffic around them.\n\n",
+    "BDDX_Processed/video/0ba933b2-3308d259_09520.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.02, 0.19, 0.4]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.01, 0.09, 0.19, 0.34, 0.51]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: at the red light.\n\n",
+    "BDDX_Processed/video/00825605-9dcec7d0_04502.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [21.16, 20.61, 20.11, 20.75, 19.54, 19.73, 18.98]\n Curvature: [0.0, -0.01, -0.01, -0.01, -0.0, -0.0, 0.0]\n Acceleration: [-0.3, -0.35, 0.51, -0.24, -0.51, 0.17, -1.73]\n Course: [0.0, 0.89, -0.12, 1.03, -0.37, 0.0, -0.14]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped in front.\n\n",
+    "BDDX_Processed/video/1eee11d8-2a7a5c07_19278.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.92, 1.37, 2.12, 3.02, 4.04, 4.94, 5.75]\n Curvature: [0.16, 0.21, 0.12, 0.07, 0.16, 0.15, 0.07]\n Acceleration: [0.27, 0.71, 1.04, 1.21, 1.21, 1.1, 0.96]\n Course: [0.0, -0.79, -2.67, -3.81, 30.94, 55.37, -3.59]\nWhat is the action of ego car?\nAssistant: The car accelerates as it turns left\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear to turn.\n\n",
+    "BDDX_Processed/video/0277379d-ed078767_01857.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.29, 10.58, 11.52, 12.33, 13.1, 13.76, 14.28]\n Curvature: [0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.62, 1.26, 0.99, 0.88, 0.79, 0.65, 0.41]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.13, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates to a constant speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/0d606092-6cb1ae91_10524.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.31, 2.32, 3.4, 4.39, 5.38, 5.83, 6.38]\n Curvature: [0.01, 0.02, 0.03, 0.13, 0.21, 0.16, 0.11]\n Acceleration: [1.66, 1.84, 1.99, 1.71, 1.43, 1.34, 1.28]\n Course: [0.0, -0.84, -1.51, -2.0, -3.09, -2.64, -2.46]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's making a left turn.\n\n",
+    "BDDX_Processed/video/099d8913-48b371ce_08257.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.51, 1.24, 0.97, 0.69, 0.47, 0.35, 0.22]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.81, -1.53, -1.25, -0.97, -0.74, -0.61, -0.48]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic in front of it is stopped at the red light.\n\n",
+    "BDDX_Processed/video/1543dc40-fbd4be55_14376.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.17]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car has stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped.\n\n",
+    "BDDX_Processed/video/1cbc652a-1e2ab11e_18192.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.3, 6.8, 8.24, 9.58, 10.63, 10.21, 8.37]\n Curvature: [-0.03, -0.05, -0.05, -0.02, 0.02, 0.0, -0.0]\n Acceleration: [1.74, 1.85, 1.79, 1.33, 0.39, -1.35, -2.6]\n Course: [0.0, 0.54, 2.04, 0.34, 0.1, -0.21, -0.54]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's pulling around the car in front of it.\n\n",
+    "BDDX_Processed/video/198d8f5e-dd29fbb8_16761.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.47, 4.81, 4.15, 3.5, 2.92, 2.46, 2.01]\n Curvature: [-0.52, -0.35, -0.17, 0.01, 0.17, 0.01, -0.15]\n Acceleration: [-1.86, -1.93, -2.0, -2.07, -2.13, -1.8, -1.47]\n Course: [0.0, 0.18, 0.18, 0.18, 0.09, -0.54, -0.54]\nWhat is the action of ego car?\nAssistant: The car almost comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: while at the stop sign\n\n",
+    "BDDX_Processed/video/17d4f1b8-3e3b9bae_15892.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.5, 6.16, 4.1, 2.63, 1.69, 0.84, 0.15]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.75, -2.92, -2.67, -1.93, -1.5, -1.08, -0.64]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: since the light ahead turns red.\n\n",
+    "BDDX_Processed/video/1c804640-adb884fc_18128.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.87, 18.97, 19.2, 19.54, 19.82, 20.12, 20.45]\n Curvature: [-0.0, -0.02, -0.02, -0.01, -0.05, -0.01, 0.0]\n Acceleration: [0.08, 0.16, 0.28, 0.3, 0.28, 0.31, 0.25]\n Course: [0.0, 0.5, 0.63, 0.62, 1.33, 0.53, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/0814c9ab-a1ba9412_07432.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.35, 6.35, 6.35, 6.34, 6.32, 6.12, 5.95]\n Curvature: [0.03, -0.0, -0.03, -0.07, -0.09, -0.05, -0.01]\n Acceleration: [0.39, 0.19, 0.01, -0.19, -0.38, -0.47, -0.55]\n Course: [0.0, -0.12, -0.06, -0.12, 0.01, 0.14, 0.07]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: as there is a slower vehicle ahead.\n\n",
+    "BDDX_Processed/video/021c4a16-1c50e818_04993.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.02, 2.55, 2.28, 2.89, 3.84, 3.96, 3.03]\n Curvature: [0.0, 0.0, -0.02, -0.19, -0.45, -0.07, -0.0]\n Acceleration: [-1.84, -1.04, 0.12, 0.83, 0.64, -0.45, -0.81]\n Course: [0.0, 0.0, 0.0, 0.0, 5.99, 9.98, 0.0]\nWhat is the action of ego car?\nAssistant: The car turns right and then immediately turns left\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is seen and the car can safely move onward.\n\n",
+    "BDDX_Processed/video/1d658258-c9a58636_18551.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.19, 1.97, 3.53, 4.76, 6.14, 7.64, 8.46]\n Curvature: [0.12, 0.01, -0.1, 0.02, 0.04, -0.05, -0.02]\n Acceleration: [-0.02, 1.01, 1.22, 1.09, 1.3, 0.97, 0.15]\n Course: [0.0, 0.0, -0.44, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates again\n\nHuman: Why does the ego car doing this?\nAssistant: as it moves with the flow of traffic\n\n",
+    "BDDX_Processed/video/06d1c4fe-4f416689_06733.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.32, 0.23, 0.16, 0.07, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.32, -0.28, -0.24, -0.2, -0.15, -0.11, -0.07]\n Course: [0.0, 6.09, 3.04, 6.09, 3.04, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car backs up a bit\n\nHuman: Why does the ego car doing this?\nAssistant: because the road conditions are difficult.\n\n",
+    "BDDX_Processed/video/1c05b0a9-538b4e46_17908.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.13, 4.35, 5.72, 5.89, 5.98, 6.37, 6.72]\n Curvature: [-0.09, 0.05, 1.07, 0.28, -0.12, -0.81, -0.06]\n Acceleration: [1.38, 1.63, 0.66, 0.08, 0.22, 0.33, 0.56]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/0c5ff0c6-b1853637_09805.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.86, 19.41, 19.02, 18.57, 18.13, 17.76, 17.43]\n Curvature: [-0.04, -0.04, -0.04, -0.05, -0.05, -0.05, -0.06]\n Acceleration: [-1.4, -1.44, -1.47, -1.51, -1.54, -1.46, -1.39]\n Course: [0.0, 0.23, 0.12, 0.23, 0.33, 0.42, 0.21]\nWhat is the action of ego car?\nAssistant: The car stays to the right\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may make a right turn ahead.\n\n",
+    "BDDX_Processed/video/0919d0a6-27280b18_08031.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.48, 1.07, 0.72, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.12, 0.08, -0.34, -0.19, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car has stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red\n\n",
+    "BDDX_Processed/video/05584a52-687411b6_00491.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.59, 0.14, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.01, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-2.98, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: The light controlling the intersection is red.\n\n",
+    "BDDX_Processed/video/030003dd-480d0868_03697.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.9, 11.63, 10.02, 8.1, 6.34, 5.13, 4.79]\n Curvature: [0.0, 0.01, 0.06, 0.16, 0.23, 0.47, 1.14]\n Acceleration: [-1.43, -2.12, -2.62, -2.77, -2.09, -1.35, -0.73]\n Course: [0.0, 0.0, -0.1, -0.08, 0.0, -12.81, -16.01]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: to make a u-turn.\n\n",
+    "BDDX_Processed/video/0723c4dd-fe5bd292_01635.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.29, 0.58, 1.18, 2.08, 2.7, 3.08, 2.99]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.38, 0.73, 1.01, 1.16, 0.74, 0.23, -0.22]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates and slowly moves left\n\nHuman: Why does the ego car doing this?\nAssistant: because it needs to pass a taxi that is stopped ahead.\n\n",
+    "BDDX_Processed/video/1c05b0a9-538b4e46_17906.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.43, 3.66, 4.86, 5.85, 6.39, 6.59, 6.2]\n Curvature: [0.36, -0.17, -0.11, 0.49, 0.27, -0.06, -0.03]\n Acceleration: [1.14, 1.31, 1.2, 0.85, 0.42, -0.14, -0.69]\n Course: [0.0, 0.0, 0.0, 0.0, 0.35, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/02d478d1-9774e131_03168.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.3, 12.97, 12.64, 12.32, 12.01, 11.53, 11.04]\n Curvature: [-0.0, -0.0, -0.0, -0.01, -0.01, -0.0, -0.0]\n Acceleration: [-1.14, -1.25, -1.36, -1.47, -1.56, -1.64, -1.73]\n Course: [0.0, 0.05, 0.05, 0.05, 0.01, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/13c94581-e10b6cc2_13671.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.95, 6.01, 6.1, 6.5, 6.93, 7.47, 7.91]\n Curvature: [0.01, 0.01, 0.01, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.19, 0.33, 0.47, 0.7, 0.89, 0.76, 0.63]\n Course: [0.0, 0.0, -0.04, -0.15, -0.07, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates more\n\nHuman: Why does the ego car doing this?\nAssistant: as to keep up with the flowing traffic.\n\n",
+    "BDDX_Processed/video/13b306d7-5d25a761_13633.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.1, 12.48, 11.93, 11.31, 10.66, 9.83, 9.11]\n Curvature: [0.02, 0.0, -0.01, -0.03, -0.05, -0.04, -0.03]\n Acceleration: [-2.49, -2.55, -2.6, -2.66, -2.72, -2.75, -2.78]\n Course: [0.0, -0.02, -0.01, -0.02, 0.0, 0.02, 0.01]\nWhat is the action of ego car?\nAssistant: The car brakes\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a stop sign.\n\n",
+    "BDDX_Processed/video/10f60db0-ee6d9de6_12146.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.68, 12.72, 12.76, 12.73, 12.59, 12.44, 12.41]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.45, 0.22, 0.01, -0.14, -0.18, -0.21, -0.17]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.02]\nWhat is the action of ego car?\nAssistant: The car continues forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no cars in its way.\n\n",
+    "BDDX_Processed/video/07380221-064f762b_01850.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.63, 6.83, 7.48, 7.8, 8.12, 8.56, 9.3]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.12, 0.42, 0.48, 0.3, 0.36, 0.58, 0.49]\n Course: [0.0, 0.0, 0.0, 0.0, -0.03, -0.02, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues to drive down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic is flowing smoothly.\n\n",
+    "BDDX_Processed/video/1cc4238c-7b557288_18221.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.88, 5.41, 5.07, 4.96, 4.73, 4.59, 4.66]\n Curvature: [0.0, -0.09, -0.26, -0.51, -0.25, -0.03, -0.06]\n Acceleration: [-0.97, -0.66, -0.42, -0.29, -0.2, -0.08, 0.12]\n Course: [0.0, 0.0, 8.73, 6.99, 3.34, 1.64, 2.05]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it turns to the right.\n\n",
+    "BDDX_Processed/video/1f156136-534f9c01_19360.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.36, 4.88, 4.5, 3.93, 4.34, 5.9, 5.61]\n Curvature: [0.0, 0.01, 0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.95, -0.32, -0.41, -0.1, 0.77, 0.33, -0.21]\n Course: [0.0, 0.0, -0.51, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car returns to a slower pace\n\nHuman: Why does the ego car doing this?\nAssistant: because there is more congestion going through the lights up ahead.\n\n",
+    "BDDX_Processed/video/1a809d9d-913c8a79_17134.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.57, 0.28, 0.27, 0.01, 0.0, 0.0, 0.27]\n Curvature: [2.93, 1.65, -0.79, -1.0, -1.0, -1.0, -1.01]\n Acceleration: [-1.81, -0.05, 0.02, -0.13, 0.0, 0.07, -0.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/04c6b8fa-d242db71_06098.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [1.91, 4.31, 4.33, 4.35, 4.36, 4.38, 4.4]\n Acceleration: [-0.24, -0.09, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.13, 0.0, 0.0, 0.19, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red and there are people in the crosswalk.\n\n",
+    "BDDX_Processed/video/019c8305-0bb153d0_04850.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.22, 1.96, 3.32, 5.48, 6.18, 6.52, 6.82]\n Curvature: [-0.0, -0.0, -0.0, 0.0, -0.0, 0.01, 0.03]\n Acceleration: [0.35, 0.67, 1.37, 0.82, 0.27, 0.27, 0.03]\n Course: [0.0, 0.28, 0.0, 0.0, 0.0, 0.0, -0.78]\nWhat is the action of ego car?\nAssistant: The car moves forward then turns left\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/1875b775-41d60a31_16198.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.17, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as it is stopped at a red light.\n\n",
+    "BDDX_Processed/video/09fe84f2-5f2228d2_08631.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.32, 7.42, 7.51, 7.6, 7.73, 8.05, 8.34]\n Curvature: [-0.02, -0.01, -0.01, -0.01, -0.0, -0.0, -0.0]\n Acceleration: [-0.27, 0.01, 0.26, 0.54, 0.8, 0.84, 0.88]\n Course: [0.0, 0.59, 0.29, 0.59, 0.34, 0.09, 0.05]\nWhat is the action of ego car?\nAssistant: The car is merging into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it has passed the cars parked to its right and there is no traffic in the right lane.\n\n",
+    "BDDX_Processed/video/1be932d6-b54812e2_17856.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.31, 3.12, 6.13, 8.53, 9.83, 10.38, 10.71]\n Curvature: [-0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.27, 0.99, 1.16, 0.92, 0.5, 0.02, 0.36]\n Course: [0.0, 0.0, 0.0, -0.06, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is now clear\n\n",
+    "BDDX_Processed/video/08e3c891-51d3dce6_07933.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: while the light is red.\n\n",
+    "BDDX_Processed/video/0817823b-8a8657e7_07438.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [20.75, 20.98, 21.57, 21.6, 21.35, 21.08, 20.21]\n Curvature: [0.04, 0.01, 0.0, -0.0, 0.01, -0.03, -0.02]\n Acceleration: [0.75, 0.26, 0.34, -0.22, -0.14, -0.45, -0.61]\n Course: [0.0, -0.23, 0.0, 0.0, 0.26, 0.78, 1.27]\nWhat is the action of ego car?\nAssistant: The car goes down the highway at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a regular speed.\n\n",
+    "BDDX_Processed/video/20652cf8-657281a8_20054.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.63, 7.46, 7.8, 7.82, 7.21, 6.09, 6.15]\n Curvature: [0.0, 0.0, -0.0, 0.0, 0.0, -0.01, -0.0]\n Acceleration: [-0.15, 0.12, 0.03, -0.03, -0.54, -0.2, 0.03]\n Course: [0.0, -0.05, 0.0, 0.0, 0.0, 0.0, 0.27]\nWhat is the action of ego car?\nAssistant: The car drives slowly down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is narrowed from snow on the sides and there is oncoming traffic.\n\n",
+    "BDDX_Processed/video/13d47839-d6add655_13677.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.33, 0.33, 0.32, 0.32, 0.31, 0.23, 0.14]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.5, -0.41, -0.32, -0.23, -0.17, -0.21, -0.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car again stops\n\nHuman: Why does the ego car doing this?\nAssistant: since the car in front of it isn't moving\n\n",
+    "BDDX_Processed/video/05cb8e1c-e064ca63_01505.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.55, 10.03, 9.34, 6.14, 8.61, 11.77, 9.94]\n Curvature: [-0.0, -0.0, 0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [1.06, 1.34, -1.73, -0.23, 1.86, 0.44, -1.87]\n Course: [0.0, 0.0, 0.0, 0.0, -0.23, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/11577eed-53fb5e4d_12410.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because there's a traffic jam in front of it.\n\n",
+    "BDDX_Processed/video/145785ea-c78220bd_13889.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has been red\n\n",
+    "BDDX_Processed/video/037795d6-d456ac0c_00469.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.35, 0.15, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.2, -0.16, -0.06, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/097450c5-3e2b46e6_08185.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.27, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-1.56, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car remains at a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light remains red.\n\n",
+    "BDDX_Processed/video/06ff7d61-e2930879_00707.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.51, 15.81, -1.0, -1.0, -1.0, -0.27, 3.0]\n Curvature: [-0.01, -0.0, 0.02, 0.0, 0.0, -0.02, 0.05]\n Acceleration: [-2.27, -9.72, -8.12, 0.0, 0.29, 2.03, 0.76]\n Course: [0.0, -6.02, -35.64, 0.0, 0.0, 29.23, 11.62]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0501cda3-6d714e69_06162.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.55, 10.92, 10.41, 10.34, 10.55, 11.1, 11.16]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.04, -0.03, -0.06]\n Acceleration: [-0.05, -0.56, -0.28, 0.06, 0.37, 0.31, -0.74]\n Course: [0.0, 0.0, 0.0, 0.0, 1.09, 1.96, 0.23]\nWhat is the action of ego car?\nAssistant: The car is heading down the street at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is empty.\n\n",
+    "BDDX_Processed/video/1accee17-cee1c2a4_17425.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.21, 0.22, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.0, -0.07, -0.04, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/1fb97fbd-62f3c4c8_19825.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.56, 12.57, 13.17, 13.57, 13.84, 13.9, 12.25]\n Curvature: [0.0, -0.01, -0.02, -0.0, 0.0, 0.0, -0.01]\n Acceleration: [-0.28, 0.45, -0.23, 0.28, 0.06, 0.04, -1.65]\n Course: [0.0, -0.03, 0.57, 0.0, -0.45, -0.22, -0.11]\nWhat is the action of ego car?\nAssistant: The car drives normally\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no traffic on the street.\n\n",
+    "BDDX_Processed/video/05e5b952-73bfedb3_01663.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.54, 10.19, 10.58, 10.02, 10.64, 10.32, 10.41]\n Curvature: [0.0, -0.02, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.09, 0.6, -0.35, 0.08, 0.14, -0.13, 0.14]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/1175dfef-e68b826b_12540.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [30.42, 30.36, 30.3, 30.24, 30.18, 30.12, 30.06]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.51, -0.5, -0.49, -0.49, -0.48, -0.48, -0.47]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving left.\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is changing lanes.\n\n",
+    "BDDX_Processed/video/14df900d-b8d58747_14102.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stationary\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked on the side of the road.\n\n",
+    "BDDX_Processed/video/1e68a7dc-e3394c1e_19060.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.16, 0.24, 0.57, 1.36, 2.37, 3.46]\n Curvature: [0.1, 0.09, 0.72, 0.76, -0.14, -0.01, 0.03]\n Acceleration: [0.21, 0.15, 0.35, 0.71, 1.16, 1.32, 1.14]\n Course: [0.0, 0.0, 0.0, 21.94, 12.74, -0.7, -0.21]\nWhat is the action of ego car?\nAssistant: The car is accelerating through an intersection from a stopped position\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turned green and the intersection is clear.\n\n",
+    "BDDX_Processed/video/05584a52-590d4e59_06269.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.24, 4.33, 4.43, 4.68, 5.27, 5.8, 6.17]\n Curvature: [0.0, -0.03, -0.06, -0.12, -0.27, -0.39, -0.35]\n Acceleration: [-1.47, -0.62, 0.31, 0.89, 0.98, 1.06, 1.08]\n Course: [0.0, 0.0, 0.0, 3.04, 4.55, 3.04, 4.53]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning to the right.\n\n",
+    "BDDX_Processed/video/0ddc430e-27170bbd_10777.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.27, 3.3, 3.34, 3.41, 3.56, 3.69, 4.27]\n Curvature: [0.31, 0.14, -0.02, -0.12, -0.12, -0.12, -0.08]\n Acceleration: [0.44, 0.36, 0.27, 0.34, 0.67, 0.98, 1.23]\n Course: [0.0, -0.31, -0.31, -0.07, -0.09, -0.07, 0.1]\nWhat is the action of ego car?\nAssistant: The car is drifting to the left\n\nHuman: Why does the ego car doing this?\nAssistant: Because the car is switching lanes from right to left to go around traffic that is jammed in its lane and to the right of it.\n\n",
+    "BDDX_Processed/video/14df900d-d216db1b_14113.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.18, 0.05, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.76, -0.26, -0.04, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car has stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front of him has stopped.\n\n",
+    "BDDX_Processed/video/04904d8d-0ab5c90a_06063.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.04, 4.88, 5.66, 5.54, 5.46, 5.58, 5.78]\n Curvature: [-0.59, -0.31, -0.04, -0.02, 0.04, 0.3, 0.48]\n Acceleration: [0.94, 0.81, 0.68, 0.31, 0.05, 0.37, 0.69]\n Course: [0.0, 3.04, 1.52, 0.0, 0.0, 0.0, -8.96]\nWhat is the action of ego car?\nAssistant: The car makes a left turn\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear of traffic.\n\n",
+    "BDDX_Processed/video/1d97c075-dac6540f_18648.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.6, 7.54, 6.54, 4.59, 2.93, 3.32, 3.83]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.53, -2.7, -2.86, -2.11, -1.27, 0.28, 1.47]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the driver is looking for his turn off.\n\n",
+    "BDDX_Processed/video/02d478d1-9774e131_03166.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.82, 15.18, 13.67, 12.35, 11.54, 10.67, 10.49]\n Curvature: [-0.01, -0.01, -0.0, -0.0, -0.01, -0.01, -0.01]\n Acceleration: [-3.01, -3.07, -3.12, -2.82, -2.08, -1.28, -0.57]\n Course: [0.0, 0.04, 0.04, 0.11, 0.07, 0.11, 0.07]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: to roll through a stop sign.\n\n",
+    "BDDX_Processed/video/110dbf4c-2909b41d_12237.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.07, 3.51, 4.41, 4.9, 4.61, 5.58, 5.93]\n Curvature: [0.0, 0.01, 0.0, -0.02, -0.01, 0.02, -0.02]\n Acceleration: [-0.28, 0.02, 0.63, -0.23, 0.24, 0.02, -0.54]\n Course: [0.0, 0.41, -0.5, 0.0, -0.33, 0.45, 1.45]\nWhat is the action of ego car?\nAssistant: The car drives slowly down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in the lane ahead is moving slowly but steadily.\n\n",
+    "BDDX_Processed/video/1f4c7852-72686adc_19524.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.26]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.01, 0.02, 0.02]\n Acceleration: [-0.69, 0.0, 0.0, 0.0, 0.0, 0.0, 0.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is waiting\n\nHuman: Why does the ego car doing this?\nAssistant: because the light hasn't changed\n\n",
+    "BDDX_Processed/video/14306a74-50cdd915_13836.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is red.\n\n",
+    "BDDX_Processed/video/0723c4dd-6d50a276_07035.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.59, 6.86, 7.13, 7.4, 7.64, 7.67, 7.71]\n Curvature: [-0.04, -0.03, -0.02, -0.01, -0.01, -0.02, -0.04]\n Acceleration: [0.92, 0.84, 0.75, 0.66, 0.54, 0.2, -0.15]\n Course: [0.0, 0.0, 0.0, 0.0, 0.2, 0.41, 0.41]\nWhat is the action of ego car?\nAssistant: The car is merging into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it has fewer cars in it.\n\n",
+    "BDDX_Processed/video/1c522a69-00786fea_18015.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [1.06, -0.34, 0.24, 0.19, 0.12, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, -2.84, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is stopped at an intersection\n\n",
+    "BDDX_Processed/video/1e68a7dc-e3394c1e_19061.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.62, 5.29, 6.28, 6.99, 7.24, 7.37, 7.69]\n Curvature: [0.02, 0.03, -0.05, -0.03, 0.14, -0.05, -0.03]\n Acceleration: [0.78, 1.05, 0.98, 0.67, 0.26, 0.3, -0.07]\n Course: [0.0, 0.06, -0.42, 0.28, -0.04, -0.35, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives at a reasonable speed down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no traffic in the way.\n\n",
+    "BDDX_Processed/video/1de119a0-f155a957_18812.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.08, 4.18, 4.27, 4.37, 4.46, 4.46, 4.46]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.95, 0.74, 0.57, 0.37, 0.19, 0.42, 0.66]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is rolling over to the right and upside down\n\nHuman: Why does the ego car doing this?\nAssistant: because it was hit broadside by an unseen vehicle coming from the left\n\n",
+    "BDDX_Processed/video/156dcd52-d023f184_14535.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.78, 10.9, 6.44, 1.57, 0.32, 0.23, 0.18]\n Curvature: [0.0, -0.01, -0.02, -0.75, -0.82, 0.35, 0.48]\n Acceleration: [-0.27, -2.11, -3.28, -2.05, -0.14, -0.07, 0.01]\n Course: [0.0, 0.9, 0.0, 0.0, 2.63, 0.41, 0.28]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: while nearing the red light\n\n",
+    "BDDX_Processed/video/1dcbf95d-e384c4ed_18744.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.57, 18.62, 18.65, 18.7, 18.75, 18.81, 18.86]\n Curvature: [-0.01, -0.01, -0.02, -0.02, -0.02, -0.02, -0.03]\n Acceleration: [0.35, 0.31, 0.27, 0.23, 0.2, 0.2, 0.2]\n Course: [0.0, 0.2, 0.1, 0.2, 0.27, 0.34, 0.17]\nWhat is the action of ego car?\nAssistant: The car pulls into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it wants to pass the truck.\n\n",
+    "BDDX_Processed/video/0725b3e1-9c9078cb_01715.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.56, 1.5, 1.14, 0.04, 0.0, 0.15, 0.0]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-3.16, 0.0, -0.65, -0.1, 0.0, 0.01, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car has stopped\n\nHuman: Why does the ego car doing this?\nAssistant: The light controlling the intersection has turned red.\n\n",
+    "BDDX_Processed/video/0bf8851e-c43ef9d2_09642.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.05, 7.73, 8.45, 8.8, 9.13, 9.82, 10.14]\n Curvature: [-0.05, -0.0, 0.01, 0.01, -0.0, 0.0, 0.01]\n Acceleration: [0.26, 0.33, 0.42, 0.03, 0.31, 0.3, 0.18]\n Course: [0.0, 0.17, 0.34, 0.17, 0.17, -0.34, -0.17]\nWhat is the action of ego car?\nAssistant: The car proceeds down the street at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is empty.\n\n",
+    "BDDX_Processed/video/13c13518-96f102e2_13650.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.92, 10.9, 10.88, 10.87, 10.85, 10.5, 10.16]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.94, 0.52, 0.1, -0.32, -0.68, -0.72, -0.75]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car swerves to the right\n\nHuman: Why does the ego car doing this?\nAssistant: while driving down the road\n\n",
+    "BDDX_Processed/video/07f87c9c-8a8d81ef_03808.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.46, 5.69, 4.99, 4.37, 3.97, 3.57, 2.86]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.91, -1.73, -1.57, -1.48, -1.51, -1.55, -1.43]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: The car has come to an intersection and the light is red.\n\n",
+    "BDDX_Processed/video/0933f463-e65d58c7_08100.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.98, 5.74, 4.49, 3.69, 3.35, 2.99, 2.81]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.42, -2.26, -2.1, -1.76, -1.27, -0.74, -0.55]\n Course: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\nWhat is the action of ego car?\nAssistant: the car brakes\n\nHuman: Why does the ego car doing this?\nAssistant: as the car ahead is braking.\n\n",
+    "BDDX_Processed/video/147a04c9-e958476e_13960.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.72, 0.92, 1.66, 1.09, 1.15, 1.86, 1.7]\n Curvature: [0.02, 6.8, 2.74, 0.07, 0.03, 0.16, 0.09]\n Acceleration: [-0.44, 0.51, -0.0, -0.28, 0.35, 0.27, -0.11]\n Course: [0.0, -0.12, -0.32, -0.23, -0.06, 0.0, -0.11]\nWhat is the action of ego car?\nAssistant: The car advances toward the intersection slowly and then stoops\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a pedestrian crossing the intersection.\n\n",
+    "BDDX_Processed/video/090ebc7d-4412ca52_08007.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.09, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is red.\n\n",
+    "BDDX_Processed/video/1e15eaab-f4b4838a_18911.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.45, 1.81, 1.44, 0.94, 0.73, 1.1, 1.89]\n Curvature: [-0.06, 0.06, 0.03, -0.07, -0.2, -0.17, 0.06]\n Acceleration: [0.8, 0.03, -0.43, -0.42, 0.13, 0.7, 1.03]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves slowly forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving forward.\n\n",
+    "BDDX_Processed/video/01440a35-e0b2f4a2_03177.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is not moving.\n\n",
+    "BDDX_Processed/video/0919d0a6-b6ec5595_08032.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is red and a pedestrian is crossing.\n\n",
+    "BDDX_Processed/video/0f856c83-be8b9deb_11452.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [28.05, 27.69, 27.62, 28.06, 28.45, 28.43, 28.51]\n Curvature: [0.0, 0.01, -0.0, -0.0, 0.02, 0.02, -0.0]\n Acceleration: [-0.2, 0.07, 0.06, 0.23, 0.04, 0.05, 0.02]\n Course: [0.0, -0.02, -1.46, -0.84, -0.74, -1.38, -0.93]\nWhat is the action of ego car?\nAssistant: The car is driving forward at normal speed on the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no traffic in the way and the path is clear.\n\n",
+    "BDDX_Processed/video/0478d053-f5bb4831_06025.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.95, 0.85, 0.75, 0.65, 0.53, 0.43, 0.33]\n Curvature: [0.09, 0.25, 0.41, 0.57, 0.77, 0.93, 1.09]\n Acceleration: [-1.62, -1.51, -1.41, -1.31, -1.18, -1.07, -0.97]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The cars slows down to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because it has approached a red light.\n\n",
+    "BDDX_Processed/video/08800a8a-ac06cf56_07696.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.96, 7.19, 5.56, 4.03, 2.83, 1.63, 1.39]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-3.13, -3.37, -3.58, -3.38, -2.58, -1.78, -1.37]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as the vehicle in front of it stops.\n\n",
+    "BDDX_Processed/video/0fb8db78-3f42b797_11521.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.09, 4.98, 5.19, 4.55, 3.66, 2.94, 2.54]\n Curvature: [0.0, 0.01, 0.02, 0.05, -0.07, -0.14, -0.08]\n Acceleration: [1.6, 0.71, -0.23, -1.13, -1.1, -0.94, -0.71]\n Course: [0.0, 46.88, -0.18, -0.14, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward.\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is driving to the exit.\n\n",
+    "BDDX_Processed/video/05a93853-58c63f87_01262.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.16, 2.73, 1.58, 0.69, 0.38, 0.45, 0.7]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.14, -0.99, -0.82, -0.45, -0.1, 0.15, 0.03]\n Course: [0.0, 0.0, 0.0, 0.0, -0.07, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/02fc82b3-279906a9_03633.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.56, 2.44, 1.35, 0.74, 0.2, 0.09, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.92, -1.8, -1.64, -1.14, -0.66, -0.35, -0.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slowed to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped.\n\n",
+    "BDDX_Processed/video/05e261cb-ef8cd52f_01555.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.33, 0.32, 0.31, 0.3, 0.29, 0.26, 0.24]\n Curvature: [0.98, 0.66, 0.35, 0.03, -0.3, -1.09, -1.87]\n Acceleration: [-0.19, -0.16, -0.13, -0.09, -0.06, -0.02, 0.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.01, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as it needed to stop for something.\n\n",
+    "BDDX_Processed/video/07184fbb-bb4c67d2_06997.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.19, 3.06, 3.75, 4.16, 4.6, 4.94, 5.02]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, 0.04, 0.16]\n Acceleration: [1.37, 1.14, 0.91, 0.7, 0.53, 0.41, 0.37]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then makes a left turn\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green and no traffic is coming.\n\n",
+    "BDDX_Processed/video/10d86362-67be6116_12031.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.23, -0.23, -0.23, -0.23, -0.23, -0.23, -0.23]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped.\n\n",
+    "BDDX_Processed/video/0a8aa157-cbfd9075_08858.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.21, 0.16, 0.1, 0.05, 0.0, 0.04, 0.08]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.14, -0.11, -0.08, -0.05, -0.02, 0.02, 0.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked.\n\n",
+    "BDDX_Processed/video/0b669851-19477a8c_09334.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.1, 8.23, 8.1, 7.98, 8.41, 9.09, 9.05]\n Curvature: [0.0, -0.0, 0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.29, 0.02, -0.12, 0.18, 0.58, 0.3, -0.47]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is green and traffic is moving at a normal speed.\n\n",
+    "BDDX_Processed/video/1496a55a-86e242fc_14004.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.25, 10.49, 11.14, 11.03, 10.72, 10.27, 10.23]\n Curvature: [0.0, -0.0, 0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.72, 0.42, 0.23, -0.19, -0.16, -0.22, 0.12]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/132f36a5-3e7891ae_13300.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.07, 2.77, 2.49, 2.04, 1.39, 0.74, 0.47]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.0, -1.68, -1.38, -1.18, -1.17, -1.16, -1.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/1c1ea16f-f3b251db_17959.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.47, 8.32, 8.18, 8.03, 7.88, 7.69, 7.51]\n Curvature: [-0.0, -0.0, 0.0, 0.01, 0.01, 0.01, 0.0]\n Acceleration: [-0.87, -0.81, -0.75, -0.69, -0.65, -0.67, -0.69]\n Course: [0.0, 0.09, 0.09, 0.09, -0.02, -0.14, -0.14]\nWhat is the action of ego car?\nAssistant: The car slows and moves to the right\n\nHuman: Why does the ego car doing this?\nAssistant: because a car is coming the opposite direction on the narrow road\n\n",
+    "BDDX_Processed/video/1d56a6ef-86725847_18507.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.07, 8.4, 8.72, 8.72, 8.69, 8.49, 8.19]\n Curvature: [0.02, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.79, 0.56, 0.31, 0.04, -0.25, -0.56, -0.82]\n Course: [0.0, -0.32, -0.32, 0.0, 0.0, 0.0, -0.08]\nWhat is the action of ego car?\nAssistant: The car is accelerating a little\n\nHuman: Why does the ego car doing this?\nAssistant: while heading down the road\n\n",
+    "BDDX_Processed/video/20ef5a70-30216bd9_20313.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [23.16, 22.48, 22.61, 24.29, 25.62, 25.74, 26.38]\n Curvature: [-0.0, -0.02, -0.02, 0.0, 0.01, 0.02, 0.02]\n Acceleration: [0.2, -0.06, 0.12, 0.16, 0.55, 0.01, 0.28]\n Course: [0.0, 1.77, 4.2, 0.2, -3.36, -4.04, -3.9]\nWhat is the action of ego car?\nAssistant: The car is moving down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving quickly.\n\n",
+    "BDDX_Processed/video/06e4a00e-346390c8_06779.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.42, 9.47, 9.51, 9.48, 9.34, 9.19, 8.7]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.12, 0.02, -0.07, -0.25, -0.53, -0.84, -1.24]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: as it goes down the street\n\n",
+    "BDDX_Processed/video/0a70b8c5-8c3ef863_08785.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.62, 0.41, 0.26, 0.14, 0.04, -0.2, -0.22]\n Acceleration: [-1.02, -0.21, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/02097021-05dcbf23_00664.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.33, 7.39, 7.44, 7.5, 7.46, 6.75, 6.13]\n Curvature: [0.07, 0.07, 0.07, 0.07, 0.07, 0.06, 0.04]\n Acceleration: [0.5, 0.04, -0.36, -0.82, -1.27, -1.63, -1.94]\n Course: [0.0, -0.33, -0.16, -0.33, -1.01, -1.69, -0.85]\nWhat is the action of ego car?\nAssistant: The car stops quickly\n\nHuman: Why does the ego car doing this?\nAssistant: because a car pulls out in front of it.\n\n",
+    "BDDX_Processed/video/06ff7d61-45086936_06861.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.5, 13.91, 13.27, 11.6, 10.51, 8.85, 5.52]\n Curvature: [0.0, -0.0, -0.02, 0.01, 0.01, -0.06, 0.02]\n Acceleration: [-1.51, -0.78, -0.88, -1.13, -1.01, -1.83, -2.34]\n Course: [0.0, 0.11, 0.12, 0.35, 0.18, 0.47, -0.12]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/083bc808-8474fdea_07522.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.78, 12.04, 12.31, 12.76, 13.16, 13.18, 13.16]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.49, 0.59, 0.68, 0.56, 0.42, 0.11, -0.14]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.02]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/0d1fc580-2bbf8587_10337.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.14, 19.16, 19.18, 19.2, 19.22, 19.24, 19.26]\n Curvature: [0.02, 0.01, 0.01, 0.01, 0.0, -0.02, -0.05]\n Acceleration: [0.1, 0.1, 0.09, 0.08, 0.07, 0.02, -0.02]\n Course: [0.0, -0.18, -0.09, -0.18, -0.09, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is passed by a silver vehicle two lanes over on the left\n\nHuman: Why does the ego car doing this?\nAssistant: because that vehicle is continuing on the highway.\n\n",
+    "BDDX_Processed/video/0a62ffee-85e97e08_08752.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.86, 5.77, 5.69, 5.59, 5.48, 5.37, 5.47]\n Curvature: [0.0, 0.0, -0.0, -0.07, -0.24, -0.41, -0.38]\n Acceleration: [-1.08, -0.75, -0.45, -0.2, -0.08, 0.04, 0.31]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 6.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's going to make a right turn\n\n",
+    "BDDX_Processed/video/08822b78-8e3a3d0e_07702.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.52, 10.03, 8.54, 10.17, 10.64, 12.3, 13.11]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [-0.25, -1.09, 0.27, 0.43, 0.55, 0.31, 0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down a street\n\nHuman: Why does the ego car doing this?\nAssistant: since the road is clear.\n\n",
+    "BDDX_Processed/video/13d47839-d6add655_13672.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.54, 6.03, 5.56, 5.07, 4.63, 4.18, 4.01]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.45, -1.35, -1.26, -1.13, -0.95, -0.77, -0.53]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.09]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches other traffic\n\n",
+    "BDDX_Processed/video/15be9533-f4c2aac4_14682.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.39, 5.42, 6.43, 7.19, 7.89, 8.2, 8.54]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [2.14, 1.93, 1.71, 1.35, 1.02, 0.84, 0.69]\n Course: [0.0, 0.0, 0.0, 0.0, -0.02, -0.05, -0.01]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: after the merged car has accelerated.\n\n",
+    "BDDX_Processed/video/04dd75bb-9507acc4_03766.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.02, 6.69, 6.37, 6.37, 6.37, 6.37, 6.41]\n Curvature: [-0.01, -0.01, 0.0, 0.02, 0.03, 0.03, 0.03]\n Acceleration: [-0.3, -0.31, -0.31, -0.15, 0.01, 0.12, 0.31]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.45]\nWhat is the action of ego car?\nAssistant: The car veers right\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane shifts and the vehicle ahead has stopped.\n\n",
+    "BDDX_Processed/video/07ef9f5f-42c0ac31_03672.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.04, -0.04, -0.04, -0.06, -0.08, -0.08, -0.09]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic in front of it is stopped.\n\n",
+    "BDDX_Processed/video/101c9c8b-738ae7ec_11730.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.65, 6.94, 6.24, 5.53, 4.81, 4.03, 3.25]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-2.21, -2.38, -2.56, -2.73, -2.86, -2.67, -2.48]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down again.\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/10425a11-c4829447_11774.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.51, 0.44, 0.37, 0.3, 0.24, 0.35, 0.44]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.05, -0.76, -0.5, -0.2, 0.08, 0.3, 0.48]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slowly moves forward again\n\nHuman: Why does the ego car doing this?\nAssistant: while going toward the intersection\n\n",
+    "BDDX_Processed/video/1a0d557d-3edb86c6_16938.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.16, 0.51, 1.29, 2.53, 3.12, 3.77, 4.53]\n Curvature: [0.79, 0.57, 0.39, 0.27, 0.21, 0.16, 0.14]\n Acceleration: [0.35, 0.92, 1.27, 1.39, 1.18, 1.08, 1.21]\n Course: [0.0, -0.3, -2.18, -2.18, -4.75, -4.67, -4.67]\nWhat is the action of ego car?\nAssistant: The car turns left\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turns green.\n\n",
+    "BDDX_Processed/video/16269211-4dc683b2_14954.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.83, 8.09, 7.28, 6.25, 5.25, 5.05, 4.98]\n Curvature: [-0.0, 0.0, 0.0, 0.0, -0.05, -0.43, -0.69]\n Acceleration: [-0.42, -1.1, -1.76, -1.49, -1.13, -0.33, 0.48]\n Course: [0.0, 0.0, 0.0, 0.0, -21.36, -32.03, -5.35]\nWhat is the action of ego car?\nAssistant: The car turns right\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear to turn.\n\n",
+    "BDDX_Processed/video/0886e393-b1f18174_07726.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.35, 17.14, 16.92, 16.71, 16.55, 16.56, 16.58]\n Curvature: [0.02, 0.02, 0.03, 0.04, 0.04, 0.04, 0.03]\n Acceleration: [-0.06, -0.14, -0.23, -0.31, -0.36, -0.27, -0.18]\n Course: [0.0, -0.3, -0.3, -0.3, -0.18, -0.36, -0.36]\nWhat is the action of ego car?\nAssistant: The car merges right\n\nHuman: Why does the ego car doing this?\nAssistant: since it was in the oncoming traffic lane\n\n",
+    "BDDX_Processed/video/06924f46-983ee289_06649.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.7, 6.8, 9.07, 11.07, 12.71, 12.23, 11.14]\n Curvature: [0.0, 0.01, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [3.03, 2.6, 2.06, 1.8, 0.56, -0.74, -1.86]\n Course: [0.0, -0.04, -0.27, 0.0, 0.0, -0.01, -0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/1eef3e68-6c0fbc27_19284.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 1.45, 1.41, 0.56, 0.23, 0.37, 0.27]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.71, 0.65, -0.4, -0.54, -0.04, 0.02, -0.05]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slowly edges forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the light turns green but pedestrians are still crossing the street.\n\n",
+    "BDDX_Processed/video/22442774-fcc1c593_20949.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.71, 6.41, 6.07, 6.97, 7.21, 7.52, 8.2]\n Curvature: [0.0, 0.01, -0.0, -0.03, -0.07, -0.05, -0.03]\n Acceleration: [-0.11, -0.27, 0.12, 0.47, -0.13, 0.47, -0.0]\n Course: [0.0, 0.01, 0.0, -0.4, 2.59, 3.19, 3.79]\nWhat is the action of ego car?\nAssistant: The car is driving slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because there is traffic and snowy conditions.\n\n",
+    "BDDX_Processed/video/18fc7b57-3272f93d_16485.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.12, 17.04, 15.74, 13.72, 11.55, 8.76, 5.79]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.05, -0.94, -1.94, -2.76, -3.24, -3.7, -3.05]\n Course: [0.0, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: since the light ahead has turned red.\n\n",
+    "BDDX_Processed/video/1d4c2467-b3826487_18494.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.55, 17.62, 17.39, 16.78, 16.43, 15.58, 13.5]\n Curvature: [0.0, 0.01, 0.0, -0.01, -0.0, 0.01, 0.01]\n Acceleration: [-0.08, -0.3, -0.51, -0.71, -1.33, -1.8, -1.73]\n Course: [0.0, -0.11, 0.0, 0.0, 0.05, -0.53, -0.53]\nWhat is the action of ego car?\nAssistant: The car pulls into the middle lane\n\nHuman: Why does the ego car doing this?\nAssistant: because that's where the driver wants to be.\n\n",
+    "BDDX_Processed/video/0573f031-fe89a0d4_00954.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.28, 7.5, 7.73, 7.88, 7.92, 7.96, 8.1]\n Curvature: [0.01, -0.0, -0.01, -0.01, 0.01, 0.03, 0.01]\n Acceleration: [0.66, 0.55, 0.42, 0.33, 0.3, 0.27, 0.37]\n Course: [0.0, 0.05, 0.07, 0.02, 0.04, 0.02, 0.0]\nWhat is the action of ego car?\nAssistant: The car maintains a slow speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is moving slowly.\n\n",
+    "BDDX_Processed/video/0ac2cd53-8287aaa9_09004.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.95, 6.94, 6.92, 6.81, 6.74, 6.97, 7.21]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.45, 0.16, -0.11, 0.01, 0.14, 0.33, 0.4]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is clear.\n\n",
+    "BDDX_Processed/video/13796ade-b4bcd515_13500.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.72, 8.4, 8.12, 7.81, 7.48, 7.11, 6.79]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.88, -0.99, -1.09, -1.19, -1.29, -1.33, -1.35]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves to the left lane while decelerating\n\nHuman: Why does the ego car doing this?\nAssistant: because a car up ahead is stopped at a red light and the left lane is empty.\n\n",
+    "BDDX_Processed/video/0a7b0436-1a46bb12_08803.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.06, 0.66, 1.79, 2.77, 3.43, 3.83]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.0, 0.26, 0.74, 0.95, 0.68, 0.45, -0.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic in front of it is moving forward.\n\n",
+    "BDDX_Processed/video/1532b0d8-435a4bfb_14315.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.02, 3.41, 3.8, 4.08, 4.22, 4.37, 4.75]\n Curvature: [0.0, 0.11, 0.22, 0.26, 0.24, 0.23, 0.14]\n Acceleration: [0.98, 0.86, 0.75, 0.7, 0.72, 0.75, 0.84]\n Course: [0.0, -3.69, -3.69, -3.54, -2.36, -3.54, -2.01]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: because it's making a left turn.\n\n",
+    "BDDX_Processed/video/0bf38594-f286ef43_09626.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.92, 6.44, 5.99, 5.8, 6.02, 6.23, 6.77]\n Curvature: [-0.56, -0.38, -0.22, -0.1, -0.05, -0.0, 0.0]\n Acceleration: [-1.58, -1.08, -0.63, -0.1, 0.47, 1.03, 1.19]\n Course: [0.0, -27.7, -18.47, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing and making a right hand turn\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may enter another road.\n\n",
+    "BDDX_Processed/video/047e732b-aa79a87d_03382.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.8, 13.9, 13.81, 13.32, 12.33, 11.16, 10.11]\n Curvature: [-0.01, -0.0, 0.0, 0.01, 0.02, 0.0, -0.01]\n Acceleration: [0.56, 0.1, -0.42, -0.93, -1.38, -1.42, -1.01]\n Course: [0.0, 0.14, 0.07, 0.07, -0.07, -0.2, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is slowing.\n\n",
+    "BDDX_Processed/video/1aa857d4-a6f9e781_17262.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.19, 1.38, 3.55, 4.82, 5.37, 5.49]\n Curvature: [0.0, 0.0, -0.0, 0.0, -0.0, 0.0, -0.01]\n Acceleration: [0.0, 0.52, 1.33, 1.41, 0.68, 0.26, 0.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car advances through the intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is advancing.\n\n",
+    "BDDX_Processed/video/0a07abbf-5eeee19d_08641.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.7, 1.59, 2.48, 3.4, 4.29, 5.37, 6.28]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [1.18, 1.47, 1.74, 1.85, 1.89, 1.52, 1.17]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic is moving.\n\n",
+    "BDDX_Processed/video/07e29820-efa9e7da_07335.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.08, 6.51, 5.98, 5.38, 4.86, 4.66, 4.45]\n Curvature: [0.01, -0.01, -0.03, -0.07, -0.1, -0.13, -0.13]\n Acceleration: [-1.05, -1.07, -1.08, -0.91, -0.74, -0.54, -0.4]\n Course: [0.0, -0.05, 0.06, 0.38, -22.95, -46.08, 2.49]\nWhat is the action of ego car?\nAssistant: The car slows and turns right\n\nHuman: Why does the ego car doing this?\nAssistant: so it can get into the side street.\n\n",
+    "BDDX_Processed/video/0e460dd3-2cf2a7b9_10901.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.29, 5.85, 6.73, 7.69, 8.34, 9.47, 10.48]\n Curvature: [0.02, 0.03, -0.01, -0.02, 0.0, 0.0, -0.01]\n Acceleration: [0.55, 0.7, 0.9, 0.77, 0.86, 1.03, 0.95]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins to accelerate again to normal speed.\n\nHuman: Why does the ego car doing this?\nAssistant: The car in front is accelerating up to normal speed.\n\n",
+    "BDDX_Processed/video/1dcbdf3a-7d747c0d_18720.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [20.75, 20.99, 21.18, 21.3, 21.05, 20.77, 20.53]\n Curvature: [0.03, 0.02, 0.01, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.2, 0.25, 0.16, -0.1, -0.3, -0.41, -0.38]\n Course: [0.0, -0.09, -0.27, -0.27, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls over to the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because the right lane is merging with the left.\n\n",
+    "BDDX_Processed/video/080f9a47-c88dc0a5_07402.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.37, 13.47, 13.69, 14.05, 14.16, 14.04, 13.96]\n Curvature: [0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.09, 0.15, 0.28, 0.23, -0.01, -0.09, -0.19]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.01]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic lights are green and there is no traffic ahead of it.\n\n",
+    "BDDX_Processed/video/06ff7d61-53ba5061_00613.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.73, 19.71, 19.68, 19.66, 19.67, 19.89, 20.11]\n Curvature: [0.02, 0.01, -0.0, -0.01, -0.02, -0.01, -0.01]\n Acceleration: [-0.28, -0.1, 0.06, 0.24, 0.38, 0.48, 0.58]\n Course: [0.0, -0.02, -0.02, -0.02, 0.02, 0.05, 0.05]\nWhat is the action of ego car?\nAssistant: The car is merging into the lane to the left.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is coming to an intersection and is preparing to move to the left hand turn lane.\n\n",
+    "BDDX_Processed/video/055e339a-e98dd764_00720.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.53, 6.56, 5.51, 4.99, 5.01, 5.04, 5.43]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-1.77, -1.57, -1.35, -0.93, -0.19, 0.5, 0.98]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: to go over a bump\n\n",
+    "BDDX_Processed/video/16ac78f8-c2c89053_15387.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.28, 3.46, 3.62, 3.9, 4.32, 4.74, 5.22]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.01, 0.31, 0.58, 0.83, 1.01, 1.19, 1.16]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has slowed down.\n\n",
+    "BDDX_Processed/video/11318349-98f9b811_12326.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.25, 8.12, 8.02, 7.89, 7.71, 7.14, 6.64]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, -0.02, -0.03]\n Acceleration: [-0.35, -0.6, -0.83, -1.08, -1.37, -1.89, -2.35]\n Course: [0.0, -0.02, -0.01, -0.02, 0.01, 0.05, 0.02]\nWhat is the action of ego car?\nAssistant: The car is slowing down.\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/1aa6dd36-f19e02e5_17237.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.01, 0.84, 0.75, 0.41, 0.47, 0.56, 0.55]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.19, -0.14, -0.24, -0.13, 0.05, 0.05, -0.06]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls forward and stops\n\nHuman: Why does the ego car doing this?\nAssistant: since traffic ahead is stopped at a red light.\n\n",
+    "BDDX_Processed/video/1236ceb8-804401af_12825.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.89, 3.48, 5.31, 6.23, 6.75, 7.08]\n Curvature: [-0.0, -0.0, -0.0, -0.01, -0.0, 0.0, -0.0]\n Acceleration: [0.0, 1.19, 1.31, 0.7, 0.42, 0.26, -0.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.16, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned green.\n\n",
+    "BDDX_Processed/video/1d288c57-918d5ae6_18403.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [28.38, 28.48, 28.58, 28.67, 28.83, 29.03, 29.28]\n Curvature: [-0.01, -0.0, 0.0, 0.01, 0.0, -0.0, -0.0]\n Acceleration: [-0.05, 0.08, 0.16, 0.2, 0.27, 0.34, 0.37]\n Course: [0.0, 0.07, -0.07, -0.07, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves one lane to the right\n\nHuman: Why does the ego car doing this?\nAssistant: since the right lane is clear.\n\n",
+    "BDDX_Processed/video/130a7c58-260fcd28_13217.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.1, 3.68, 3.19, 2.77, 2.39, 2.21, 2.06]\n Curvature: [-0.04, -0.04, -0.05, -0.05, -0.06, -0.15, -0.22]\n Acceleration: [-2.01, -1.8, -1.56, -1.35, -1.14, -0.96, -0.81]\n Course: [0.0, 1.44, 2.88, 2.88, 0.72, 1.43, 1.43]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: as it is parking.\n\n",
+    "BDDX_Processed/video/2021c742-bef77982_19973.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.38, 0.69, 1.39, 2.49, 3.74, 4.7, 4.97]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, 0.0, -0.33]\n Acceleration: [0.39, 0.69, 1.12, 1.43, 1.39, 0.81, 0.34]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -54.61]\nWhat is the action of ego car?\nAssistant: The car accelerates and turns right\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turns green.\n\n",
+    "BDDX_Processed/video/07184fbb-af89b759_01319.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.54, 0.73, 0.76, 0.55, 0.42, 0.16, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.24, 0.17, -0.01, -0.06, -0.15, -0.14, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car remains stopped at a red light.\n\nHuman: Why does the ego car doing this?\nAssistant: The car stays stopped because the red light has not changed yet.\n\n",
+    "BDDX_Processed/video/0abf21f6-eaacbb29_08998.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.31, 11.3, 12.58, 13.14, 13.05, 13.35, 13.46]\n Curvature: [0.0, 0.0, -0.0, 0.0, -0.01, -0.0, 0.0]\n Acceleration: [0.18, 0.56, 0.45, -0.1, 0.08, 0.08, -0.15]\n Course: [0.0, -0.21, 1.04, -0.65, 0.23, 0.0, -0.22]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light was green and traffic is moving at a normal rate of speed.\n\n",
+    "BDDX_Processed/video/0fb86701-246317f2_11505.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.45, 2.53, 2.69, 2.94, 3.29, 3.49, 3.44]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.12, 0.21, 0.32, 0.46, 0.33, 0.15, -0.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves slowly\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic is congested.\n\n",
+    "BDDX_Processed/video/0070bc56-7d01076a_01417.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.68, 8.19, 7.66, 7.16, 6.63, 6.2, 5.72]\n Curvature: [0.0, 0.01, 0.01, -0.01, -0.02, -0.01, 0.0]\n Acceleration: [-0.92, -0.96, -0.99, -0.96, -0.93, -0.93, -0.93]\n Course: [0.0, -0.04, -0.05, -0.04, -0.02, 0.0, 0.04]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is braking, signaled by its brake lights.\n\n",
+    "BDDX_Processed/video/02d478d1-937f841f_03142.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.77, 9.35, 10.91, 12.11, 12.34, 12.05, 10.54]\n Curvature: [0.07, -0.01, 0.01, 0.01, -0.01, -0.04, 0.0]\n Acceleration: [1.73, 1.56, 0.52, 0.43, 0.12, -0.5, -1.31]\n Course: [0.0, -0.98, -0.16, -0.3, -0.07, 0.06, 0.16]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/1532b0d8-91c5e136_14323.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.41, 9.81, 10.17, 10.55, 10.87, 11.22, 11.41]\n Curvature: [-0.03, -0.02, -0.01, -0.01, -0.0, 0.0, 0.01]\n Acceleration: [1.53, 1.29, 1.07, 0.88, 0.78, 0.66, 0.52]\n Course: [0.0, 0.21, 0.21, 0.14, 0.09, 0.14, -0.18]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: Because it has come out of the right turn and is now going straight with no traffic in front of it and the traffic light is green.\n\n",
+    "BDDX_Processed/video/16a98dc3-c4e841ab_15368.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.14, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: while the car in front slowly moves forward\n\n",
+    "BDDX_Processed/video/137ec1c1-eee6f6be_13563.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.46, 8.77, 10.13, 10.7, 11.3, 12.8, 13.5]\n Curvature: [0.0, -0.0, 0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [1.07, 1.15, 0.51, 0.37, 0.66, 0.63, 0.66]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car advances down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as there are no vehicles driving on the roadway.\n\n",
+    "BDDX_Processed/video/0d66ed0a-b2595268_10531.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.46, 8.04, 7.61, 7.19, 6.82, 5.47, 4.11]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-1.79, -2.2, -2.6, -3.01, -3.36, -3.21, -3.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: as it prepares to make a right hand turn.\n\n",
+    "BDDX_Processed/video/0710389f-2a7ff2be_06913.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.66, 0.61, 0.57, 0.76, 0.93, 0.91, 0.91]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.1, 0.02, 0.14, 0.15, 0.16, 0.11, 0.06]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because it is at a busy intersection\n\n",
+    "BDDX_Processed/video/135a9354-3c1d7523_13361.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.84, 13.97, 11.81, 9.55, 7.82, 6.77, 6.64]\n Curvature: [-0.01, 0.05, 0.06, 0.01, 0.04, 0.05, 0.01]\n Acceleration: [-1.45, -2.6, -3.19, -3.05, -2.02, -1.23, -0.9]\n Course: [0.0, -0.56, -0.64, -0.52, -0.18, -0.14, -0.17]\nWhat is the action of ego car?\nAssistant: The car brakes\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic ahead has slowed down.\n\n",
+    "BDDX_Processed/video/05f899e8-b72038aa_01931.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.33, 11.19, 10.4, 9.59, 8.2, 5.55, 5.05]\n Curvature: [0.01, 0.02, 0.0, -0.0, -0.04, 0.02, -1.01]\n Acceleration: [-1.56, -0.86, -0.63, -0.98, -1.69, -1.47, -1.74]\n Course: [0.0, -0.53, -0.11, 0.0, 0.0, 0.21, 1.16]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic in front of it is stopped.\n\n",
+    "BDDX_Processed/video/21f695ce-930a648f_20784.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.5, 1.44, 1.38, 1.31, 1.26, 1.23, 1.19]\n Curvature: [0.07, 0.05, 0.04, 0.03, 0.02, 0.01, 0.01]\n Acceleration: [-0.12, -0.14, -0.15, -0.17, -0.18, -0.16, -0.15]\n Course: [0.0, 0.2, 0.2, 0.2, -1.1, -2.4, -2.4]\nWhat is the action of ego car?\nAssistant: The car turns off the headlights\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is parking.\n\n",
+    "BDDX_Processed/video/17ffc2bb-02076e76_16012.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.36, 10.44, 10.32, 10.33, 10.21, 9.92, 7.1]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.19, -0.17, -0.09, 0.2, -0.17, -0.13, -1.4]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.01, 0.04]\nWhat is the action of ego car?\nAssistant: The car is driving normally\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no one on the road.\n\n",
+    "BDDX_Processed/video/0c5ff0c6-b1853637_09806.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.69, 15.58, 15.48, 15.36, 15.24, 15.11, 14.99]\n Curvature: [-0.05, -0.05, -0.06, -0.07, -0.08, -0.16, -0.22]\n Acceleration: [-0.76, -0.68, -0.61, -0.53, -0.5, -0.8, -1.06]\n Course: [0.0, 0.63, 0.32, 0.63, 0.62, 0.61, 0.3]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no other cars in its vicinity.\n\n",
+    "BDDX_Processed/video/02e948bb-53fcbba0_05377.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.1, 2.76, 2.65, 2.55, 2.39, 2.24, 1.56]\n Curvature: [0.0, 0.01, 0.13, -0.14, 0.09, -0.12, -0.04]\n Acceleration: [-0.31, -0.14, -0.07, -0.1, -0.09, -0.33, -0.43]\n Course: [0.0, -0.13, 0.0, -0.33, 0.0, -0.34, 0.12]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign ahead\n\n",
+    "BDDX_Processed/video/1c4bf60c-586beb58_18000.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.31, 11.86, 12.0, 13.7, 15.02, 14.01, 11.89]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-1.4, -0.61, 0.53, 0.84, 0.29, -1.0, -1.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: since the road is clear.\n\n",
+    "BDDX_Processed/video/0943bcdd-481da984_08117.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.54, 4.54, 4.54, 4.54, 4.52, 4.37, 4.23]\n Curvature: [-0.01, 0.02, 0.04, 0.07, 0.11, 0.15, 0.2]\n Acceleration: [1.17, 0.8, 0.42, 0.04, -0.28, -0.27, -0.26]\n Course: [0.0, -0.18, -0.18, -0.18, -0.65, -1.11, -1.11]\nWhat is the action of ego car?\nAssistant: The car pulls forward\n\nHuman: Why does the ego car doing this?\nAssistant: There are no cars crossing in the intersection.\n\n",
+    "BDDX_Processed/video/1e2d42a4-83915709_18959.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.26, 2.41, 3.51, 4.2, 4.82, 5.28, 5.83]\n Curvature: [0.0, 0.36, 0.64, 0.36, 0.13, 0.06, -0.0]\n Acceleration: [2.16, 1.94, 1.71, 1.38, 1.13, 1.28, 1.42]\n Course: [0.0, -5.88, -5.98, -6.08, -1.52, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning to the left.\n\n",
+    "BDDX_Processed/video/03d34d05-4a61174c_05718.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.03, 5.45, 4.86, 4.28, 3.78, 3.26, 2.74]\n Curvature: [-0.15, -0.46, -0.77, -1.08, -1.28, -0.97, -0.67]\n Acceleration: [-1.79, -1.87, -1.95, -2.03, -2.09, -2.07, -2.04]\n Course: [0.0, 0.02, 0.02, 0.02, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: as it waits at the red light\n\nHuman: Why does the ego car doing this?\nAssistant: The car is stopped\n\n",
+    "BDDX_Processed/video/0b498ab4-104c3f92_09247.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.49, 11.19, 10.9, 10.44, 10.06, 9.93, 8.7]\n Curvature: [0.0, -0.01, -0.0, 0.0, 0.0, -0.0, 0.01]\n Acceleration: [-0.26, -0.25, -0.31, -0.4, -0.18, -0.58, -0.71]\n Course: [0.0, 0.06, -0.1, -0.1, -0.11, 0.1, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating slowly\n\nHuman: Why does the ego car doing this?\nAssistant: As traffic in front of it and lanes surrounding it are moving slow.\n\n",
+    "BDDX_Processed/video/047a7ecb-68221e4a_06031.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.34, 6.27, 6.2, 6.22, 6.36, 6.5, 6.48]\n Curvature: [0.03, 0.0, -0.03, -0.03, -0.01, 0.02, 0.02]\n Acceleration: [0.02, 0.05, 0.07, 0.09, 0.1, 0.11, 0.03]\n Course: [0.0, -0.13, -0.1, -0.03, -0.04, -0.03, -0.29]\nWhat is the action of ego car?\nAssistant: The car is driving in the middle of the two lanes\n\nHuman: Why does the ego car doing this?\nAssistant: because the right lane has obstacles in it.\n\n",
+    "BDDX_Processed/video/00268999-9f6d5823_00430.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.56, 6.91, 5.37, 3.87, 2.52, 1.51, 0.68]\n Curvature: [-0.02, -0.02, -0.03, -0.01, -0.0, -0.0, -0.01]\n Acceleration: [-2.97, -2.96, -2.94, -2.63, -2.3, -1.59, -0.84]\n Course: [0.0, 0.39, 0.37, 0.32, 0.08, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car veers right and slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: for an unknown reason.\n\n",
+    "BDDX_Processed/video/040fff88-4e31bfdd_05852.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.66, 7.24, 8.77, 8.89, 8.15, 7.6, 7.88]\n Curvature: [-0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.12, 0.73, 0.6, -0.31, -0.47, -0.05, 0.07]\n Course: [0.0, 0.0, -0.01, 0.0, 0.0, 0.0, 0.17]\nWhat is the action of ego car?\nAssistant: The car is accelerating again\n\nHuman: Why does the ego car doing this?\nAssistant: Because the car in the opposing traffic that made the left turn has cleared off the street and traffic is once again clear ahead.\n\n",
+    "BDDX_Processed/video/0714456d-619b3a70_01035.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.43, 0.92, 1.52, 2.42, 3.67, 5.0, 6.58]\n Curvature: [0.1, 0.06, 0.02, -0.0, 0.02, -0.01, -0.01]\n Acceleration: [0.52, 0.7, 1.01, 1.36, 1.66, 1.9, 1.5]\n Course: [0.0, 0.14, -0.11, -0.07, -0.07, -0.07, -0.06]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving.\n\n",
+    "BDDX_Processed/video/0848e4c3-34d62aa6_07568.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.9, 2.51, 4.06, 5.26, 6.23, 6.95]\n Curvature: [-0.0, -0.0, -0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.59, 1.49, 1.9, 1.82, 1.41, 1.1, 0.92]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.17]\nWhat is the action of ego car?\nAssistant: the car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is green\n\n",
+    "BDDX_Processed/video/0b9bd129-fb05843a_09508.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.13, -0.13, -0.13, -0.13, -0.13, -0.13, -0.13]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.14]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it's waiting for another car to turn.\n\n",
+    "BDDX_Processed/video/097450c5-3e2b46e6_08181.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.04, 0.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is at a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because it is waiting at an intersection where the traffic light is red.\n\n",
+    "BDDX_Processed/video/0f6ea00d-ef00cdae_11407.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.37, 2.0, 3.65, 4.67, 5.19, 5.64, 6.01]\n Curvature: [0.0, 0.0, 0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.09, 1.74, 1.47, 0.88, 0.56, 0.44, 0.4]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.23]\nWhat is the action of ego car?\nAssistant: The car accelerates down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turns green.\n\n",
+    "BDDX_Processed/video/006a7635-c42f9f97_04432.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.57, 1.04, 0.38, 0.24, 0.55, 0.71, 0.26]\n Curvature: [-0.15, 0.26, 0.43, 1.46, 1.74, 1.11, 0.73]\n Acceleration: [0.15, -0.42, -0.3, 0.11, 0.1, -0.09, -0.01]\n Course: [0.0, -6.12, -7.31, -7.62, -8.23, -8.57, -7.52]\nWhat is the action of ego car?\nAssistant: The car is slowing down in reverse and pulling back over to the side of the street again\n\nHuman: Why does the ego car doing this?\nAssistant: Because the car is parking in the new parking spot on the left side of the street.\n\n",
+    "BDDX_Processed/video/165a8caf-e1ce6f38_15105.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.9, 4.49, 3.27, 1.86, 0.61, 0.44, 0.29]\n Curvature: [-0.04, 0.07, 0.16, 0.27, 0.36, 0.36, 0.36]\n Acceleration: [-5.64, -4.92, -4.3, -3.58, -2.86, -2.16, -1.54]\n Course: [0.0, -0.02, -0.01, -0.02, 0.0, 0.02, 0.01]\nWhat is the action of ego car?\nAssistant: The car abruptly slows down to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because it is approaching an intersection where the traffic light has turned from green to yellow to red.\n\n",
+    "BDDX_Processed/video/1b48a43c-0f3827d0_17624.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.28, 6.08, 5.89, 5.5, 4.9, 4.24, 3.84]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.51, -0.74, -0.94, -1.11, -1.17, -1.24, -0.87]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a near stop\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a stop sign at an intersection.\n\n",
+    "BDDX_Processed/video/038db692-9c25743b_05591.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.49, 9.75, 10.0, 10.06, 10.11, 10.07, 10.04]\n Curvature: [-0.0, -0.01, -0.01, -0.01, -0.0, -0.0, -0.0]\n Acceleration: [0.59, 0.45, 0.31, 0.16, 0.02, -0.02, -0.05]\n Course: [0.0, 0.0, 0.0, 0.21, 0.11, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as it drives straight down the road\n\n",
+    "BDDX_Processed/video/097120a3-de3c12af_08180.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.79, 4.44, 6.04, 6.93, 7.79, 8.21, 8.57]\n Curvature: [0.0, -0.01, -0.02, -0.01, 0.0, 0.01, 0.01]\n Acceleration: [2.99, 2.72, 2.42, 1.81, 1.21, 0.85, 0.54]\n Course: [0.0, 0.05, 0.06, 0.09, 0.08, 0.05, -0.13]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: after traffic in front of it accelerates due to a green light.\n\n",
+    "BDDX_Processed/video/0b4892bd-444faa63_09242.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.49, 1.29, 2.02, 2.67, 3.02, 3.37, 3.44]\n Curvature: [-0.0, 0.32, 0.62, 0.83, 0.81, 0.8, 0.56]\n Acceleration: [1.06, 1.22, 1.36, 1.33, 0.93, 0.52, 0.71]\n Course: [0.0, 0.24, 0.16, -5.91, -5.91, -5.91, -2.16]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning to the left.\n\n",
+    "BDDX_Processed/video/0a6966b1-ee9ded94_08770.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.35, 4.73, 4.18, 3.56, 2.91, 2.11, 1.4]\n Curvature: [0.01, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.2, -2.32, -2.43, -2.56, -2.64, -2.32, -2.05]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls to the left and stops\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may park.\n\n",
+    "BDDX_Processed/video/00488b40-0a8cf1a0_04369.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.67, 0.61, 0.56, 0.5, 0.47, 0.51, 0.56]\n Curvature: [2.59, 2.51, 2.43, 2.35, 2.24, 1.86, 1.47]\n Acceleration: [-0.83, -0.61, -0.4, -0.18, -0.01, 0.04, 0.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: as there is a read light.\n\n",
+    "BDDX_Processed/video/045cb354-470666ec_05978.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.28, 14.86, 14.47, 14.01, 13.51, 13.01, 12.62]\n Curvature: [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.01]\n Acceleration: [-0.76, -0.92, -1.06, -1.16, -1.16, -1.17, -1.14]\n Course: [0.0, -0.28, -0.19, -0.11, -0.11, -0.11, -0.07]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the cars in front of it move forward.\n\n",
+    "BDDX_Processed/video/0453391a-f15e9e30_05962.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [-1.0, -1.0, -1.0, -1.0, -0.85, 0.37, 1.59]\n Curvature: [-0.0, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01]\n Acceleration: [0.0, 0.63, 1.18, 1.81, 2.21, 1.6, 0.99]\n Course: [0.0, 0.0, 0.0, 0.0, 7.1, 14.2, 14.2]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as the traffic in front begins to move.\n\n",
+    "BDDX_Processed/video/148aff32-c27bd967_13989.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.78, 2.99, 3.19, 3.23, 2.92, 2.96, 2.83]\n Curvature: [0.0, -0.0, 0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.23, 0.16, 0.16, -0.17, -0.1, -0.03, -0.31]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car straightens its direction\n\nHuman: Why does the ego car doing this?\nAssistant: as it drives slowly down the road\n\n",
+    "BDDX_Processed/video/076399f2-d397b12e_02394.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.03, 0.11, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.07, -0.03, -0.04, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car waits at the side of the road\n\nHuman: Why does the ego car doing this?\nAssistant: as the driver is not ready to proceed.\n\n",
+    "BDDX_Processed/video/0603e6ca-1f32cf5c_02061.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08]\n Acceleration: [-0.34, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.21, 0.0, 0.21, 0.05, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car inches right slightly\n\nHuman: Why does the ego car doing this?\nAssistant: to try to merge right.\n\n",
+    "BDDX_Processed/video/21a40bb6-3e3a3e67_20650.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.48, 17.57, 15.9, 14.45, 14.99, 17.32, 18.14]\n Curvature: [0.0, -0.0, 0.0, -0.0, -0.0, -0.01, -0.0]\n Acceleration: [-0.18, -0.79, -0.89, -0.05, 0.02, 1.02, 0.41]\n Course: [0.0, -1.16, 0.0, 0.16, 0.0, 0.89, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down a road\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane ahead is clear and traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/017b02cb-477b66fa_03758.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.47, 5.27, 3.96, 2.86, 1.88, 0.97, 0.54]\n Curvature: [-0.05, 0.04, 0.15, 0.22, 0.21, 0.21, 0.21]\n Acceleration: [-2.08, -2.36, -2.65, -2.66, -2.15, -1.68, -1.16]\n Course: [0.0, -0.28, -0.42, -0.1, -0.15, -0.1, -0.04]\nWhat is the action of ego car?\nAssistant: The car veers left and slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: to merge back into the left lane.\n\n",
+    "BDDX_Processed/video/135a9354-c68b1b82_13378.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.69, 1.66, 2.62, 3.97, 4.73, 4.85, 4.87]\n Curvature: [0.55, 0.09, 0.02, 0.05, 0.03, 0.03, 0.02]\n Acceleration: [0.54, 0.62, 0.58, 0.81, 0.23, 0.04, -0.03]\n Course: [0.0, -0.76, -0.61, -1.43, -1.27, -1.37, -1.31]\nWhat is the action of ego car?\nAssistant: The car starts to slowly move forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving forward.\n\n",
+    "BDDX_Processed/video/02b4d710-c34ba68e_05261.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.33, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.28, -0.28, -0.28, -0.28, -0.28, -0.28, -0.28]\n Acceleration: [-0.12, -0.16, -0.03, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/07cacc74-cee72ba5_03461.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.28, 7.32, 6.25, 5.03, 4.56, 4.36, 4.56]\n Curvature: [-0.0, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01]\n Acceleration: [-0.75, -1.35, -1.56, -1.27, -0.62, -0.18, -0.27]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is making a left hand turn\n\nHuman: Why does the ego car doing this?\nAssistant: The car is turning through the intersection.\n\n",
+    "BDDX_Processed/video/20dfe192-fcb0a8a7_20268.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.47, 0.0]\n Curvature: [0.06, 0.06, 0.06, 0.06, 0.06, 0.4, 0.68]\n Acceleration: [-0.25, 0.0, 0.0, 0.0, 0.15, 0.07, -0.26]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.28, 0.12]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/207056d6-9179f1e4_20113.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.5, 2.32, 2.12, 1.89, 1.55, 1.24, 0.92]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.33, -0.45, -0.58, -0.69, -0.77, -0.84, -0.86]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is braking.\n\n",
+    "BDDX_Processed/video/0877376c-3ec788af_07649.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [26.12, 25.76, 25.85, 26.02, 26.49, 26.73, 26.92]\n Curvature: [-0.0, -0.01, -0.01, -0.01, -0.01, -0.02, 0.0]\n Acceleration: [-0.13, -0.07, 0.06, 0.14, 0.15, 0.03, -0.26]\n Course: [0.0, 1.61, 0.39, 0.81, 0.56, -0.14, 0.45]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no other cars in its lane.\n\n",
+    "BDDX_Processed/video/17a0445d-6debbef0_15806.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as it is stopped behind other cars at a red light.\n\n",
+    "BDDX_Processed/video/2021c742-4e7b9f7c_19963.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.69, 9.85, 10.54, 11.71, 12.07, 10.8, 9.84]\n Curvature: [0.0, 0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [1.07, 0.52, 0.45, 0.38, -0.14, -0.84, 0.22]\n Course: [0.0, -0.06, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The vehicle is freely driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a safe distance between it and the car in front of it.\n\n",
+    "BDDX_Processed/video/04488604-f1028b71_05935.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.83, 1.01, 1.08, 1.0, 0.94, 0.9, 0.91]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.21, 0.13, 0.02, -0.09, -0.07, 0.14, 0.72]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves slowly along the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the light changed to green.\n\n",
+    "BDDX_Processed/video/08b7e1dc-c621cc6f_07867.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.45, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.19, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is edging forward and stopping, hardly moving\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped and the light ahead is red.\n\n",
+    "BDDX_Processed/video/1f864b3f-cb058068_19714.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.69, 3.66, 5.59, 7.31, 7.84, 8.91, 11.42]\n Curvature: [0.16, -0.02, -0.02, -0.0, 0.01, -0.02, 0.0]\n Acceleration: [0.89, 1.38, 0.96, 0.47, 0.12, 1.41, 0.52]\n Course: [0.0, -0.34, 0.36, -1.19, 0.33, 0.51, 0.18]\nWhat is the action of ego car?\nAssistant: The car slowly proceeds forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned green and cars are moving cautiously due to the wet road.\n\n",
+    "BDDX_Processed/video/14df900d-c5c145cb_14108.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.78, 14.05, 14.72, 14.47, 14.3, 13.84, 13.44]\n Curvature: [-0.03, 0.01, -0.01, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.2, 0.83, 0.1, -0.18, -0.24, -0.34, -0.29]\n Course: [0.0, 0.7, 0.7, -0.12, 0.21, 0.23, 0.35]\nWhat is the action of ego car?\nAssistant: The car proceeds down the road\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is clear ahead.\n\n",
+    "BDDX_Processed/video/0104eb19-162abfea_04627.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.01, 5.93, 10.17, 10.28, 10.94, 12.68]\n Curvature: [0.0, 0.01, 0.02, 0.0, 0.02, 0.0, 0.0]\n Acceleration: [0.0, 0.17, 1.24, 0.63, -1.36, 0.89, -0.64]\n Course: [0.0, 0.0, 33.21, 0.72, -1.7, 0.44, 0.04]\nWhat is the action of ego car?\nAssistant: The car moves along the road\n\nHuman: Why does the ego car doing this?\nAssistant: Because traffic is moving forward.\n\n",
+    "BDDX_Processed/video/0ea059ef-6d95b982_11140.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.55, 15.69, 15.47, 14.08, 13.14, 11.42, 11.45]\n Curvature: [0.0, -0.01, 0.0, 0.02, 0.01, 0.04, 0.02]\n Acceleration: [0.17, -0.05, -0.29, -0.75, -0.36, -0.51, 0.69]\n Course: [0.0, 0.26, -0.68, 0.0, -4.73, -3.52, -1.76]\nWhat is the action of ego car?\nAssistant: The car is going down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/10d86362-67be6116_12032.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.98, 1.43, 1.73, 1.9, 1.96, 1.83, 1.43]\n Curvature: [-0.5, -0.17, 0.02, 0.04, 0.01, -0.04, -0.11]\n Acceleration: [0.52, 0.49, 0.38, 0.18, -0.1, -0.42, -0.76]\n Course: [0.0, 0.07, 0.05, 0.07, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has begun to move.\n\n",
+    "BDDX_Processed/video/05e4d323-5e76c428_01584.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [23.27, 23.27, 23.28, 23.28, 23.19, 22.45, 21.8]\n Curvature: [-0.0, -0.03, -0.06, -0.1, -0.12, -0.09, -0.05]\n Acceleration: [0.01, -0.36, -0.69, -1.06, -1.39, -1.43, -1.47]\n Course: [0.0, 0.15, 0.07, 0.15, 0.2, 0.25, 0.12]\nWhat is the action of ego car?\nAssistant: The car veer right\n\nHuman: Why does the ego car doing this?\nAssistant: to take the off ramp.\n\n",
+    "BDDX_Processed/video/031637c4-bc0eaa8e_05490.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.8, 16.11, 16.4, 16.46, 16.21, 15.93, 15.83]\n Curvature: [-0.0, -0.0, -0.01, -0.0, 0.0, 0.01, 0.0]\n Acceleration: [0.45, 0.29, 0.14, -0.04, -0.21, -0.39, -0.39]\n Course: [0.0, -0.04, -0.04, 0.25, 0.16, 0.25, -0.07]\nWhat is the action of ego car?\nAssistant: The car moves over one lane to the right.\n\nHuman: Why does the ego car doing this?\nAssistant: as there is an opening in the traffic.\n\n",
+    "BDDX_Processed/video/0432d284-b1121aaf_02568.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.75, 5.16, 3.61, 2.81, 2.0, 1.13, 0.4]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-3.11, -2.7, -2.3, -1.94, -1.58, -1.23, -0.87]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: after the light turns yellow and then red.\n\n",
+    "BDDX_Processed/video/01213e45-5edb70f0_04677.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.99, 7.52, 6.72, 6.59, 6.74, 7.19, 8.08]\n Curvature: [0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.23, -0.54, -0.35, 0.03, 0.21, 0.51, 0.62]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then merges left\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/13656215-6f02b382_13414.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.62, 11.96, 11.77, 11.89, 12.22, 13.1, 13.49]\n Curvature: [0.0, -0.0, 0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.9, 0.06, -0.06, 0.23, 0.47, 0.61, 0.18]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues straight down the road\n\nHuman: Why does the ego car doing this?\nAssistant: since traffic is clear\n\n",
+    "BDDX_Processed/video/13b306d7-0732fad9_13628.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.81, 8.33, 7.35, 7.11, 6.98, 7.07, 7.52]\n Curvature: [0.04, 0.15, 0.2, 0.17, 0.14, 0.18, 0.35]\n Acceleration: [-2.99, -1.96, -1.04, -0.36, 0.03, 0.4, 0.76]\n Course: [0.0, -1.22, -4.34, -4.34, -4.22, -3.05, -3.05]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it is making a left turn.\n\n",
+    "BDDX_Processed/video/0cd440c4-31cf6726_10095.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.26, 8.01, 5.83, 3.98, 2.45, 1.38, 1.01]\n Curvature: [-0.04, -0.0, -0.02, -0.08, -1.41, -4.69, -12.46]\n Acceleration: [-3.35, -3.28, -3.05, -2.65, -1.92, -1.23, -0.59]\n Course: [0.0, 0.09, 0.06, 0.05, 0.0, 0.05, 0.06]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: for the red light.\n\n",
+    "BDDX_Processed/video/18e7592e-305fca1b_16411.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.62, 12.65, 12.68, 12.71, 12.73, 12.82, 12.91]\n Curvature: [0.11, 0.07, 0.03, -0.01, -0.05, -0.03, -0.01]\n Acceleration: [0.39, 0.35, 0.3, 0.26, 0.22, 0.15, 0.08]\n Course: [0.0, 0.02, 0.02, 0.02, 0.01, 0.09, 0.09]\nWhat is the action of ego car?\nAssistant: The car moves into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it wants to pass the car in front of it.\n\n",
+    "BDDX_Processed/video/083bc808-038273c1_07505.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is at a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because it is at an intersection and the traffic light is red.\n\n",
+    "BDDX_Processed/video/0e7ecff9-ef733740_11049.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.67, 4.54, 3.67, 2.09, 1.9, 1.06, 1.74]\n Curvature: [0.0, 0.01, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.73, -1.14, -0.32, -0.04, -0.55, 0.41, 0.01]\n Course: [0.0, -2.52, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because its lane of traffic is moving slowly and traffic is merging ahead.\n\n",
+    "BDDX_Processed/video/21f695ce-930a648f_20781.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.94, 2.53, 2.1, 1.98, 1.46, 1.37, 1.14]\n Curvature: [0.03, 0.03, 0.32, -0.1, 0.08, 0.17, 0.0]\n Acceleration: [-0.05, -0.4, -0.06, -0.27, -0.2, -0.07, -0.18]\n Course: [0.0, 2.27, 2.87, 2.39, 2.87, 1.86, 2.99]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it is turning right around the end of a parking lot aisle.\n\n",
+    "BDDX_Processed/video/08ad7534-3db5e60b_07821.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.59, 10.8, 10.11, 9.33, 8.64, 8.29, 7.94]\n Curvature: [-0.02, -0.03, -0.04, -0.06, -0.07, -0.04, -0.02]\n Acceleration: [-1.86, -1.93, -1.98, -2.05, -2.11, -2.41, -2.71]\n Course: [0.0, 0.28, 0.28, 0.28, 0.14, 0.12, 0.12]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: for a stop sign.\n\n",
+    "BDDX_Processed/video/145785ea-c78220bd_13892.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.58, 0.04, 0.0, 0.11, 0.06, 0.0, 0.0]\n Curvature: [0.24, 0.24, 0.24, 0.03, -0.05, -0.05, -0.05]\n Acceleration: [-0.91, -0.33, 0.0, 0.02, -0.05, -0.04, 0.0]\n Course: [0.0, -0.25, 0.0, 0.08, -0.07, 0.08, 0.0]\nWhat is the action of ego car?\nAssistant: The car remains stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as a pedestrian crosses in front of it\n\n",
+    "BDDX_Processed/video/1e3c65ba-f847b4df_19001.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.52, 4.19, 4.92, 5.59, 6.24, 6.96, 7.4]\n Curvature: [0.0, 0.0, 0.01, 0.01, 0.01, 0.0, 0.0]\n Acceleration: [1.77, 1.77, 1.78, 1.72, 1.59, 1.44, 1.26]\n Course: [0.0, 0.0, 0.0, -0.17, -0.12, -0.17, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/0378d8dc-92df7b3e_00528.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.0, 7.01, 6.14, 5.16, 4.23, 3.75, 3.32]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-3.39, -3.22, -3.07, -2.91, -2.7, -2.2, -1.77]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the left lane doesn't continue forward.\n\n",
+    "BDDX_Processed/video/1ca994b0-6fbd6ea4_18153.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.27, 0.66, 1.07, 1.48, 1.77, 1.97, 2.08]\n Curvature: [0.02, 0.01, 0.01, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.44, 0.56, 0.59, 0.53, 0.38, 0.25, 0.12]\n Course: [0.0, 0.05, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slowly pulls away from the curb\n\nHuman: Why does the ego car doing this?\nAssistant: because is entering the roadway.\n\n",
+    "BDDX_Processed/video/1698c1ee-12445160_15266.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.45, 10.23, 10.02, 9.8, 9.56, 9.16, 8.76]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.18, -0.44, -0.7, -0.96, -1.2, -1.23, -1.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is merging into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may make a left turn at the intersection ahead.\n\n",
+    "BDDX_Processed/video/1d8ee15c-a64eb02d_18638.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.82, 2.41, 4.49, 6.34, 7.77]\n Curvature: [0.34, 0.35, 0.42, 0.43, 0.26, 0.01, 0.01]\n Acceleration: [0.0, 0.38, 1.13, 1.76, 1.91, 1.61, 1.31]\n Course: [0.0, 0.0, -10.2, -4.45, -4.76, 0.0, -0.29]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/227829d9-6a5c7405_21048.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: in a parking spot.\n\n",
+    "BDDX_Processed/video/0d029d7e-12b07534_10261.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.92, 1.61, 1.31, 1.0, 0.76, 0.65, 0.55]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.64, -0.68, -0.71, -0.75, -0.73, -0.41, -0.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car steers slightly to the left and back\n\nHuman: Why does the ego car doing this?\nAssistant: to get around a vehicle parked to its right.\n\n",
+    "BDDX_Processed/video/1d3825e0-6aafe66d_18448.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.49, 0.75, 1.27, 2.14, 3.43, 4.81, 6.36]\n Curvature: [-0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.05, 0.53, 1.11, 1.67, 1.98, 2.07, 1.7]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no traffic directly in front of it.\n\n",
+    "BDDX_Processed/video/0706e16c-8eb9233a_06910.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.89, 15.04, 14.37, 13.74, 12.98, 9.53, 5.96]\n Curvature: [0.0, -0.08, -0.0, -0.01, -0.01, -0.01, -0.01]\n Acceleration: [-1.66, -0.71, -0.4, -0.06, -1.13, -1.39, -1.39]\n Course: [0.0, -0.99, 0.0, 0.76, 0.75, 0.98, 0.97]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped in front.\n\n",
+    "BDDX_Processed/video/0d2354a4-5a8192dd_10427.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [21.29, 21.32, 21.35, 21.38, 21.33, 20.73, 20.13]\n Curvature: [-0.02, -0.02, -0.02, -0.02, -0.01, -0.0, 0.0]\n Acceleration: [0.22, -0.13, -0.47, -0.81, -1.16, -1.54, -1.92]\n Course: [0.0, 0.06, 0.06, 0.06, 0.03, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops turning\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is now straight again\n\n",
+    "BDDX_Processed/video/1015e05f-83f9f562_11721.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.38, 5.82, 5.31, 4.77, 4.26, 3.74, 3.61]\n Curvature: [-0.06, -0.03, -0.01, -0.06, -0.22, -0.37, -0.36]\n Acceleration: [-0.72, -0.98, -1.21, -1.26, -1.02, -0.79, -0.41]\n Course: [0.0, 0.0, 0.0, 0.32, 0.32, 0.32, 5.8]\nWhat is the action of ego car?\nAssistant: The car is turning right.\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is entering another street.\n\n",
+    "BDDX_Processed/video/0289eba3-1c552656_01954.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.84, 13.01, 12.28, 11.45, 10.57, 9.39, 8.36]\n Curvature: [0.01, 0.01, 0.0, -0.0, -0.01, -0.0, 0.0]\n Acceleration: [-2.34, -2.73, -3.06, -3.44, -3.77, -3.76, -3.75]\n Course: [0.0, -0.07, -0.04, -0.07, 0.02, 0.12, 0.06]\nWhat is the action of ego car?\nAssistant: The car merges into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: so that it can make a left turn.\n\n",
+    "BDDX_Processed/video/1f4c7852-72686adc_19521.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.24, 2.06, 2.89, 3.72, 4.57, 5.41, 6.14]\n Curvature: [-0.07, -0.09, -0.12, -0.09, 0.02, 0.13, 0.11]\n Acceleration: [1.68, 1.87, 2.05, 2.13, 2.07, 2.01, 1.91]\n Course: [0.0, 21.24, 21.24, -2.52, -2.52, -2.52, -1.29]\nWhat is the action of ego car?\nAssistant: The car moves forward to the left\n\nHuman: Why does the ego car doing this?\nAssistant: as it gets onto the road\n\n",
+    "BDDX_Processed/video/14e111e7-722385a8_14122.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.47, 4.77, 6.33, 8.53, 10.3, 10.97, 11.16]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.72, 0.62, 1.44, 1.47, 0.87, 0.24, -0.22]\n Course: [0.0, 0.0, 0.0, 0.04, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a red light and two people crossing in the crosswalk.\n\n",
+    "BDDX_Processed/video/15c70e70-e3b6a249_14698.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.48, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.2, -0.23, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops again\n\nHuman: Why does the ego car doing this?\nAssistant: while waiting at the light\n\n",
+    "BDDX_Processed/video/0b3912a8-ac966f70_09222.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.91, 3.33, 2.82, 2.24, 1.71, 1.59, 1.48]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.01, -0.0, -0.01]\n Acceleration: [-3.14, -2.66, -2.23, -1.75, -1.28, -0.91, -0.59]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is waiting for the motorcylce to pull into its parking spot.\n\n",
+    "BDDX_Processed/video/1ecd8829-f3779856_19254.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.17, 0.33, 0.38, 0.5, 1.12, 1.86]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.17, 0.19, 0.22, 0.45, 0.72, 1.33, 1.89]\n Course: [0.0, -0.04, -0.03, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: since traffic ahead is not moving.\n\n",
+    "BDDX_Processed/video/1504d2f9-933da05f_14194.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.66, 4.47, 4.59, 5.11, 6.17, 7.2, 8.06]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [-0.22, 0.08, 0.55, 1.2, 1.42, 1.25, 0.45]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car speeds up and moves to the right\n\nHuman: Why does the ego car doing this?\nAssistant: as it switches lanes\n\n",
+    "BDDX_Processed/video/1a33d01a-f55b3303_17003.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.63, 10.94, 11.29, 11.66, 11.92, 12.23, 12.48]\n Curvature: [-0.0, -0.0, -0.0, 0.01, 0.01, 0.01, -0.0]\n Acceleration: [0.35, 0.37, 0.4, 0.34, 0.32, 0.3, -0.09]\n Course: [0.0, 0.25, 0.25, -0.07, -0.07, -0.35, 0.08]\nWhat is the action of ego car?\nAssistant: The car drifts right\n\nHuman: Why does the ego car doing this?\nAssistant: while going down the street\n\n",
+    "BDDX_Processed/video/2021c742-9e004c6f_19970.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.3, 2.73, 3.19, 3.66, 4.19, 4.88, 5.61]\n Curvature: [0.0, -0.06, -0.13, -0.2, -0.31, -0.6, -0.73]\n Acceleration: [0.86, 0.88, 0.91, 1.04, 1.17, 1.27, 1.35]\n Course: [0.0, 3.4, 4.16, 2.29, 3.89, 3.55, 0.37]\nWhat is the action of ego car?\nAssistant: The car is making a right turn\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no other vehicles or people in its way.\n\n",
+    "BDDX_Processed/video/07254f2e-32a98be9_01679.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.07, 0.24, 0.14, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.11, 0.0, -0.12, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: Traffic ahead of the car has stopped.\n\n",
+    "BDDX_Processed/video/1816310a-5b8fc686_16056.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.14, 9.75, 10.29, 10.9, 11.5, 11.94, 12.34]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [2.3, 2.22, 2.14, 2.06, 1.97, 1.84, 1.72]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car travels in the left lane of a wet road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear of traffic.\n\n",
+    "BDDX_Processed/video/04629c39-69d7c08c_03129.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.28, 8.23, 8.13, 7.09, 6.08, 4.5, 2.94]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.25, -0.67, -1.1, -1.84, -2.5, -2.68, -2.7]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped.\n\n",
+    "BDDX_Processed/video/1e3dcb86-1a791f34_19022.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.99, 6.81, 6.65, 6.48, 6.35, 6.58, 6.79]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.44, -0.29, -0.17, -0.02, 0.13, 0.35, 0.54]\n Course: [0.0, 0.01, 0.01, 0.01, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: as a vehicle in front of it slows to make a right hand turn.\n\n",
+    "BDDX_Processed/video/130add4f-d6b8cd0a_13242.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 1.64, 3.0, 3.18, 4.72, 4.83, 5.7]\n Curvature: [0.03, 0.03, 0.0, -0.0, -0.01, -0.01, 0.02]\n Acceleration: [0.6, 0.72, 0.34, 0.41, 0.19, 0.27, 0.26]\n Course: [0.0, -0.33, -0.2, 0.19, 0.38, 0.6, 0.41]\nWhat is the action of ego car?\nAssistant: The car moves slowly down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as it keeps up with traffic\n\n",
+    "BDDX_Processed/video/13796ade-e12adb3b_13506.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.27, 10.49, 10.66, 10.76, 10.72, 10.87, 11.39]\n Curvature: [0.0, 0.0, 0.01, 0.02, 0.01, 0.01, 0.01]\n Acceleration: [0.14, 0.21, 0.19, 0.05, 0.22, 0.36, 0.4]\n Course: [0.0, -0.11, 0.0, 0.0, -0.4, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car goes to the right\n\nHuman: Why does the ego car doing this?\nAssistant: while moving across to the right lane\n\n",
+    "BDDX_Processed/video/1125356f-b698b999_12264.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.28, 2.14, 4.18, 5.76, 6.52, 6.63, 7.81]\n Curvature: [0.05, 0.05, 0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [1.03, 1.9, 1.77, 1.14, 0.4, 0.64, 0.82]\n Course: [0.0, -0.0, -0.02, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned green.\n\n",
+    "BDDX_Processed/video/0e460dd3-6a2d1311_10910.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.37, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.11, 0.78, 2.16, 2.63, 2.22, 1.18, 1.08]\n Acceleration: [-0.29, -0.13, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is blocked by traffic.\n\n",
+    "BDDX_Processed/video/0fc0c815-8ba80d60_11539.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.75, 4.24, 4.7, 5.48, 6.22, 7.24, 8.29]\n Curvature: [0.0, 0.01, 0.02, 0.01, -0.01, 0.0, 0.01]\n Acceleration: [0.48, 0.85, 1.2, 1.46, 1.69, 1.91, 2.04]\n Course: [0.0, -0.05, -0.05, -0.38, -0.26, -0.28, -0.11]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane in front of it is free of traffic and the light is green.\n\n",
+    "BDDX_Processed/video/1212b385-7fe587c2_12800.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.17, 0.48, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.01, -0.02]\n Acceleration: [0.07, -0.01, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/1bb0ae8c-4b09ba9e_17756.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.56, 1.41, 1.15, 0.9, 0.75, 0.85, 1.05]\n Curvature: [-0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01]\n Acceleration: [0.04, -0.22, -0.3, -0.23, -0.03, 0.17, 0.22]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 64.05]\nWhat is the action of ego car?\nAssistant: The car inches forward as it makes a left turn\n\nHuman: Why does the ego car doing this?\nAssistant: because there pedestrians crossing the road.\n\n",
+    "BDDX_Processed/video/1bf7446f-55e5b149_17891.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.46, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.07, -0.21, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/15f3d7e1-85cf2263_14826.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.46, 2.07, 1.68, 1.28, 0.96, 1.08, 1.2]\n Curvature: [-0.02, -0.0, 0.01, 0.02, 0.04, 0.03, 0.02]\n Acceleration: [-1.85, -1.51, -1.17, -0.82, -0.47, 0.01, 0.49]\n Course: [0.0, -0.14, -0.14, -0.14, -0.05, 0.05, 0.05]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because there's a stop sign.\n\n",
+    "BDDX_Processed/video/161ba5ea-4c97217e_14918.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.83, 12.37, 12.76, 14.99, 15.62, 15.99, 15.82]\n Curvature: [0.0, 0.03, 0.01, -0.01, -0.01, 0.01, -0.01]\n Acceleration: [-0.25, -0.13, 0.8, 0.74, -0.03, 0.23, -0.51]\n Course: [0.0, -2.99, 0.0, 1.69, 0.4, 0.38, 1.25]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/1c6e07d1-326ec05e_18092.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.77, 0.95, 1.11, 1.2, 1.11, 1.21, 1.68]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.05, 0.16, 0.15, 0.01, 0.16, 0.35, 0.57]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: to pass the slower moving traffic in the left.\n\n",
+    "BDDX_Processed/video/1236ceb8-804401af_12826.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.49, 3.95, 3.45, 3.12, 3.1, 3.07, 3.39]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.84, -1.39, -0.97, -0.52, -0.09, 0.37, 0.61]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: to allow a pedestrian to cross the road.\n\n",
+    "BDDX_Processed/video/076399f2-d397b12e_07154.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.7, 0.65, 0.36, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.03, -0.16, -0.32, -0.17, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: the car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked\n\n",
+    "BDDX_Processed/video/07184fbb-bb4c67d2_01358.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.22, 0.9, 2.42, 4.15, 5.87, 7.83, 9.17]\n Curvature: [-0.0, -0.0, -0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.56, 1.36, 1.97, 2.28, 2.3, 2.07, 1.65]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned green.\n\n",
+    "BDDX_Processed/video/12ef9094-a52c7627_13149.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.23, 2.0, 1.77, 1.54, 1.35, 1.16, 0.98]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.32, -1.17, -1.03, -0.89, -0.78, -0.76, -0.74]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front of it has stopped.\n\n",
+    "BDDX_Processed/video/1358b742-90ffc3bf_13347.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.65, 5.14, 4.64, 4.13, 3.64, 3.23, 2.82]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.21, -2.1, -1.99, -1.87, -1.77, -1.71, -1.65]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: while drifting to the right behind a stopped car\n\n",
+    "BDDX_Processed/video/0b669851-19477a8c_09330.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.43, 9.4, 9.36, 9.32, 9.27, 9.22, 8.95]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.17, -0.15, -0.12, -0.17, -0.31, -0.43, -0.57]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.06]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/0339a42b-1e75ac9a_00109.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.65, 0.7, 0.75, 0.75, 0.68, 0.61, 0.53]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.13, 0.07, 0.01, -0.05, -0.12, -0.19, -0.23]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car reverses slowly\n\nHuman: Why does the ego car doing this?\nAssistant: to move into a parking spot.\n\n",
+    "BDDX_Processed/video/031637c4-655be58e_04000.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.52, 6.42, 6.32, 6.22, 6.12, 5.93, 5.74]\n Curvature: [-0.02, -0.01, -0.0, 0.01, 0.01, 0.01, 0.01]\n Acceleration: [-0.02, -0.16, -0.31, -0.45, -0.56, -0.61, -0.65]\n Course: [0.0, -0.02, -0.02, -0.02, -0.02, -0.05, -0.05]\nWhat is the action of ego car?\nAssistant: The car slows slightly\n\nHuman: Why does the ego car doing this?\nAssistant: to go over a bump.\n\n",
+    "BDDX_Processed/video/0c91b387-772700c3_09933.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.14, 11.42, 11.27, 10.73, 10.63, 11.26, 13.35]\n Curvature: [0.0, -0.0, 0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.09, 0.15, -0.17, -0.35, 0.2, 0.64, 0.64]\n Course: [0.0, 0.0, 0.0, 0.0, 0.42, 0.0, -0.38]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic lights are green and there are no cars ahead of it.\n\n",
+    "BDDX_Processed/video/0104eb19-718b1a9c_02603.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.65, 17.54, 17.44, 17.35, 17.27, 17.19, 17.21]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.18, -0.2, -0.22, -0.2, -0.14, -0.06, -0.02]\n Course: [0.0, -0.04, -0.04, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is merging into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: after it has passed the slower vehicle.\n\n",
+    "BDDX_Processed/video/025b2ded-1b0c1607_05108.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.42, 4.78, 5.17, 5.66, 6.44, 7.16, 7.95]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.01]\n Acceleration: [0.93, 1.12, 1.32, 1.5, 1.71, 1.91, 1.89]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates and merges left\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane ahead is ending and the left lane is clear.\n\n",
+    "BDDX_Processed/video/0d0324f9-835b0ca6_10271.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.4, 10.77, 10.4, 9.94, 10.74, 10.48, 9.98]\n Curvature: [0.0, 0.0, -0.0, 0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.14, 0.03, -0.22, 0.06, 0.17, -0.3, 0.34]\n Course: [0.0, 0.0, 0.0, -0.03, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives down a street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road ahead is clear.\n\n",
+    "BDDX_Processed/video/0c6c9c66-5e86e200_09874.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.87, 15.8, 15.73, 15.61, 15.49, 15.42, 15.3]\n Curvature: [0.0, 0.0, 0.01, 0.01, 0.0, 0.01, 0.01]\n Acceleration: [-0.13, -0.16, -0.19, -0.19, -0.2, -0.28, -0.4]\n Course: [0.0, 0.0, -0.01, -0.04, -0.11, -0.14, -0.09]\nWhat is the action of ego car?\nAssistant: The car is driving very quickly in the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane has no traffic.\n\n",
+    "BDDX_Processed/video/0092e44c-3b3dee4b_01882.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.18, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.15, -0.13, -0.11, -0.09, -0.07, -0.05, -0.03]\n Acceleration: [-0.23, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.11, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car has its wipers on while stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as it sits at a red light in the rain.\n\n",
+    "BDDX_Processed/video/0672c229-4d6e08a2_06596.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.94, 6.83, 6.73, 6.62, 6.52, 6.56, 6.58]\n Curvature: [-0.75, -0.55, -0.38, -0.18, -0.0, -0.0, -0.0]\n Acceleration: [-0.25, -0.22, -0.2, -0.17, -0.13, 0.03, 0.18]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: as there are no nearby cars.\n\n",
+    "BDDX_Processed/video/06e4a00e-9ed56776_06789.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.56, 1.13, 1.38, 1.13, 0.77, 0.48]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.38, 0.64, 0.45, 0.03, -0.38, -0.41, 0.27]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car turns around\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is behind the vehicle.\n\n",
+    "BDDX_Processed/video/07ef9f5f-42c0ac31_07352.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.04, -0.04, -0.04, -0.06, -0.08, -0.08, -0.09]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is at a complete stop.\n\nHuman: Why does the ego car doing this?\nAssistant: Because there are cars stopped in front of it.\n\n",
+    "BDDX_Processed/video/0d1914f4-7aafe102_10325.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.03, 5.24, 4.31, 3.32, 2.43, 1.7, 1.14]\n Curvature: [-0.0, 0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.9, -1.22, -1.42, -1.44, -1.22, -0.99, -0.74]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slowly comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is red.\n\n",
+    "BDDX_Processed/video/211918a6-4dbd06ae_20398.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.13, 5.12, 4.51, 3.9, 3.38, 3.02, 2.79]\n Curvature: [0.0, 0.01, 0.01, 0.02, -0.01, 0.0, -0.02]\n Acceleration: [0.04, -0.2, -0.53, -0.41, -0.33, -0.26, -0.04]\n Course: [0.0, -0.23, -0.23, -0.2, -0.05, -0.1, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving down the fast lane of the freeway\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic allows it\n\n",
+    "BDDX_Processed/video/028b5d16-af6a6275_02055.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.78, 8.89, 8.0, 7.12, 6.37, 5.74, 5.11]\n Curvature: [-0.0, -0.04, -0.09, -0.13, -0.18, -0.27, -0.37]\n Acceleration: [-3.05, -3.01, -2.96, -2.92, -2.86, -2.64, -2.42]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: The car is preparing to make a right hand turn.\n\n",
+    "BDDX_Processed/video/0fcfcf5e-7bfac95b_11555.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red and pedestrians are crossing the street.\n\n",
+    "BDDX_Processed/video/0fb86701-19f58a57_11503.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.7, 1.9, 1.16, 0.9, 0.7, 1.65, 4.01]\n Curvature: [0.0, -0.0, 0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.3, -0.4, -0.48, 0.35, -0.3, 1.35, -0.16]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving slowly\n\nHuman: Why does the ego car doing this?\nAssistant: as there is a slow-moving garbage truck ahead.\n\n",
+    "BDDX_Processed/video/0d13ae3d-3ac8a40d_10312.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.29, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.01, -0.02, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/00091078-7cff8ea6_00145.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.14]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.05, -0.08, 0.1]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/2090b4bf-44dbec3d_20158.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.62, 1.28, 2.39, 3.47, 4.72, 5.35]\n Curvature: [-0.3, -0.14, -0.18, -0.15, 0.02, 0.02, 0.02]\n Acceleration: [0.28, 0.52, 0.78, 0.95, 0.99, 0.82, 0.2]\n Course: [0.0, 1.92, 3.88, 6.94, 5.81, 0.66, -1.05]\nWhat is the action of ego car?\nAssistant: The car turns right and goes down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the obstructing SUV has moved down the road as well.\n\n",
+    "BDDX_Processed/video/1c4bf60c-0a6d4747_17998.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.21, 11.34, 12.32, 13.0, 13.09, 12.97, 12.88]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.67, 1.02, 0.81, 0.37, -0.02, -0.1, -0.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car proceeds down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/10af5310-38cadf93_11894.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.68, 5.24, 4.59, 3.76, 3.02, 2.31, 1.72]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.7, -0.89, -1.07, -1.21, -1.12, -0.79, -0.13]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: as there is traffic stopped ahead.\n\n",
+    "BDDX_Processed/video/1bf71c4f-8a713c58_17880.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.13, 0.55, 2.69, 3.4, 3.19, 3.02, 3.2]\n Curvature: [-0.0, -0.26, -0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [-0.77, 0.48, 0.89, 0.06, -0.1, -0.02, 0.15]\n Course: [0.0, -32.25, 0.8, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in front of it moves slowly during a green light.\n\n",
+    "BDDX_Processed/video/0e8235ce-155d45b8_11058.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.96, 4.57, 4.19, 3.8, 3.5, 3.43, 3.36]\n Curvature: [0.05, 0.04, 0.04, 0.03, 0.02, 0.02, 0.01]\n Acceleration: [-1.44, -1.28, -1.13, -0.98, -0.85, -0.7, -0.54]\n Course: [0.0, -0.23, -0.23, -0.23, -0.2, -0.41, -0.41]\nWhat is the action of ego car?\nAssistant: The car is slowing and merging into the lane to its left\n\nHuman: Why does the ego car doing this?\nAssistant: because a vehicle is stopped in its lane ahead.\n\n",
+    "BDDX_Processed/video/09ae119b-f7f6ef39_08449.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [22.56, 23.24, 23.45, 23.08, 22.35, 21.27, 20.21]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.02, 0.02, 0.01]\n Acceleration: [0.58, 0.37, -0.08, -0.44, -0.71, -0.82, -0.95]\n Course: [0.0, -0.11, 0.0, 0.11, -0.57, -0.79, -0.5]\nWhat is the action of ego car?\nAssistant: the car merges over two lanes to the right\n\nHuman: Why does the ego car doing this?\nAssistant: because the right lane is open\n\n",
+    "BDDX_Processed/video/1522a344-d97c2885_14285.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.47, 0.29, 0.11, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.52, -0.41, -0.3, -0.2, -0.1, -0.01, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because it is turning right but there is cross traffic.\n\n",
+    "BDDX_Processed/video/1f1a34a0-56845b24_19367.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.62, 2.41, 4.25, 5.82, 7.29, 8.72, 10.1]\n Curvature: [0.2, -0.02, -0.0, 0.01, -0.0, -0.0, 0.02]\n Acceleration: [1.34, 1.92, 1.87, 1.69, 1.61, 1.54, 1.05]\n Course: [0.0, -0.2, 1.07, 0.14, -0.4, 0.07, -0.16]\nWhat is the action of ego car?\nAssistant: The car begins accelerating and moves one lane to the right.\n\nHuman: Why does the ego car doing this?\nAssistant: Because the light has turned green and the lanes are clear.\n\n",
+    "BDDX_Processed/video/14540082-0de5f53a_13872.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.9, 1.3, 2.11, 3.66, 5.69, 7.27, 8.56]\n Curvature: [-0.06, 0.02, -0.04, -0.0, 0.01, -0.01, -0.0]\n Acceleration: [-0.05, 0.49, 0.69, 1.18, 1.19, 0.95, 0.61]\n Course: [0.0, -0.14, -0.1, -0.54, -0.61, 0.7, -0.07]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving forward\n\n",
+    "BDDX_Processed/video/15f559a7-ab6e9739_14857.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.01, 0.31, 1.44, 2.32, 2.88, 3.13]\n Curvature: [0.2, 0.2, -0.01, -0.03, -0.01, -0.01, 0.03]\n Acceleration: [-0.09, 0.14, 0.7, 0.96, 0.69, 0.4, 0.14]\n Course: [0.0, 0.0, 0.0, 0.07, 0.15, 0.09, 0.09]\nWhat is the action of ego car?\nAssistant: The car begins to accelerate but slows\n\nHuman: Why does the ego car doing this?\nAssistant: since the car to the right has other cars parked in it's lane.\n\n",
+    "BDDX_Processed/video/17c78228-2fc1fc6f_15855.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.7, 5.32, 2.82, 0.46, 0.34, 0.49, 0.61]\n Curvature: [-0.02, -0.03, 0.02, 0.12, -0.14, -0.28, -0.11]\n Acceleration: [-3.71, -3.73, -3.15, -1.88, -0.64, 0.19, 0.13]\n Course: [0.0, 0.05, 0.0, 0.0, -0.05, 0.06, 0.06]\nWhat is the action of ego car?\nAssistant: The car remains stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is still at the stop sign.\n\n",
+    "BDDX_Processed/video/0451ce55-c7cdc6f2_02837.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.45, 3.73, 4.73, 5.93, 6.7, 7.31, 7.93]\n Curvature: [0.04, 0.01, -0.0, 0.01, 0.01, -0.0, -0.01]\n Acceleration: [0.84, 1.21, 1.23, 1.06, 0.79, 0.68, 0.86]\n Course: [0.0, 0.0, 0.21, 0.08, 0.08, -0.08, -0.07]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: after traffic starts accelerating.\n\n",
+    "BDDX_Processed/video/009215e2-d6d2d61b_04521.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.6, 13.09, 12.67, 12.67, 13.16, 13.82, 14.43]\n Curvature: [-0.07, -0.07, -0.09, -0.12, -0.1, -0.12, -0.07]\n Acceleration: [-0.26, 0.05, -0.28, 0.21, 0.34, 0.26, -0.14]\n Course: [0.0, 5.53, 9.1, 170.68, 8.29, 6.75, 4.9]\nWhat is the action of ego car?\nAssistant: The car takes a right exit and continues to move forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear.\n\n",
+    "BDDX_Processed/video/021c4a16-1c50e818_00824.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.47, 6.79, 7.07, 7.4, 7.71, 7.98, 8.21]\n Curvature: [-0.47, -0.08, 0.27, 0.66, 0.98, 0.7, 0.47]\n Acceleration: [1.07, 1.08, 1.09, 1.09, 1.1, 1.06, 1.03]\n Course: [0.0, -2.09, -1.05, -2.09, -1.05, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear.\n\n",
+    "BDDX_Processed/video/1e9e8d92-69576065_19181.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.34, 12.7, 12.11, 10.77, 8.67, 6.38, 5.84]\n Curvature: [0.0, -0.01, -0.03, -0.03, -0.03, -0.02, 0.01]\n Acceleration: [-0.82, -1.96, -3.01, -3.58, -3.37, -3.15, -1.92]\n Course: [0.0, -0.04, -0.04, 0.11, 0.07, 0.11, 0.14]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because a van encroaches on its lane.\n\n",
+    "BDDX_Processed/video/0d82a02d-60a4654b_10565.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.66, 0.73, 0.8, 0.87, 0.96, 1.19, 1.4]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.26, 0.34, 0.42, 0.5, 0.59, 0.68, 0.76]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in its lane moves forward.\n\n",
+    "BDDX_Processed/video/1a0524a4-076e31e9_16900.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.79, 1.97, 8.52, 9.99, 11.29, 11.79, 11.88]\n Curvature: [0.0, -0.0, -0.0, 0.0, -0.0, 0.0, 0.01]\n Acceleration: [0.44, 2.66, 2.48, 0.85, 0.58, 0.1, 0.26]\n Course: [0.0, 0.0, 0.19, -0.05, 0.0, -0.21, -0.52]\nWhat is the action of ego car?\nAssistant: The car is heading down the street at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/06924f46-7f03bcdc_06645.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.93, 10.19, 9.44, 8.7, 8.04, 7.27, 6.5]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-2.97, -2.95, -2.93, -2.91, -2.85, -2.53, -2.2]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because they want to make a turn.\n\n",
+    "BDDX_Processed/video/11659c5d-2b5fe71d_12500.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.11, 10.11, 10.11, 10.12, 10.06, 9.55, 9.11]\n Curvature: [0.01, 0.01, 0.01, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.75, 0.3, -0.1, -0.55, -1.0, -1.46, -1.86]\n Course: [0.0, 0.0, 0.0, 0.0, -0.03, -0.06, -0.03]\nWhat is the action of ego car?\nAssistant: The car drives in the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: as it goes around the truck\n\n",
+    "BDDX_Processed/video/1af21ce1-633a8e8f_17494.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [26.89, 25.47, 17.82, 15.64, 12.72, 10.93, 11.05]\n Curvature: [-0.0, -0.02, 0.03, -0.01, 0.0, 0.02, -0.01]\n Acceleration: [-0.93, -2.32, -4.63, -1.43, -1.81, -0.44, 0.18]\n Course: [0.0, 0.0, 0.77, 0.51, 0.47, 0.23, -0.26]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic is slowing down.\n\n",
+    "BDDX_Processed/video/006a7635-ec8fe02c_04436.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.65, 1.51, 1.86, 2.35, 2.84, 3.25, 3.46]\n Curvature: [0.0, 0.0, 0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.33, 0.1, 0.43, 0.53, 0.5, 0.35, 0.21]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: since traffic has started to move\n\n",
+    "BDDX_Processed/video/1a0524a4-076e31e9_16904.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.55, 3.87, 4.16, 4.53, 4.99, 5.45, 5.86]\n Curvature: [-0.22, -0.24, -0.25, -0.23, -0.18, -0.13, -0.13]\n Acceleration: [0.96, 0.96, 0.97, 1.0, 1.07, 1.14, 1.13]\n Course: [0.0, 0.0, 0.0, 1.52, 1.52, 1.52, 2.01]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning right.\n\n",
+    "BDDX_Processed/video/0738f42e-fe0d895e_07079.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.11, 6.83, 6.57, 6.63, 7.18, 7.72, 8.05]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.06, -0.5, 0.01, 0.46, 0.78, 1.1, 1.18]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down and merges right\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a vehicle blocking the lane ahead and there is a spot open in the right lane.\n\n",
+    "BDDX_Processed/video/0502de3f-c9529246_06188.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]\n Acceleration: [-0.09, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, -0.09, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops at the intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red\n\n",
+    "BDDX_Processed/video/1af0ed08-f99421ec_17488.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.87, 6.72, 6.58, 6.43, 6.3, 5.99, 5.68]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.38, -0.5, -0.63, -0.76, -0.87, -0.97, -1.06]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: because it is approaching traffic stopped at a red light\n\n",
+    "BDDX_Processed/video/034ebec5-b2b76478_00216.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.51, 6.36, 6.2, 6.01, 5.78, 5.54, 5.48]\n Curvature: [-0.08, -0.05, -0.02, 0.01, 0.02, 0.03, 0.02]\n Acceleration: [-0.53, -0.53, -0.52, -0.48, -0.41, -0.33, -0.3]\n Course: [0.0, 0.14, 0.14, 0.1, 0.07, 0.1, -0.11]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: while passing a stopped vehicle\n\n",
+    "BDDX_Processed/video/160a76cc-b5993b9d_14889.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.95, 6.89, 7.66, 6.79, 5.95, 4.56, 3.39]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.1, 1.77, 2.76]\n Acceleration: [2.26, 1.08, -0.03, -1.16, -2.12, -1.75, -1.3]\n Course: [0.0, -0.05, -0.04, 0.0, 0.0, 0.0, -6.04]\nWhat is the action of ego car?\nAssistant: The car accelerates quickly\n\nHuman: Why does the ego car doing this?\nAssistant: to turn left into a gas station.\n\n",
+    "BDDX_Processed/video/021c4a16-1c50e818_04992.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.77, 9.72, 9.81, 9.81, 9.53, 8.57, 7.5]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.06, 0.03, 0.01, -0.26, -0.8, -1.23, -1.31]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/07bfc402-10ede36e_03219.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.16, 0.0, 0.35, 0.0, 0.0, 0.0, 0.0]\n Curvature: [1.51, 1.52, 2.85, 4.42, 4.44, 4.46, 4.48]\n Acceleration: [-1.33, 0.03, 0.06, -0.08, 0.0, 0.0, 0.0]\n Course: [0.0, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red\n\n",
+    "BDDX_Processed/video/07e9e760-687a2c40_03616.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.44, 9.88, 9.39, 9.6, 11.45, 11.53, 11.81]\n Curvature: [0.0, -0.0, -0.01, -0.03, -0.02, -0.02, -0.0]\n Acceleration: [-0.37, -0.32, -0.16, 1.07, 0.31, 0.1, -0.02]\n Course: [0.0, -0.0, 0.53, 1.33, 1.51, 0.17, 0.86]\nWhat is the action of ego car?\nAssistant: The car continues down the slightly curved road\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead is moving.\n\n",
+    "BDDX_Processed/video/11c8017c-740c9045_12689.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.07, 5.93, 7.19, 8.0, 9.45, 10.98, 12.57]\n Curvature: [0.0, 0.02, 0.02, -0.05, 0.01, 0.01, -0.01]\n Acceleration: [1.19, 1.01, 0.26, 0.62, 0.58, 0.73, 0.69]\n Course: [0.0, 0.51, 0.0, 1.2, 0.0, 0.0, -0.21]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear and traffic is moving.\n\n",
+    "BDDX_Processed/video/05acab14-b78b76c7_06381.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.78, 0.93, 0.6, 0.99, 1.5, 1.72, 1.93]\n Curvature: [0.72, 0.28, 0.34, 0.0, -0.33, -0.02, 0.02]\n Acceleration: [0.39, -0.1, 0.03, 0.44, 0.35, 0.22, -0.24]\n Course: [0.0, -0.08, -0.02, -0.09, -0.04, -0.12, -0.05]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped in front.\n\n",
+    "BDDX_Processed/video/04904d8d-0ab5c90a_03496.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.68, 8.53, 8.41, 8.26, 8.05, 7.24, 6.44]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.79, -1.06, -1.3, -1.57, -1.79, -1.89, -2.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: to make a right hand turn.\n\n",
+    "BDDX_Processed/video/15e7be3e-6bad38ac_14757.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.29, 3.53, 7.71, 9.28, 8.88, 6.07, 8.59]\n Curvature: [0.0, -0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.86, 0.68, 1.4, 0.12, -0.8, -0.06, 0.99]\n Course: [0.0, 0.0, 0.0, -0.1, 0.0, 0.0, 0.56]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road going the same speed in the same lane in the city at night.\n\nHuman: Why does the ego car doing this?\nAssistant: Because there is no traffic and the lane is clear.\n\n",
+    "BDDX_Processed/video/02ff80b4-9b422cbd_05425.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.95, 17.34, 17.68, 18.07, 18.48, 19.0, 19.45]\n Curvature: [0.0, -0.0, -0.0, -0.01, -0.01, -0.0, 0.0]\n Acceleration: [1.53, 1.58, 1.62, 1.67, 1.7, 1.65, 1.61]\n Course: [0.0, -0.12, -0.06, -0.12, -0.04, 0.05, 0.02]\nWhat is the action of ego car?\nAssistant: The car is merging right\n\nHuman: Why does the ego car doing this?\nAssistant: to get around slow moving traffic\n\n",
+    "BDDX_Processed/video/115d9e12-8a9ad671_12475.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.94, 5.78, 6.54, 7.35, 8.13, 8.9, 9.7]\n Curvature: [0.8, 0.56, 0.33, 0.15, 0.08, 0.01, -0.0]\n Acceleration: [2.12, 2.09, 2.06, 2.06, 2.08, 2.11, 2.05]\n Course: [0.0, -4.96, -3.3, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's making a left turn.\n\n",
+    "BDDX_Processed/video/1458e4ad-f3ce8d5e_13920.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.04, 2.25, 3.02, 4.2, 5.23, 5.75, 5.84]\n Curvature: [-0.0, 0.0, -0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.14, 0.53, 1.06, 1.19, 0.85, 0.36, -0.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward at a moderate speed\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in front of it moves more quickly.\n\n",
+    "BDDX_Processed/video/190bb329-2d95a35f_16543.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.26, 1.46, 3.08, 5.16, 6.78, 7.31, 7.01]\n Curvature: [0.04, -0.08, 0.01, -0.01, -0.0, 0.0, -0.0]\n Acceleration: [-0.05, 0.79, 1.19, 1.1, 0.69, -0.03, -0.05]\n Course: [0.0, -1.83, 1.86, 0.4, -0.15, 0.15, 0.05]\nWhat is the action of ego car?\nAssistant: The car pulls to the left and accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: to avoid the walking cyclist.\n\n",
+    "BDDX_Processed/video/0fa741ce-76dbff13_11492.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.07, -0.23, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked.\n\n",
+    "BDDX_Processed/video/0070bc56-401304be_04454.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.84, 8.31, 8.78, 9.12, 9.32, 9.53, 9.82]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.14, 1.03, 0.92, 0.83, 0.76, 0.68, 0.55]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating faster\n\nHuman: Why does the ego car doing this?\nAssistant: As the pedestrians that were crossing the street have now cleared out of the way and there are no cars in front of it.\n\n",
+    "BDDX_Processed/video/1d6ad9a0-a8974645_18569.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.05, 12.13, 12.21, 12.26, 12.26, 12.26, 11.64]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.49, 0.34, 0.2, -0.05, -0.51, -0.96, -1.32]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges to the right\n\nHuman: Why does the ego car doing this?\nAssistant: as it changes laness\n\n",
+    "BDDX_Processed/video/1aaf831d-1c50cde1_17296.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.72, 6.09, 5.46, 4.82, 4.22, 3.81, 3.39]\n Curvature: [0.03, 0.02, 0.02, 0.02, 0.02, 0.01, 0.0]\n Acceleration: [-2.71, -2.53, -2.36, -2.18, -1.97, -1.54, -1.11]\n Course: [0.0, -0.05, -0.05, -0.05, -0.03, -0.02, -0.02]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because there's a stop sign.\n\n",
+    "BDDX_Processed/video/009215e2-8500bd0b_01803.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.17, 7.59, 7.01, 6.39, 5.8, 5.51, 5.23]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-1.12, -1.14, -1.16, -1.01, -0.86, -0.69, -0.55]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is proceeding slowly down city street\n\nHuman: Why does the ego car doing this?\nAssistant: as there is a stopped car ahead.\n\n",
+    "BDDX_Processed/video/0a248ce2-5111a890_08680.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.07, 5.04, 5.86, 8.78, 9.55, 9.94, 10.43]\n Curvature: [0.0, -0.01, -0.01, 0.0, 0.01, 0.0, 0.0]\n Acceleration: [-0.15, -0.29, 1.45, 0.56, 0.32, 0.2, 0.32]\n Course: [0.0, 0.0, 0.16, -0.33, 0.33, 0.49, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is driving at a normal rate of speed.\n\n",
+    "BDDX_Processed/video/0dcd165f-6cc8189f_10684.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.46, 8.8, 8.23, 7.57, 6.92, 6.37, 5.89]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.14, -2.17, -2.21, -2.24, -2.27, -2.27, -2.27]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: since it is approaching a red light\n\n",
+    "BDDX_Processed/video/0478d053-f5bb4831_06017.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.08, 0.16, 0.24, 0.3, 0.22, 0.14]\n Curvature: [0.14, 0.14, 0.13, 0.13, 0.13, 0.13, 0.13]\n Acceleration: [0.31, 0.23, 0.15, 0.07, -0.0, -0.05, -0.09]\n Course: [0.0, 0.02, 0.02, 0.02, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped on a single lane road\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic directly ahead is stopped.\n\n",
+    "BDDX_Processed/video/015fe6c9-e880c0d7_04757.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.62, 16.77, 16.92, 17.03, 17.11, 17.04, 16.95]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.37, 0.31, 0.24, 0.13, 0.02, -0.12, -0.23]\n Course: [0.0, 0.0, 0.0, 0.0, -0.02, -0.04, -0.02]\nWhat is the action of ego car?\nAssistant: The car merges right\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is seen and the car can safely move onward.\n\n",
+    "BDDX_Processed/video/1f77fb7b-d1d8dfee_19617.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.45, 3.19, 5.14, 7.37, 8.25, 7.56, 5.67]\n Curvature: [0.06, -0.08, -0.04, -0.01, 0.02, -0.01, -0.01]\n Acceleration: [0.37, 0.91, 1.64, 1.13, 0.09, -1.16, -1.12]\n Course: [0.0, 1.87, 7.36, -0.06, 0.25, -0.23, 0.12]\nWhat is the action of ego car?\nAssistant: The car pulls forward onto the street it was on before and continues\n\nHuman: Why does the ego car doing this?\nAssistant: because the intersection and street are free of traffic.\n\n",
+    "BDDX_Processed/video/17dd182d-1df6245c_15909.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is reversing and turning to the right\n\nHuman: Why does the ego car doing this?\nAssistant: because they are trying to get their front end closer to the curb\n\n",
+    "BDDX_Processed/video/0c9f5bb9-4d89a4ed_09956.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: while the driver of the car that was almost hit is impeding their path.\n\n",
+    "BDDX_Processed/video/1cc77c32-a2cb4cb9_18229.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.97, 3.17, 2.43, 1.73, 1.16, 0.6, 0.36]\n Curvature: [0.02, -0.22, -0.44, -0.61, -0.66, -0.72, -0.73]\n Acceleration: [-2.01, -1.9, -1.8, -1.61, -1.32, -1.03, -0.76]\n Course: [0.0, -0.04, -0.02, -0.03, -0.03, -0.03, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is red\n\n",
+    "BDDX_Processed/video/2168a252-c052da7a_20512.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.14, 0.01]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.06, 0.02, 0.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is stuck behind a stopped truck.\n\n",
+    "BDDX_Processed/video/09ac7416-ea7be0e4_08410.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is red\n\n",
+    "BDDX_Processed/video/04629c39-1d564d8a_03061.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 1.25, 2.07, 2.61, 2.8, 3.27, 4.82]\n Curvature: [0.0, 0.0, 0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.47, 0.74, 0.48, 0.24, 0.19, 0.79, 0.88]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving forward with its wipers on\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic moves forward in the rain.\n\n",
+    "BDDX_Processed/video/1e3c65ba-fd167f4c_19010.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.04, 0.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is waiting\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has been red\n\n",
+    "BDDX_Processed/video/0c28dcae-c3c1e002_09748.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.34, 7.36, 7.28, 5.91, 4.41, 2.72, 1.19]\n Curvature: [0.03, 0.01, -0.01, -0.01, -0.01, -0.03, -0.07]\n Acceleration: [-0.19, -0.77, -1.41, -2.26, -2.99, -2.47, -1.88]\n Course: [0.0, -0.6, -0.54, 0.18, 0.21, 0.14, 0.08]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light ahead is red.\n\n",
+    "BDDX_Processed/video/0ac3cbf4-ce89456d_09048.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.31, 10.77, 11.24, 11.56, 11.72, 11.89, 11.9]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.72, 1.36, 0.99, 0.69, 0.47, 0.23, 0.11]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no impediments in its lane.\n\n",
+    "BDDX_Processed/video/146b8d6e-8f069566_13939.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.2, 2.02, 1.87, 1.69, 1.48, 1.12, 0.8]\n Curvature: [-0.03, -0.04, -0.04, -0.05, -0.07, -0.1, -0.13]\n Acceleration: [-0.22, -0.43, -0.62, -0.83, -1.01, -0.94, -0.88]\n Course: [0.0, 0.0, 0.0, 0.0, -0.02, -0.05, -0.02]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead has stopped.\n\n",
+    "BDDX_Processed/video/097450c5-87ec5fcf_08188.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.76, 8.83, 8.02, 7.09, 6.14, 5.01, 4.01]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.83, -3.11, -3.35, -3.62, -3.84, -3.65, -3.48]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car decelerates and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: since the traffic light ahead turns red.\n\n",
+    "BDDX_Processed/video/0252f2b5-2e69185a_01433.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.88, 4.06, 3.77, 3.88, 3.76, 3.21, 3.39]\n Curvature: [-0.28, 0.0, 0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.6, -0.35, -0.09, 0.17, -0.31, -0.05, 0.03]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: The car has completed its turn and traffic ahead of the car is moving slowly.\n\n",
+    "BDDX_Processed/video/043026cb-f72901db_02530.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.6, 18.37, 18.42, 18.28, 18.18, 16.89, 16.63]\n Curvature: [0.0, -0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.19, -0.16, 0.02, 0.01, -0.34, -0.27, -0.12]\n Course: [0.0, 0.2, -0.06, 0.0, -0.06, 0.0, 0.04]\nWhat is the action of ego car?\nAssistant: The car is driving forward with its wipers on intermittently\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green and it is raining.\n\n",
+    "BDDX_Processed/video/1e6bdf2e-eef84253_19096.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.64, 4.95, 3.39, 1.87, 0.77, 1.83, 2.91]\n Curvature: [0.08, 0.0, -0.06, -0.03, 0.02, 0.1, 0.16]\n Acceleration: [-2.96, -2.98, -2.91, -1.54, -0.26, 1.09, 2.11]\n Course: [0.0, -0.84, -0.68, -0.19, -0.19, -0.28, -1.5]\nWhat is the action of ego car?\nAssistant: The car slowed down to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: because it approached a stop sign.\n\n",
+    "BDDX_Processed/video/1458e4ad-f3ce8d5e_13924.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.32, 5.62, 5.91, 6.21, 6.46, 6.67, 6.87]\n Curvature: [-0.02, -0.02, -0.01, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.58, 0.68, 0.78, 0.88, 0.94, 0.75, 0.56]\n Course: [0.0, 0.18, 0.18, 0.18, -0.0, -0.0, -0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as the car partially in its lane speeds up.\n\n",
+    "BDDX_Processed/video/101c9c8b-738ae7ec_11727.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.9, 6.53, 6.16, 5.79, 5.4, 4.8, 4.2]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.48, -1.58, -1.68, -1.78, -1.88, -2.0, -2.12]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down.\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/02346f91-18e2acf4_05030.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.69, 1.39, 2.15, 3.05, 3.66, 3.22, 3.04]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.46, 0.56, 0.66, 0.63, 0.07, -0.28, 0.1]\n Course: [0.0, 0.0, 0.0, 0.09, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates slowly down the road\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead begins to move.\n\n",
+    "BDDX_Processed/video/0379e014-116dc5cd_00552.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.87, 2.93, 1.54, 0.94, 0.46, 0.24, 0.32]\n Curvature: [-0.01, 0.02, -0.05, 0.01, -0.06, -0.1, -0.1]\n Acceleration: [-1.92, -1.58, -0.38, -0.36, -0.24, -0.0, -0.04]\n Course: [0.0, 0.14, 0.0, 0.14, 0.07, 0.0, 0.03]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red\n\n",
+    "BDDX_Processed/video/043026cb-3a39d0b7_05880.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.41, 13.49, 13.84, 14.16, 14.45, 14.43, 14.94]\n Curvature: [-0.04, 0.02, -0.08, 0.0, 0.0, -0.01, -0.01]\n Acceleration: [1.03, 0.13, 0.26, 0.23, 0.07, 0.18, -0.43]\n Course: [0.0, 0.64, 0.27, 0.4, 0.0, 0.09, 0.38]\nWhat is the action of ego car?\nAssistant: The car is driving forward with its wipers on\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green and it is raining.\n\n",
+    "BDDX_Processed/video/03fd9aa1-78e4c146_01846.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.83, 9.94, 10.47, 10.61, 9.93, 9.37, 9.65]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.16, 0.34, 0.1, -0.07, -0.37, -0.0, 0.09]\n Course: [0.0, 0.0, -0.1, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car maintains a constant speed and lane\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/031637c4-1d39b9e6_03989.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.85, 1.75, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-3.82, -0.07, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.48, -0.03, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.35, 0.35, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/06c2ee7b-b7b17d24_06686.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.72, 10.27, 10.73, 10.69, 9.95, 8.22, 5.88]\n Curvature: [0.0, 0.0, 0.01, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.35, 0.34, 0.13, -0.2, -0.82, -1.33, -1.69]\n Course: [0.0, 0.0, -0.14, -0.24, -0.54, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0f4fb685-0cc41310_11386.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.72, 8.83, 8.06, 7.17, 6.28, 5.36, 4.54]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.89, -3.03, -3.14, -3.28, -3.39, -3.31, -3.24]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because it needs to back up.\n\n",
+    "BDDX_Processed/video/0acc0c71-fb703219_09073.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.76, 6.93, 7.11, 7.38, 7.68, 8.2, 8.59]\n Curvature: [0.01, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.2, 0.32, 0.44, 0.61, 0.74, 0.52, 0.29]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03]\nWhat is the action of ego car?\nAssistant: The car drifts back to the right\n\nHuman: Why does the ego car doing this?\nAssistant: To get back into its own lane and the correct side of the street after it passed up the utility truck.\n\n",
+    "BDDX_Processed/video/0cdde16a-e2d9677e_10120.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.03, 11.43, 12.8, 13.88, 14.35, 14.79, 15.16]\n Curvature: [-0.06, -0.09, -0.09, -0.1, -0.12, -0.07, -0.03]\n Acceleration: [1.16, 1.07, 0.99, 0.55, 0.35, 0.3, 0.33]\n Course: [0.0, 4.2, 4.22, 4.22, 4.51, 4.57, 1.37]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no other cars on the road.\n\n",
+    "BDDX_Processed/video/016c5aac-f5e49c84_03691.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.16, 14.12, 14.08, 14.1, 14.06, 13.69, 13.32]\n Curvature: [-0.01, -0.01, -0.0, -0.01, -0.02, 0.01, 0.02]\n Acceleration: [0.08, 0.03, -0.04, -0.2, -0.37, -0.57, -0.79]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is merging into the lane to its right\n\nHuman: Why does the ego car doing this?\nAssistant: due to the lane being free of cars.\n\n",
+    "BDDX_Processed/video/07797fc1-3f6aee10_07178.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [-0.2, 0.0, 0.0, 0.23, 0.07, 0.0, -0.31]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: for the red light.\n\n",
+    "BDDX_Processed/video/208caf98-7316a867_20156.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.65, 7.74, 7.48, 8.84, 9.39, 8.79, 7.85]\n Curvature: [0.0, -0.0, 0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [1.17, -0.26, 0.51, 0.56, -0.05, -0.49, -0.47]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the is a safe distance between it and the car in front of it.\n\n",
+    "BDDX_Processed/video/10af6ba8-47f7bd63_11910.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.65, 4.93, 3.81, 2.59, 1.6, 0.65, 0.3]\n Curvature: [0.02, 0.01, -0.05, -0.03, 0.11, 0.17, 0.12]\n Acceleration: [-0.91, -1.22, -1.41, -1.42, -1.22, -0.83, -0.22]\n Course: [0.0, -0.28, -0.23, -0.07, 0.0, 0.07, -0.06]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as there is a stop sign ahead.\n\n",
+    "BDDX_Processed/video/07184fbb-5d195420_01275.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.89, 7.01, 9.53, 10.68, 9.48, 9.01, 9.03]\n Curvature: [0.12, -0.01, 0.0, 0.0, -0.0, 0.0, -0.0]\n Acceleration: [1.33, 1.66, 1.51, -0.14, -0.64, 0.1, -0.59]\n Course: [0.0, 0.0, 0.0, -0.06, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: The car has completed its street change maneuver.\n\n",
+    "BDDX_Processed/video/0336de82-802d2437_05505.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.4, 0.98, 0.55, 0.05, 0.11, 0.15, 0.02]\n Curvature: [0.25, 0.09, 0.01, 0.01, 0.01, 0.0, 0.0]\n Acceleration: [-0.66, -0.69, -0.58, -0.31, -0.12, -0.02, -0.08]\n Course: [0.0, -0.26, 0.0, 0.0, -0.13, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a slow stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic stopped moving.\n\n",
+    "BDDX_Processed/video/10425a11-c4829447_11773.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.55, 3.22, 2.94, 2.61, 2.27, 1.78, 1.36]\n Curvature: [-0.01, -0.01, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.14, -0.3, -0.69, -1.13, -1.5, -1.38, -1.27]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to an abrupt stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the truck in front of it has stopped suddenly\n\n",
+    "BDDX_Processed/video/04904d8d-0ab5c90a_06064.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.29, 9.09, 9.98, 10.98, 11.57, 11.94, 11.99]\n Curvature: [-0.0, -0.0, -0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.44, 1.43, 1.36, 1.2, 0.77, 0.28, -0.24]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates and drives down the street\n\nHuman: Why does the ego car doing this?\nAssistant: since the road is clear.\n\n",
+    "BDDX_Processed/video/16ee2f26-d1308565_15505.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.75, 9.35, 9.91, 10.41, 10.83, 10.77, 10.74]\n Curvature: [-0.02, -0.01, -0.0, -0.01, -0.01, -0.01, -0.0]\n Acceleration: [1.42, 1.23, 1.05, 0.7, 0.38, 0.18, 0.05]\n Course: [0.0, 0.46, 0.34, 0.0, 0.12, 0.25, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is clear.\n\n",
+    "BDDX_Processed/video/10719793-72719572_11803.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.41, 0.47, 0.52, 0.59, 0.64, 0.67, 0.7]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.32, 0.28, 0.25, 0.2, 0.17, 0.12, 0.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car starts to edge forward and stops again.\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is anticipating the red light changing.\n\n",
+    "BDDX_Processed/video/011aeca1-fe5af665_04669.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.45, 5.27, 4.32, 3.74, 3.13, 2.47, 1.6]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.86, -1.56, -1.24, -0.94, -1.03, -0.94, -0.46]\n Course: [0.0, -0.24, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has stopped.\n\n",
+    "BDDX_Processed/video/029407d6-ca53347d_02149.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.41, 0.7, 0.85, 0.95, 1.64, 2.86]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.14, 0.36, 0.29, 0.26, 0.51, 1.2, 1.38]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is merging to the next lane to the right.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is bypassing the stopped traffic in the left lane.\n\n",
+    "BDDX_Processed/video/09f81455-e194d277_08622.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.69, 2.09, 3.0, 4.55, 5.6, 4.71, 5.59]\n Curvature: [0.02, -0.01, -0.01, 0.0, 0.0, -0.01, 0.0]\n Acceleration: [0.23, 0.54, 1.31, 1.37, 0.29, 0.24, 1.22]\n Course: [0.0, 0.14, 0.0, 0.35, 0.14, -0.07, 0.12]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as it merges with traffic.\n\n",
+    "BDDX_Processed/video/07b584c5-fff3b3bd_03169.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.78, 10.07, 10.09, 9.86, 10.12, 9.59, 9.95]\n Curvature: [0.0, 0.01, 0.03, -0.04, -0.02, -0.05, 0.01]\n Acceleration: [0.45, 0.17, -0.44, 0.1, 0.11, -0.65, 0.65]\n Course: [0.0, -0.88, -1.15, -0.86, -0.9, -0.43, -0.69]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/019c8305-0bb153d0_04150.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [22.03, 22.12, 22.22, 22.23, 22.13, 22.04, 21.98]\n Curvature: [0.01, 0.0, -0.0, -0.01, -0.0, -0.0, -0.0]\n Acceleration: [0.15, 0.09, 0.03, -0.03, -0.12, -0.19, -0.19]\n Course: [0.0, -0.03, -0.03, 0.0, 0.0, 0.0, 0.1]\nWhat is the action of ego car?\nAssistant: The car is merging to the left.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is merging onto the freeway.\n\n",
+    "BDDX_Processed/video/00f42f6b-b1ca1f6c_02494.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.37, 3.5, 2.7, 2.23, 1.78, 1.33, 1.0]\n Curvature: [-0.03, 0.02, 0.07, -0.0, -0.06, -0.0, 0.03]\n Acceleration: [-1.97, -1.6, -1.25, -1.04, -0.84, -0.58, -0.32]\n Course: [0.0, -0.09, -0.07, 0.0, -0.02, -0.05, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: for a parked bus partially in its lane.\n\n",
+    "BDDX_Processed/video/07bfcfae-64af58ec_07284.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.27, 0.48, 0.69, 1.06, 1.56, 2.11, 2.47]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.52, 0.7, 0.88, 1.0, 1.06, 1.12, 1.03]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car inches forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the other lanes are moving but the car ahead of them is still stopped.\n\n",
+    "BDDX_Processed/video/0bb3f3d3-12b80b0b_09535.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.05, 0.06]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is red.\n\n",
+    "BDDX_Processed/video/00d79c0a-a2b85ca4_02338.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.17, 12.44, 12.7, 13.0, 13.36, 13.72, 13.88]\n Curvature: [0.01, -0.01, -0.03, -0.03, -0.01, 0.01, 0.01]\n Acceleration: [0.66, 0.72, 0.77, 0.78, 0.7, 0.63, 0.44]\n Course: [0.0, -0.03, -0.02, -0.04, -0.04, -0.04, 0.07]\nWhat is the action of ego car?\nAssistant: The car veers right\n\nHuman: Why does the ego car doing this?\nAssistant: to merge into the right lane.\n\n",
+    "BDDX_Processed/video/066557f7-f2fe2ec0_06567.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.85, 9.05, 10.12, 10.68, 8.71, 5.52, 2.91]\n Curvature: [0.01, -0.01, -0.01, -0.0, -0.0, 0.0, -0.06]\n Acceleration: [1.34, 1.26, 0.9, -0.82, -2.7, -3.18, -2.51]\n Course: [0.0, -0.07, 0.24, 0.16, 0.07, 0.0, -0.08]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has stopped.\n\n",
+    "BDDX_Processed/video/014070bf-4de0af14_03133.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [25.57, 25.43, 25.29, 25.28, 25.38, 25.49, 25.4]\n Curvature: [-0.01, -0.02, -0.02, -0.02, -0.0, 0.01, -0.01]\n Acceleration: [-0.18, -0.12, -0.06, -0.03, -0.03, -0.03, -0.09]\n Course: [0.0, 0.28, 0.28, 0.2, 0.14, 0.2, 0.17]\nWhat is the action of ego car?\nAssistant: The car merges into the lane to its left\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a slower car in its current lane.\n\n",
+    "BDDX_Processed/video/10c0c72d-4c55f57f_11945.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.15, 9.5, 9.88, 10.05, 10.0, 9.72, 9.38]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.39, 0.4, 0.3, 0.07, -0.19, -0.33, -0.33]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is traveling down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road ahead is clear\n\n",
+    "BDDX_Processed/video/1c1c7174-c95618d4_17937.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.87, 6.42, 6.03, 5.58, 5.13, 4.68, 4.29]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.0, -1.18, -1.34, -1.53, -1.66, -1.44, -1.24]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a stop sign.\n\n",
+    "BDDX_Processed/video/02fc82b3-279906a9_05414.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is at a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light at the intersection is red and there are cars in front of it stopped.\n\n",
+    "BDDX_Processed/video/225dad3c-fd2a7987_21004.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.35]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.01]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.03, 0.24, 0.73]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is halted\n\nHuman: Why does the ego car doing this?\nAssistant: because it is at a red light.\n\n",
+    "BDDX_Processed/video/002b562f-e0ac84fe_04329.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.34, 8.85, 8.41, 7.92, 7.36, 6.36, 5.48]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.76, -2.04, -2.28, -2.56, -2.79, -2.68, -2.59]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: for the stop sign ahead.\n\n",
+    "BDDX_Processed/video/1d97c075-e850faa9_18653.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.91, 7.71, 5.69, 4.2, 2.78, 1.86, 1.14]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-2.51, -3.04, -3.47, -2.85, -2.22, -1.54, -0.88]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a near stop\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a stop sign.\n\n",
+    "BDDX_Processed/video/1aaf831d-c31b9f53_17300.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.04, 9.06, 6.18, 4.16, 3.47, 4.1, 5.21]\n Curvature: [0.0, 0.0, -0.0, -0.14, -0.43, -0.3, -0.1]\n Acceleration: [-3.43, -3.68, -3.01, -1.68, -0.03, 1.01, 1.56]\n Course: [0.0, 0.0, 0.0, 0.0, 8.72, 17.43, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes and turns right\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear to turn.\n\n",
+    "BDDX_Processed/video/03d34d05-4a61174c_05706.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [21.58, 21.61, 21.64, 21.66, 21.69, 21.7, 21.72]\n Curvature: [0.0, 0.01, 0.01, 0.02, 0.03, 0.02, 0.02]\n Acceleration: [0.11, 0.1, 0.1, 0.09, 0.09, 0.21, 0.32]\n Course: [0.0, 0.16, 0.08, 0.16, 0.09, 0.02, 0.01]\nWhat is the action of ego car?\nAssistant: The car is driving straight forward\n\nHuman: Why does the ego car doing this?\nAssistant: as it goes down a road\n\n",
+    "BDDX_Processed/video/015af63d-7af26e37_03299.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.62, 18.75, 18.88, 19.01, 19.13, 19.18, 19.24]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.53, 0.48, 0.44, 0.39, 0.35, 0.32, 0.29]\n Course: [0.0, -0.1, -0.1, -0.1, -0.05, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is merging left\n\nHuman: Why does the ego car doing this?\nAssistant: The car is leaving the merging lane and moving onto a freeway.\n\n",
+    "BDDX_Processed/video/129b62fc-6dcbe269_13004.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.21, 7.26, 7.76, 7.59, 6.9, 6.42, 6.3]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [1.33, 0.95, 0.21, -0.46, -0.74, -0.4, -0.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is going down the road at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/14306a74-b2857ba1_13838.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.08, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, 0.6, 0.6, -0.38, -0.04, 0.25, 0.0]\n Acceleration: [-1.67, -0.74, 0.0, 0.09, -0.08, 0.0, 0.0]\n Course: [0.0, -1.18, -0.33, -0.28, -0.22, -0.16, -0.45]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/1683a857-4bae22fe_15200.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.45, 3.91, 3.38, 3.04, 2.74, 2.69, 2.74]\n Curvature: [-0.01, -0.04, -0.06, -0.05, -0.05, -0.1, -0.14]\n Acceleration: [-1.3, -1.07, -0.83, -0.59, -0.33, 0.02, 0.36]\n Course: [0.0, 0.24, 0.28, 0.29, 0.61, 0.76, 1.78]\nWhat is the action of ego car?\nAssistant: The car makes a right turn\n\nHuman: Why does the ego car doing this?\nAssistant: as there is no other traffic in the intersection.\n\n",
+    "BDDX_Processed/video/02d478d1-9ce5120d_05344.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.94, 11.56, 10.06, 8.7, 7.25, 5.93, 5.41]\n Curvature: [0.0, 0.02, 0.05, 0.07, 0.08, 0.1, 0.07]\n Acceleration: [-3.02, -3.24, -3.48, -3.42, -2.82, -2.27, -1.6]\n Course: [0.0, 0.0, 0.0, -0.99, -1.48, -0.99, -0.76]\nWhat is the action of ego car?\nAssistant: The car makes a left turn\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is green.\n\n",
+    "BDDX_Processed/video/19906a63-e2ba347a_16783.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.22, 5.49, 5.77, 6.04, 6.28, 6.53, 6.77]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.54, 0.65, 0.77, 0.89, 0.99, 0.93, 0.87]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is merging into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: since a car is blocking the right lane\n\n",
+    "BDDX_Processed/video/195ec407-6f35d93a_16673.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.5, 5.27, 5.68, 6.57, 7.85, 7.8, 6.81]\n Curvature: [-0.0, 0.0, -0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.12, 0.57, 0.61, 1.07, 0.61, -0.5, -1.17]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is empty.\n\n",
+    "BDDX_Processed/video/1e1d4587-788bb811_18926.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the driver has parked.\n\n",
+    "BDDX_Processed/video/004071a4-ef4bf541_00871.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.01, 2.69, 3.11, 4.2, 5.95, 7.43, 8.54]\n Curvature: [-0.19, -0.2, -0.19, -0.13, -0.0, 0.02, 0.01]\n Acceleration: [-0.07, 0.2, 0.97, 1.72, 2.02, 1.68, 1.23]\n Course: [0.0, 3.81, 5.8, 4.7, 3.3, 0.34, -0.82]\nWhat is the action of ego car?\nAssistant: The car is turning right\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turned green.\n\n",
+    "BDDX_Processed/video/029e1042-e795c758_02306.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.55, 6.46, 5.51, 4.41, 3.36, 2.52, 1.79]\n Curvature: [-0.0, -0.01, -0.02, -0.03, -0.03, -0.02, -0.01]\n Acceleration: [-2.86, -3.06, -3.24, -3.44, -3.54, -3.02, -2.57]\n Course: [0.0, 0.02, 0.01, 0.02, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a stop sign.\n\n",
+    "BDDX_Processed/video/19829784-7e4ee4e9_16733.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.54, 7.2, 6.68, 6.41, 6.23, 0.9, 8.2]\n Curvature: [0.0, -0.0, -0.03, -0.09, -0.05, -0.0, 0.0]\n Acceleration: [-1.28, -0.47, -0.11, -0.35, -0.6, -0.13, 2.87]\n Course: [0.0, 0.0, 1.83, 4.55, 1.45, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/0b53476e-9d258f61_09291.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [27.25, 27.6, 28.08, 28.68, 28.98, 28.91, 28.26]\n Curvature: [0.0, -0.01, -0.01, -0.02, -0.05, -0.05, -0.02]\n Acceleration: [0.8, 0.76, 0.72, 0.68, 0.08, -0.37, -0.54]\n Course: [0.0, 0.05, 0.41, 0.33, 0.94, 0.68, 0.85]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic in its lane is moving freely.\n\n",
+    "BDDX_Processed/video/04095551-efe2622a_02215.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.09, 11.21, 11.31, 11.55, 11.95, 12.35, 12.41]\n Curvature: [-0.01, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.01, 0.27, 0.51, 0.64, 0.58, 0.52, 0.28]\n Course: [0.0, 0.04, 0.02, 0.03, 0.03, 0.03, -0.05]\nWhat is the action of ego car?\nAssistant: The car merges into the lane to its right\n\nHuman: Why does the ego car doing this?\nAssistant: due to traffic moving freely in that lane.\n\n",
+    "BDDX_Processed/video/1a032081-0e891e50_16889.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.96, 3.62, 3.19, 2.8, 2.0, 1.24, 0.68]\n Curvature: [0.0, 0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.33, -0.38, -0.39, -0.58, -0.76, -0.64, -0.42]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The gradually slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the cars ahead are stopped at a red light.\n\n",
+    "BDDX_Processed/video/01e9deb7-ed388859_00421.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.47, 6.11, 5.8, 5.44, 5.07, 4.62, 4.22]\n Curvature: [0.02, 0.02, 0.01, 0.01, 0.0, 0.01, 0.01]\n Acceleration: [-0.8, -0.99, -1.16, -1.36, -1.52, -1.44, -1.37]\n Course: [0.0, 0.02, 0.01, 0.02, 0.06, 0.09, 0.05]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has stopped.\n\n",
+    "BDDX_Processed/video/025b2ded-1b0c1607_05102.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.78, 4.89, 4.99, 5.1, 5.24, 5.55, 5.81]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.43, 0.53, 0.61, 0.7, 0.79, 0.86, 0.93]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves right\n\nHuman: Why does the ego car doing this?\nAssistant: because they are following the flow of traffic.\n\n",
+    "BDDX_Processed/video/160f6a0b-85d4230f_14902.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.35, 17.46, 17.56, 17.67, 17.78, 17.99, 18.19]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.51, 0.53, 0.56, 0.58, 0.61, 0.66, 0.72]\n Course: [0.0, -0.05, -0.05, -0.05, -0.03, -0.07, -0.07]\nWhat is the action of ego car?\nAssistant: The car starts to drift across the yellow line\n\nHuman: Why does the ego car doing this?\nAssistant: as the road begins to curve\n\n",
+    "BDDX_Processed/video/0b88820f-3a647f1d_09422.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.48, 0.35, 0.24, 0.11, 0.01, 0.05, 0.09]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.48, -0.39, -0.32, -0.23, -0.15, -0.06, 0.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls forward\n\nHuman: Why does the ego car doing this?\nAssistant: to move closer to the car in front of it.\n\n",
+    "BDDX_Processed/video/070383db-0363b1c7_06891.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.83, 1.44, 4.02, 4.78, 4.01, 3.53, 4.72]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.01, 0.99, 0.79, -0.06, -0.45, 0.21, 0.75]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins driving forward with traffic\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has begun moving forward.\n\n",
+    "BDDX_Processed/video/1c2512e1-9deecbba_17967.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.94, 11.47, 11.9, 12.19, 12.4, 12.72, 12.9]\n Curvature: [-0.0, 0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.72, 0.62, 0.46, 0.35, 0.32, 0.31, 0.28]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.14]\nWhat is the action of ego car?\nAssistant: The car continues down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear.\n\n",
+    "BDDX_Processed/video/08119845-793c9e3a_07424.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.76, 5.54, 5.33, 5.0, 4.55, 4.06, 3.74]\n Curvature: [0.05, 0.06, 0.08, 0.12, 0.18, 0.25, 0.2]\n Acceleration: [-0.24, -0.5, -0.74, -0.9, -0.93, -0.96, -0.84]\n Course: [0.0, 0.0, 0.0, 0.04, 0.02, 0.04, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: after the light turned yellow and then red.\n\n",
+    "BDDX_Processed/video/002b562f-e0ac84fe_04332.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.77, 11.87, 11.96, 11.87, 11.52, 11.15, 10.49]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.28, 0.03, -0.2, -0.51, -0.88, -1.29, -1.53]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a stop sign.\n\n",
+    "BDDX_Processed/video/18ca3d6f-744f223d_16385.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.26, 15.15, 15.27, 14.65, 14.57, 15.36, 16.03]\n Curvature: [0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 0.02]\n Acceleration: [0.4, 0.23, -0.1, -0.18, 0.39, 0.19, 0.22]\n Course: [0.0, -0.17, 0.0, 0.0, -0.14, 0.0, -1.35]\nWhat is the action of ego car?\nAssistant: The car is heading down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/213f19cf-e0fdcf88_20443.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.56, 4.48, 6.54, 6.55, 6.96, 9.35, 10.89]\n Curvature: [0.03, -0.0, -0.02, 0.05, -0.02, 0.01, 0.0]\n Acceleration: [1.74, 0.76, 0.58, -0.04, 0.89, 1.05, 0.34]\n Course: [0.0, 0.0, 0.17, 0.34, -0.53, 0.68, -0.17]\nWhat is the action of ego car?\nAssistant: The car moves forward and goes through the intersection\n\nHuman: Why does the ego car doing this?\nAssistant: since the light turns green\n\n",
+    "BDDX_Processed/video/00091078-875c1f73_00175.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.08, 3.7, 3.34, 2.89, 2.33, 1.78, 1.74]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.76, -0.93, -1.08, -1.09, -0.89, -0.7, -0.45]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: as the car in front of it brakes.\n\n",
+    "BDDX_Processed/video/1c071aeb-030e9cdb_17911.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.83, 1.61, 1.41, 1.49, 1.57, 1.72, 1.79]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.56, -0.33, -0.13, 0.05, 0.19, 0.04, -0.11]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: as the car in front is stopping at a stop sign.\n\n",
+    "BDDX_Processed/video/047e715f-81e81a28_03376.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: for an unknown reason.\n\n",
+    "BDDX_Processed/video/0fd7d527-313c3f93_11565.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.23, 0.36, 0.49, 0.78, 1.08, 1.79, 2.51]\n Curvature: [-0.11, -0.09, -0.06, -0.05, -0.03, 0.0, 0.03]\n Acceleration: [-0.22, 0.1, 0.4, 0.69, 0.95, 1.2, 1.39]\n Course: [0.0, 0.32, 0.32, 0.66, 0.39, 0.23, -0.05]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: to avoid getting hit by a fellow stepping out of his car.\n\n",
+    "BDDX_Processed/video/13ff2e36-2301a60e_13727.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.05, 8.54, 7.95, 7.43, 6.82, 6.1, 5.29]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-1.82, -2.0, -2.2, -2.37, -2.55, -2.62, -2.7]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows again\n\nHuman: Why does the ego car doing this?\nAssistant: because there is another stop sign\n\n",
+    "BDDX_Processed/video/0a6966b1-936df4dd_08764.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.37, 4.55, 4.79, 6.19, 7.94, 9.38, 10.21]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.98, 0.65, 0.11, 0.64, 0.79, 0.45, -0.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the car up ahead is moving.\n\n",
+    "BDDX_Processed/video/0d0e69b6-a30b3acb_10307.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.86, 10.85, 10.78, 10.65, 10.28, 9.98, 9.89]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.08, -0.04, -0.2, -0.39, -0.4, -0.31, -0.12]\n Course: [0.0, 0.0, 0.05, 0.05, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drifts slightly into the right lane before correcting back to the left\n\nHuman: Why does the ego car doing this?\nAssistant: for no discernible reason.\n\n",
+    "BDDX_Processed/video/01ab0f6d-b74b888d_04861.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [20.95, 20.78, 20.61, 20.44, 20.26, 20.0, 19.74]\n Curvature: [0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01]\n Acceleration: [-0.02, -0.23, -0.44, -0.65, -0.84, -0.93, -1.02]\n Course: [0.0, 0.02, 0.02, 0.02, 0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges right\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is seen and the car can safely move onward.\n\n",
+    "BDDX_Processed/video/06fb4e9c-f4fc93c7_00401.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked on the side of the road.\n\n",
+    "BDDX_Processed/video/111aecfb-d6fb70d7_12243.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.34, 1.6, 1.85, 2.11, 2.37, 2.54, 2.71]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.99, 0.95, 0.91, 0.87, 0.79, 0.61, 0.44]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving slowly forward\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead is moving slow.\n\n",
+    "BDDX_Processed/video/1e7af489-858285fa_19125.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [-1.0, 2.58, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.88, 0.44, -1.29, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car has come to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because someone is moving in the vehicle (it is unclear whether exiting or entering).\n\n",
+    "BDDX_Processed/video/15b8be6c-ab5d258f_14651.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.69, 5.76, 6.01, 7.7, 11.03, 14.54, 17.59]\n Curvature: [-0.0, -0.1, -0.11, -0.04, -0.0, -0.03, 0.01]\n Acceleration: [0.35, -1.08, 0.47, 1.46, 1.82, 1.72, 1.47]\n Course: [0.0, 0.0, 8.09, 1.91, 0.0, 0.0, 0.34]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's merging onto the highway.\n\n",
+    "BDDX_Processed/video/02191150-4615d36f_00806.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, -0.02, -0.02, -0.02, -0.01, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car stops.\n\nHuman: Why does the ego car doing this?\nAssistant: The cab is still stopped in front because of the red light.\n\n",
+    "BDDX_Processed/video/09bf8565-ff3f3d49_08502.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.32, 17.5, 17.68, 17.76, 17.91, 18.49, 19.04]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.35, 0.3, 0.27, 0.47, 0.66, 0.82, 0.92]\n Course: [0.0, 0.02, 0.01, 0.0, 0.01, 0.02, 0.01]\nWhat is the action of ego car?\nAssistant: The car merges onto the right lane of a highway\n\nHuman: Why does the ego car doing this?\nAssistant: as there is an opening in the lane.\n\n",
+    "BDDX_Processed/video/13796ade-e1216328_13504.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.39, 0.16, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, -0.14, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car remains at a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light remains red and there are pedestrians crossing the road.\n\n",
+    "BDDX_Processed/video/04e6f6b8-67767837_06118.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [22.1, 22.29, 22.41, 22.55, 22.98, 22.68, 21.63]\n Curvature: [0.02, 0.01, 0.01, 0.02, 0.04, -0.01, 0.02]\n Acceleration: [-0.01, 0.16, -0.05, 0.38, -0.05, -0.42, -0.67]\n Course: [0.0, -0.77, -1.62, -0.38, 0.0, -0.44, -0.55]\nWhat is the action of ego car?\nAssistant: The car drives down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road ahead is clear.\n\n",
+    "BDDX_Processed/video/1f77fb7b-d1d8dfee_19614.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.7, 3.65, 3.6, 3.26, 2.51, 1.76, 2.05]\n Curvature: [3.17, 3.16, 3.15, 2.64, 1.43, 0.21, 0.0]\n Acceleration: [-0.28, -0.58, -0.87, -0.92, -0.64, -0.37, 0.12]\n Course: [0.0, -0.03, -0.03, -0.03, -0.03, -0.03, -0.17]\nWhat is the action of ego car?\nAssistant: The car comes to a quick stop\n\nHuman: Why does the ego car doing this?\nAssistant: for a stop sign.\n\n",
+    "BDDX_Processed/video/00c12bd0-bb46e479_04561.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.13, 6.33, 4.91, 1.51, 0.14, 0.18, 0.39]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.86, -1.74, -2.69, -2.48, -0.91, 0.12, 0.27]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows and veers to the curb on the right\n\nHuman: Why does the ego car doing this?\nAssistant: to make room for an oncoming car.\n\n",
+    "BDDX_Processed/video/1bb2df55-2815f001_17764.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.88, 0.85, 1.56, 1.02, 0.99, 0.37, 0.0]\n Curvature: [-0.09, -1.3, 0.42, 0.2, -0.09, 0.7, 0.82]\n Acceleration: [-2.0, 0.09, -0.08, -0.32, -0.3, -0.17, 0.0]\n Course: [0.0, 0.38, 0.0, 0.22, 0.0, -0.22, 0.05]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/18386634-99f37a79_16116.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.35, 0.0, 0.0, 0.0, 0.19, 0.0, 0.0]\n Curvature: [0.17, 0.18, 0.19, 0.19, 0.06, 0.02, -0.03]\n Acceleration: [-0.98, 0.0, 0.0, 0.02, -0.03, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red and pedestrians are crossing the street.\n\n",
+    "BDDX_Processed/video/0309d11c-4f7cba1a_03848.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.69, 6.88, 7.01, 7.09, 6.99, 6.81, 6.56]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.21, 0.21, 0.13, -0.02, -0.18, -0.27, -0.2]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is merging into the right hand lane\n\nHuman: Why does the ego car doing this?\nAssistant: The car is preparing to avoid a stopped car that is attempting to make a left turn ahead.\n\n",
+    "BDDX_Processed/video/1fbe0baa-e8dfd916_19836.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.18, 0.0, 0.0, 0.26, 0.1, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.12, 0.0, 0.0, 0.07, 0.08, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slowly edges forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the area ahead is clear.\n\n",
+    "BDDX_Processed/video/225dad3c-da577033_20997.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.49, 2.87, 2.31, 2.03, 2.19, 2.37, 2.85]\n Curvature: [0.08, 0.03, -0.02, -0.06, -0.07, -0.1, -0.0]\n Acceleration: [-2.99, -2.01, -1.11, -0.3, 0.23, 0.81, 1.11]\n Course: [0.0, 0.11, 0.11, 0.0, 0.0, 0.0, 0.42]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: to let the car in front turn into a business.\n\n",
+    "BDDX_Processed/video/0786294e-634c696d_07189.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.24, 0.15, 0.05, 0.03, 0.1, 0.17, 0.12]\n Curvature: [-0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.15]\n Acceleration: [-0.24, -0.16, -0.07, -0.02, -0.01, -0.0, -0.03]\n Course: [0.0, 0.05, 0.07, -0.04, -0.02, -0.04, -0.04]\nWhat is the action of ego car?\nAssistant: The car moves forward slightly\n\nHuman: Why does the ego car doing this?\nAssistant: because there is room in the parking space.\n\n",
+    "BDDX_Processed/video/076399f2-e2bc0747_02406.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped at a red light.\n\n",
+    "BDDX_Processed/video/07f87c9c-8a8d81ef_03809.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.44, 0.89, 0.11, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-1.19, -0.58, -0.32, -0.02, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: The light controlling the intersection is red..\n\n",
+    "BDDX_Processed/video/1d288c57-7db44b8f_18401.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.99, 10.56, 11.11, 11.6, 11.71, 11.73, 11.49]\n Curvature: [0.03, 0.03, 0.02, 0.05, 0.03, 0.05, 0.04]\n Acceleration: [0.38, 0.42, 0.36, 0.25, 0.01, -0.07, -0.12]\n Course: [0.0, -1.03, -1.76, -2.09, -1.64, -2.38, -2.23]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear\n\n",
+    "BDDX_Processed/video/1a7df43e-c08a2cc4_17125.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.62, 0.28, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01]\n Acceleration: [0.08, -0.28, -0.04, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.13]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/028b5d16-45f7e11a_05193.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.15, 0.84, 0.54, 0.23, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.0, -1.62, -1.24, -0.86, -0.56, -0.41, -0.26]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: for the red light.\n\n",
+    "BDDX_Processed/video/07184fbb-bab6f8ce_06995.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.39, 4.96, 7.53, 9.36, 10.27, 9.36, 5.09]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [1.04, 0.92, 1.91, 0.84, 0.19, -2.25, -2.6]\n Course: [0.0, 0.0, 0.0, -0.24, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has stopped in front.\n\n",
+    "BDDX_Processed/video/0738f42e-734821e2_07072.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.34, 2.73, 1.98, 1.51, 1.02, 0.98, 0.62]\n Curvature: [0.0, 0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.58, -0.66, -0.58, -0.47, -0.26, -0.19, -0.27]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slowly moves forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic moving.\n\n",
+    "BDDX_Processed/video/1494619f-87888b77_13998.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.1, 0.87, 1.9]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.01, 0.07, 0.11]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is parked\n\nHuman: Why does the ego car doing this?\nAssistant: because the driver is getting ready to leave.\n\n",
+    "BDDX_Processed/video/198b38c6-ccb3915d_16745.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.72, 3.2, 3.66, 4.09, 4.39, 4.3, 3.61]\n Curvature: [-0.0, -0.14, -0.18, -0.13, -0.03, 0.01, -0.03]\n Acceleration: [0.73, 0.71, 0.66, 0.56, 0.03, -0.32, -0.3]\n Course: [0.0, 7.8, 8.04, 6.7, -0.49, 0.8, 0.96]\nWhat is the action of ego car?\nAssistant: The car turns right onto the street\n\nHuman: Why does the ego car doing this?\nAssistant: since the road is clear to turn.\n\n",
+    "BDDX_Processed/video/0b7cbaa7-d677bc73_09393.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.25, 1.55, 1.83, 2.16, 2.51, 2.88, 3.11]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.28, 0.51, 0.71, 0.82, 0.78, 0.72, 0.66]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the vehicle in front of it is traveling at a normal rate of speed.\n\n",
+    "BDDX_Processed/video/114e578a-47a24858_12379.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.02, 12.1, 13.59, 15.66, 16.15, 17.92, 17.73]\n Curvature: [0.0, 0.09, 0.02, 0.04, 0.02, 0.0, -0.0]\n Acceleration: [0.03, 0.28, 0.59, 0.97, 0.59, 0.19, -0.02]\n Course: [0.0, -8.21, -3.63, -2.09, -1.73, -0.59, 0.0]\nWhat is the action of ego car?\nAssistant: The car maintains a constant speed and lane\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/04629c39-faefdb18_03146.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.87, 6.92, 7.33, 7.69, 7.19, 6.81, 5.9]\n Curvature: [0.08, -0.02, 0.0, 0.01, 0.01, -0.0, -0.0]\n Acceleration: [1.39, 0.56, 0.29, -0.07, -0.32, -0.53, -0.55]\n Course: [0.0, -0.24, 0.11, 0.63, -0.12, -0.51, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/0e7ecff9-761fe466_11040.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.05, 0.1, 0.14, 0.17, 0.13, 0.08]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.11, -0.81, -0.52, -0.22, -0.0, -0.03, -0.05]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: after the light turned yellow.\n\n",
+    "BDDX_Processed/video/06ac374f-5c7f25cf_06669.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car sits parked on the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the car has not moved yet.\n\n",
+    "BDDX_Processed/video/1726919b-9157ae86_15636.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.7, 8.75, 9.03, 8.51, 8.52, 6.08, 6.46]\n Curvature: [0.0, -0.0, 0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.08, 0.34, -0.22, -0.01, -0.73, -0.9, 0.26]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is now going straight\n\nHuman: Why does the ego car doing this?\nAssistant: as it heads down the street\n\n",
+    "BDDX_Processed/video/11ecaf4a-850edf66_12740.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.67, 12.74, 12.82, 13.03, 13.24, 13.63, 14.03]\n Curvature: [0.0, -0.0, -0.01, 0.0, 0.01, 0.0, -0.0]\n Acceleration: [0.01, 0.15, 0.28, 0.43, 0.57, 0.71, 0.83]\n Course: [0.0, -0.14, -0.09, 0.05, 0.17, 0.58, -0.05]\nWhat is the action of ego car?\nAssistant: The car proceeds down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead is flowing.\n\n",
+    "BDDX_Processed/video/01dc1bbe-8919b3d8_04901.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.9, 9.22, 8.55, 7.87, 7.33, 7.12, 6.92]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.02, -0.16, -0.29]\n Acceleration: [-2.54, -2.31, -2.08, -1.85, -1.66, -1.53, -1.39]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: Because it is preparing to make a right turn.\n\n",
+    "BDDX_Processed/video/023a07ea-89f817c3_05056.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.03, 4.73, 5.53, 6.08, 7.62, 8.66, 8.8]\n Curvature: [0.04, 0.01, -0.0, -0.52, -0.2, 0.08, -0.0]\n Acceleration: [1.68, 1.1, 0.55, 0.88, 1.19, 0.44, -0.15]\n Course: [0.0, -0.2, 0.51, 0.0, 0.31, 0.82, 0.92]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the car ahead moves forward to make a turn.\n\n",
+    "BDDX_Processed/video/14288818-a3e8421e_13809.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.8, 12.14, 11.56, 10.9, 10.25, 9.73, 9.27]\n Curvature: [0.01, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.54, -1.72, -1.88, -2.06, -2.23, -2.25, -2.28]\n Course: [0.0, 0.02, 0.01, 0.02, 0.02, 0.02, 0.01]\nWhat is the action of ego car?\nAssistant: The car decelerates to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a red light.\n\n",
+    "BDDX_Processed/video/02621ea2-399eaeb2_05126.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.16]\n Curvature: [-0.11, -0.11, -0.11, -0.11, -0.11, -0.11, -0.13]\n Acceleration: [0.0, 0.0, 0.0, 0.04, 0.13, 0.22, 0.48]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car remains at a complete stop.\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light remains red.\n\n",
+    "BDDX_Processed/video/1f5efe2c-bb484e49_19571.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.32, 1.37, 1.48, 1.8, 2.43, 3.32, 4.24]\n Curvature: [0.0, 0.0, -0.05, -0.2, -0.45, -0.65, -0.24]\n Acceleration: [0.32, 0.16, 0.31, 0.62, 0.97, 1.16, 1.24]\n Course: [0.0, 0.0, 0.0, 7.14, 4.81, 4.85, 5.95]\nWhat is the action of ego car?\nAssistant: The vehicle move to the right\n\nHuman: Why does the ego car doing this?\nAssistant: because it is completing a right turn.\n\n",
+    "BDDX_Processed/video/132a6855-befebf75_13283.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.04, 3.51, 4.71, 5.44, 6.04, 6.39, 6.27]\n Curvature: [-0.25, -0.05, 0.06, 0.03, 0.02, 0.01, -0.02]\n Acceleration: [1.64, 1.71, 1.53, 1.08, 0.66, 0.19, -0.27]\n Course: [0.0, 0.05, -0.06, -0.06, -0.23, -0.35, -0.35]\nWhat is the action of ego car?\nAssistant: The car is moving slowly forward toward the intersection\n\nHuman: Why does the ego car doing this?\nAssistant: The car is moving slowly and keeping to the left edge of the lane to avoid a car that is attempting to enter the roadway from the right\n\n",
+    "BDDX_Processed/video/04095551-efe2622a_02213.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.02, 2.91, 3.88, 4.89, 5.83, 5.84, 5.99]\n Curvature: [0.04, -0.01, -0.06, -0.07, -0.07, -0.05, -0.03]\n Acceleration: [1.9, 1.9, 1.88, 1.43, 1.0, 0.85, 0.81]\n Course: [0.0, -1.34, -0.98, 1.05, -22.64, -35.02, -11.07]\nWhat is the action of ego car?\nAssistant: The car merges into the lane to its left\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving more quickly in that lane.\n\n",
+    "BDDX_Processed/video/07184fbb-2e67b768_06981.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.29, 12.28, 15.0, 16.65, 14.63, 14.0, 13.42]\n Curvature: [0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.49, 1.35, 1.5, -0.3, -0.99, 0.05, -0.33]\n Course: [0.0, 0.0, -0.06, 0.0, 0.1, 0.0, 0.2]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/1f67d11b-cf3f5bf0_19601.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.36, 0.94, 1.72, 2.63, 4.72, 6.42]\n Curvature: [0.0, 0.0, 0.0, 0.02, 0.02, 0.0, 0.01]\n Acceleration: [0.14, 0.33, 0.55, 0.58, 1.2, 1.46, 0.88]\n Course: [0.0, 0.0, 0.0, -0.78, 0.0, 0.0, -0.49]\nWhat is the action of ego car?\nAssistant: The car rolls forward\n\nHuman: Why does the ego car doing this?\nAssistant: as other cars move up\n\n",
+    "BDDX_Processed/video/11b47a8a-b54f88dd_12667.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.67, 9.41, 8.21, 6.71, 5.33, 3.97, 2.89]\n Curvature: [-0.01, -0.01, -0.02, -0.0, 0.01, 0.03, 0.09]\n Acceleration: [-1.46, -2.06, -2.59, -2.64, -2.6, -1.98, -1.51]\n Course: [0.0, 0.0, 0.0, 0.0, -0.03, -0.06, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead is stopped.\n\n",
+    "BDDX_Processed/video/06918135-10720a5a_03449.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.21, 11.93, 11.67, 11.32, 10.92, 10.47, 9.8]\n Curvature: [-0.0, -0.0, -0.0, -0.01, -0.02, -0.03, -0.01]\n Acceleration: [-0.7, -0.78, -0.86, -1.0, -1.2, -1.42, -1.54]\n Course: [0.0, 0.0, 0.0, 0.04, 0.02, 0.04, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as its way is clear.\n\n",
+    "BDDX_Processed/video/16a98dc3-2215097f_15360.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.3, 3.29, 2.54, 2.1, 1.55, 1.11, 0.89]\n Curvature: [0.05, -0.15, -0.12, 0.16, 0.2, 0.18, 0.12]\n Acceleration: [-1.68, -1.32, -1.02, -0.8, -0.68, -0.43, 0.08]\n Course: [0.0, -0.06, 0.11, 0.11, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has stopped.\n\n",
+    "BDDX_Processed/video/0714456d-2648a0c9_00996.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.17, 5.32, 4.46, 3.61, 2.8, 2.35, 1.89]\n Curvature: [-0.02, -0.01, 0.01, 0.02, 0.04, 0.02, 0.01]\n Acceleration: [-3.12, -2.97, -2.82, -2.67, -2.48, -2.04, -1.59]\n Course: [0.0, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: due to the car in front of it slowing.\n\n",
+    "BDDX_Processed/video/1fbec55a-4685ff6c_19837.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [23.43, 24.52, 24.06, 23.91, 23.87, 23.15, 23.71]\n Curvature: [0.0, 0.0, 0.0, 0.01, -0.0, 0.0, -0.01]\n Acceleration: [0.25, 0.07, -0.02, -0.01, -0.18, -0.15, 0.14]\n Course: [0.0, -0.28, -1.55, 0.0, 0.02, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving at a steady rate of speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is flowing steadily\n\n",
+    "BDDX_Processed/video/18ca3d6f-744f223d_16387.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.24, 6.21, 6.18, 6.2, 6.34, 6.46, 6.43]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.54, -0.29, -0.03, 0.12, 0.1, 0.08, 0.08]\n Course: [0.0, 0.0, 0.0, -0.01, -0.02, -0.01, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is mo\n\n",
+    "BDDX_Processed/video/06cb522f-aaed5ecf_04008.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.71, 8.12, 6.54, 5.41, 4.92, 4.44, 4.31]\n Curvature: [-0.03, -0.02, -0.01, -0.01, -0.02, -0.02, -0.01]\n Acceleration: [-4.42, -3.74, -3.07, -2.36, -1.6, -0.84, -0.33]\n Course: [0.0, 0.31, 0.31, 0.2, 0.2, 0.2, 0.2]\nWhat is the action of ego car?\nAssistant: The car slows to a near stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is stopped.\n\n",
+    "BDDX_Processed/video/129833be-f2be8e39_12995.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.65, 15.63, 13.59, 11.88, 10.59, 9.24, 7.24]\n Curvature: [0.0, -0.0, 0.0, 0.0, -0.06, -0.48, -0.26]\n Acceleration: [-2.52, -2.58, -2.34, -1.98, -1.74, -2.16, -1.43]\n Course: [0.0, 0.0, 0.0, 0.0, 3.33, 6.67, 11.31]\nWhat is the action of ego car?\nAssistant: The car makes a right turn\n\nHuman: Why does the ego car doing this?\nAssistant: because the intersection is clear.\n\n",
+    "BDDX_Processed/video/00391a82-8be5b76d_00701.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.22, 8.87, 8.5, 8.24, 8.1, 7.94, 7.59]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.91, -0.81, -0.71, -0.67, -0.69, -0.72, -0.74]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.1]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic ahead is slower.\n\n",
+    "BDDX_Processed/video/0bf6d230-656be21a_09631.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.84, 5.38, 5.54, 5.79, 5.13, 4.34, 3.85]\n Curvature: [-0.04, 0.01, -0.08, 0.19, 0.1, 0.0, -0.02]\n Acceleration: [0.6, 0.2, 0.09, -0.1, -0.46, -0.37, 0.2]\n Course: [0.0, 0.47, 0.12, 0.58, -0.32, -1.3, -0.53]\nWhat is the action of ego car?\nAssistant: The car drives down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the light ahead is green and traffic is moving.\n\n",
+    "BDDX_Processed/video/017b02cb-477b66fa_04799.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.95, -0.95, -0.95, -0.96, -0.96, -0.96, -0.96]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, -0.02, -0.04, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: due to stopped traffic in front of it.\n\n",
+    "BDDX_Processed/video/012fdff1-9d1d0d1d_04691.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.97, 2.41, 1.89, 1.43, 1.16, 0.86, 0.85]\n Curvature: [0.01, -0.0, -0.01, -0.02, -0.02, -0.02, -0.01]\n Acceleration: [-0.71, -0.86, -0.99, -0.95, -0.67, -0.37, 0.02]\n Course: [0.0, 0.04, 0.04, -0.04, -0.02, -0.04, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then stops\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic isn't really going very far.\n\n",
+    "BDDX_Processed/video/1de119a0-f155a957_18807.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red and there is a car in front of it\n\n",
+    "BDDX_Processed/video/10229247-f8592123_11748.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.14, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/13796ade-097d1f83_13459.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is at a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because there are cars ahead of it stopped as they wait at the traffic light which is red.\n\n",
+    "BDDX_Processed/video/029e1042-66968a89_02246.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.41, 9.57, 10.29, 11.0, 11.19, 9.98, 8.63]\n Curvature: [-0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.83, 0.4, 0.39, 0.3, -0.16, -1.1, -0.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car maintains speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/041fb602-19f8495d_02342.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.71, 3.73, 2.66, 2.2, 2.09, 2.09, 2.36]\n Curvature: [0.04, -0.0, 0.03, 0.02, -0.04, -0.05, -0.01]\n Acceleration: [-2.07, -1.68, -0.87, -0.33, -0.07, 0.18, 0.7]\n Course: [0.0, -0.2, 0.0, -0.16, 0.0, 0.48, 0.57]\nWhat is the action of ego car?\nAssistant: The car moves forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because the policeman is in the road.\n\n",
+    "BDDX_Processed/video/0d405525-3d6d563b_10480.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.15, 1.56, 1.92, 1.9, 1.82, 1.36, 1.2]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.77, 0.55, 0.34, -0.1, -0.41, -0.01, 0.47]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowly driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches the intersection\n\n",
+    "BDDX_Processed/video/1bb2df55-52b38d56_17765.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.55, 12.23, 12.32, 12.0, 12.0, 12.78, 12.98]\n Curvature: [-0.0, -0.02, 0.02, 0.05, -0.07, -0.0, -0.04]\n Acceleration: [0.43, 0.09, 0.04, -0.23, 0.49, 0.04, 0.21]\n Course: [0.0, -0.68, 0.05, 0.27, -0.73, 0.12, 0.14]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: since the road is clear.\n\n",
+    "BDDX_Processed/video/227829d9-6a5c7405_21053.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.48, 0.61, 0.72, 0.85, 1.02, 1.54, 1.98]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.22, 0.48, 0.71, 0.97, 1.22, 1.42, 1.58]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car starts to move\n\nHuman: Why does the ego car doing this?\nAssistant: after seeing a clear intersection.\n\n",
+    "BDDX_Processed/video/1d658258-c9a58636_18553.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.52, 4.34, 4.76, 5.65, 6.78, 8.04, 8.74]\n Curvature: [-0.08, 0.03, 0.01, -0.03, -0.02, 0.01, 0.04]\n Acceleration: [-0.32, 0.18, 0.77, 1.29, 1.51, 1.25, 0.7]\n Course: [0.0, 0.15, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car speeds up\n\nHuman: Why does the ego car doing this?\nAssistant: while still following other traffic\n\n",
+    "BDDX_Processed/video/014b8fc3-532a0660_04723.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.58, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because pedestrians are crossing the road.\n\n",
+    "BDDX_Processed/video/00391a82-8be5b76d_00703.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.93, 14.61, 15.09, 15.33, 15.29, 15.01, 14.83]\n Curvature: [-0.06, -0.01, 0.0, -0.01, -0.0, -0.02, -0.04]\n Acceleration: [0.81, 0.65, 0.41, 0.12, -0.17, -0.23, 0.02]\n Course: [0.0, 0.15, 0.0, 0.49, 0.31, 0.12, 0.96]\nWhat is the action of ego car?\nAssistant: The car veers left\n\nHuman: Why does the ego car doing this?\nAssistant: to merge into the left lane.\n\n",
+    "BDDX_Processed/video/040fff88-4e31bfdd_02287.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.44, 6.91, 8.03, 8.12, 7.8, 7.27, 6.15]\n Curvature: [0.0, 0.0, 0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.0, -0.53, 0.86, -0.47, -0.14, -0.27, 0.08]\n Course: [0.0, 0.0, -0.08, 0.0, 0.02, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car maintains a slow constant speed and spacing with the vehicle ahead\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving slow, but smoothly.\n\n",
+    "BDDX_Processed/video/0c13b339-fac6ef24_09689.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.54, 4.22, 4.57, 3.3, 3.6, 4.16, 3.37]\n Curvature: [0.0, 0.42, -0.08, -0.0, 0.09, -0.02, -2.33]\n Acceleration: [0.73, 0.65, -0.43, -0.34, 0.58, -0.17, -0.45]\n Course: [0.0, 2.04, 3.33, 0.0, 7.5, 2.72, 3.18]\nWhat is the action of ego car?\nAssistant: The is moving ahead slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because it's following behind another slow moving vehicle.\n\n",
+    "BDDX_Processed/video/182dc406-9d6e0eae_16091.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.93, 10.2, 10.44, 10.63, 10.8, 10.91, 11.01]\n Curvature: [-0.25, 0.02, 0.24, 0.1, -0.01, -0.0, 0.0]\n Acceleration: [0.65, 0.53, 0.43, 0.35, 0.28, 0.24, 0.22]\n Course: [0.0, -0.51, -0.23, 0.09, 0.11, 0.14, 0.17]\nWhat is the action of ego car?\nAssistant: The car pulls into the middle lane\n\nHuman: Why does the ego car doing this?\nAssistant: because a bus is pulling out nearby.\n\n",
+    "BDDX_Processed/video/1a6798d7-3ab0d080_17090.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.63, 4.5, 4.54, 4.24, 3.6, 4.45, 5.11]\n Curvature: [-0.02, -0.01, -0.01, 0.02, -0.01, -0.06, 0.03]\n Acceleration: [0.98, 0.26, -0.0, -0.5, 0.18, 0.6, 0.43]\n Course: [0.0, -0.13, 0.13, 0.25, 0.6, 0.74, -0.4]\nWhat is the action of ego car?\nAssistant: The car continues to drive on the roadway\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic is moving forward.\n\n",
+    "BDDX_Processed/video/20c3c3e1-3861f97b_20241.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.36, 5.27, 5.16, 4.91, 4.38, 3.64, 2.56]\n Curvature: [-0.01, 0.02, 0.01, -0.01, -0.0, -0.04, 0.02]\n Acceleration: [-0.17, -0.14, -0.27, -0.5, -0.83, -1.14, -1.15]\n Course: [0.0, -0.11, 0.14, 0.0, 0.07, 0.12, 0.21]\nWhat is the action of ego car?\nAssistant: The car is slowing down to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: As it approaches the intersection in the two lane street.\n\n",
+    "BDDX_Processed/video/10af24c9-8a062c1a_11886.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.92, 10.2, 10.97, 12.13, 11.69, 11.27, 10.64]\n Curvature: [0.0, 0.01, -0.0, -0.01, 0.01, 0.0, 0.0]\n Acceleration: [0.22, 0.31, 0.81, 0.38, -0.48, -0.26, -0.38]\n Course: [0.0, 0.13, 0.12, 0.03, -0.36, 0.0, 0.12]\nWhat is the action of ego car?\nAssistant: The car is moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear on the road.\n\n",
+    "BDDX_Processed/video/00721168-56efa5c2_04475.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.89, 4.54, 4.22, 3.87, 3.51, 3.11, 2.76]\n Curvature: [0.0, 0.0, 0.01, 0.01, 0.04, 0.2, 0.35]\n Acceleration: [-0.79, -0.95, -1.1, -1.27, -1.39, -1.15, -0.95]\n Course: [0.0, -2.09, -1.05, -2.09, -1.28, -0.47, -0.23]\nWhat is the action of ego car?\nAssistant: The car slows down in the middle of its left turn\n\nHuman: Why does the ego car doing this?\nAssistant: Because there is a pedestrian that walks onto the street right when the car is turning onto it\n\n",
+    "BDDX_Processed/video/09cf2788-3645c2cc_08508.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.3, 0.13, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.99, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car remains at a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light remains red and pedestrians are crossing the street.\n\n",
+    "BDDX_Processed/video/1462e08b-8ee18629_13931.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.08, 16.35, 16.59, 16.79, 16.88, 16.98, 17.02]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.49, 0.48, 0.47, 0.41, 0.29, 0.17, 0.08]\n Course: [0.0, 0.0, 0.0, 0.04, 0.03, 0.04, -0.04]\nWhat is the action of ego car?\nAssistant: The car pulls into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it's getting ready to turn.\n\n",
+    "BDDX_Processed/video/14cd7eab-a62d9f29_14073.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.19, 10.91, 9.74, 8.4, 7.09, 5.66, 5.43]\n Curvature: [-0.02, -0.06, -0.1, -0.14, -0.16, -0.18, -0.19]\n Acceleration: [-3.49, -3.44, -3.41, -3.12, -2.52, -1.88, -0.9]\n Course: [0.0, 0.56, 0.56, 1.97, 1.31, 1.97, 2.6]\nWhat is the action of ego car?\nAssistant: The car makes a right turn at the intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/039c5dcd-a603673a_00691.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.9, 7.96, 8.02, 8.01, 8.01, 8.11, 8.26]\n Curvature: [0.0, 0.0, 0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.33, 0.18, 0.05, 0.07, 0.1, 0.28, 0.44]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.09]\nWhat is the action of ego car?\nAssistant: The car veers left\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a pedestrian in the road.\n\n",
+    "BDDX_Processed/video/0d207cff-4d922795_10387.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: at the red light.\n\n",
+    "BDDX_Processed/video/0dcd165f-81255c47_10696.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.2, 6.3, 5.4, 4.4, 3.24, 2.08, 1.52]\n Curvature: [-0.0, -0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.62, -2.64, -2.65, -2.56, -2.33, -2.11, -1.71]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turned red.\n\n",
+    "BDDX_Processed/video/15eb3650-16b6a9ca_14780.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.78, 13.38, 13.03, 12.64, 12.21, 11.53, 10.95]\n Curvature: [-0.04, -0.02, 0.0, 0.02, 0.04, 0.03, 0.02]\n Acceleration: [-1.09, -1.34, -1.55, -1.79, -2.04, -2.3, -2.53]\n Course: [0.0, 0.04, 0.02, 0.04, 0.02, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car swerves a little left\n\nHuman: Why does the ego car doing this?\nAssistant: while slowing down\n\n",
+    "BDDX_Processed/video/0378d8dc-62d8e85a_00522.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.35, 2.03, 3.02, 3.9, 3.68, 3.07, 3.98]\n Curvature: [0.1, 0.11, 0.11, 0.12, 0.12, 0.11, -0.0]\n Acceleration: [-0.17, 0.4, 0.96, 0.35, -0.4, 0.25, 1.61]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.42]\nWhat is the action of ego car?\nAssistant: The car accelerates slowly\n\nHuman: Why does the ego car doing this?\nAssistant: to cross the road slightly left to stay doing the same direction.\n\n",
+    "BDDX_Processed/video/05584a52-730a88df_00511.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.79, 11.12, 11.49, 11.94, 12.37, 12.65, 12.89]\n Curvature: [-0.0, -0.0, 0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [1.32, 1.03, 0.77, 0.74, 0.7, 0.5, 0.3]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates to a constant speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/081d977d-620470d8_07457.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.12, 0.25]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.12, -0.03, 0.06, 0.15, 0.23, 0.34, 0.44]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: due to stopped traffic in front of it.\n\n",
+    "BDDX_Processed/video/15569b20-8dcc97aa_14440.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.15, 0.42, 1.61, 2.02, 1.7, 1.27, 0.41]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.01, 0.67, 0.73, 0.02, -0.33, -0.55, -0.47]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating slowly\n\nHuman: Why does the ego car doing this?\nAssistant: Because the bus in front of it is slowly accelerating forward.\n\n",
+    "BDDX_Processed/video/1d20db39-1e1e61c2_18373.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.16]\n Curvature: [0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.04]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.07]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.38, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked.\n\n",
+    "BDDX_Processed/video/0ba933b2-3308d259_09523.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.29, 8.59, 7.85, 7.35, 6.87, 6.78, 6.73]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-0.84, -1.02, -1.18, -0.87, -0.54, -0.23, 0.07]\n Course: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\nWhat is the action of ego car?\nAssistant: The car is steering to the left and partially entering the oncoming traffic lane\n\nHuman: Why does the ego car doing this?\nAssistant: so that it may get around vehicles parked in its lane.\n\n",
+    "BDDX_Processed/video/1fe4fb48-53ee13f8_19910.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is sitting still\n\nHuman: Why does the ego car doing this?\nAssistant: because it has finished parking.\n\n",
+    "BDDX_Processed/video/083bc808-b649e045_07527.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.45, 0.16, 2.75, 2.47, 2.62, 1.52]\n Curvature: [0.0, 0.0, 0.0, 0.01, 0.0, -0.0, 0.0]\n Acceleration: [0.25, -0.35, 0.66, 0.36, -0.57, -0.74, 0.27]\n Course: [0.0, 0.0, 0.0, 0.0, -0.4, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/03c8da12-57d3c936_01363.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 1.78, 1.79, 1.79, 1.8, 1.8, 1.8]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked.\n\n",
+    "BDDX_Processed/video/0b84641a-2c1532fd_09401.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.77, 6.7, 6.63, 6.55, 6.53, 6.87, 7.2]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.97, -0.58, -0.2, 0.18, 0.54, 0.76, 0.97]\n Course: [0.0, -0.01, -0.01, -0.01, -0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows and swerves to the left\n\nHuman: Why does the ego car doing this?\nAssistant: to avoid an object partially in its lane on the right side of the road.\n\n",
+    "BDDX_Processed/video/0075b179-8e09869a_04482.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.77, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.78, 0.83, 0.84, 0.83, 0.81, 0.8, -0.0]\n Acceleration: [-1.49, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.55]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: as the traffic light is red.\n\n",
+    "BDDX_Processed/video/023a07ea-89f817c3_01237.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.48, 0.57, 0.68, 0.98, 1.36, 2.28, 3.18]\n Curvature: [0.34, 0.25, 0.16, 0.1, 0.05, 0.05, 0.04]\n Acceleration: [-0.72, -0.15, 0.41, 0.82, 1.22, 1.47, 1.63]\n Course: [0.0, 0.0, 0.01, 0.05, 0.0, -0.05, -0.08]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: since there is a ambulance waiting to turn left\n\n",
+    "BDDX_Processed/video/0710389f-7b28ff14_06924.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.15, 0.23, 0.27, 0.29, 0.31, 0.25, 0.2]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.13, 0.08, 0.04, 0.01, -0.02, -0.06, -0.04]\n Course: [0.0, 0.06, 0.28, 55.17, -9.02, -54.81, 54.81]\nWhat is the action of ego car?\nAssistant: The car very slowly merges into the lane\n\nHuman: Why does the ego car doing this?\nAssistant: as the other cars are not allowing easy entry.\n\n",
+    "BDDX_Processed/video/02065c11-194478e8_04955.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.58, 6.76, 6.91, 7.01, 7.12, 7.19, 7.17]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.28, 0.24, 0.2, 0.17, 0.11, -0.03, -0.32]\n Course: [0.0, 0.0, 0.0, 0.0, 0.09, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: the car drives slowly down the road\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is clear\n\n",
+    "BDDX_Processed/video/0b3912a8-ac966f70_09221.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.65, 9.21, 8.83, 8.39, 7.87, 6.78, 5.82]\n Curvature: [-0.02, -0.01, 0.0, 0.01, 0.02, 0.02, 0.02]\n Acceleration: [-1.28, -1.7, -2.07, -2.5, -2.88, -2.95, -3.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down.\n\nHuman: Why does the ego car doing this?\nAssistant: A motorcycle is pulling out while parking.\n\n",
+    "BDDX_Processed/video/10b038d4-a0efb156_11922.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.18, 2.11, 2.04, 1.97, 1.96, 2.29, 2.62]\n Curvature: [0.02, 0.05, 0.07, 0.1, 0.11, 0.08, 0.05]\n Acceleration: [-1.06, -0.64, -0.22, 0.19, 0.53, 0.73, 0.92]\n Course: [0.0, -0.14, -0.14, -0.14, -0.29, -0.59, -0.59]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because it's going to make a left turn.\n\n",
+    "BDDX_Processed/video/0d207cff-6d499379_10396.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.61, 2.24, 2.87, 3.52, 4.18, 4.85, 5.35]\n Curvature: [0.15, 0.1, 0.06, 0.04, 0.04, 0.04, 0.04]\n Acceleration: [0.93, 1.22, 1.51, 1.64, 1.56, 1.48, 1.37]\n Course: [0.0, -1.26, -1.26, -1.29, -1.29, -1.29, -0.65]\nWhat is the action of ego car?\nAssistant: The car is veering to the left.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is turning left onto another street.\n\n",
+    "BDDX_Processed/video/154e0f88-2bfc184e_14412.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.47, 11.68, 11.89, 12.09, 12.35, 12.56, 12.77]\n Curvature: [-0.02, -0.02, -0.01, -0.01, -0.01, -0.01, -0.0]\n Acceleration: [1.66, 1.66, 1.66, 1.66, 1.66, 1.66, 1.66]\n Course: [0.0, -0.03, -0.03, -0.03, -0.07, -0.03, -0.03]\nWhat is the action of ego car?\nAssistant: The car accelerates quickly down the ramp\n\nHuman: Why does the ego car doing this?\nAssistant: since traffic is clear.\n\n",
+    "BDDX_Processed/video/060f62bd-686c028e_02087.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.76, 5.3, 5.78, 6.32, 6.81, 6.91, 7.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.77, 1.62, 1.49, 1.34, 1.17, 0.93, 0.72]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car maintains a slow speed\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a red light ahead.\n\n",
+    "BDDX_Processed/video/10425a11-febf988c_11784.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.87, 6.62, 6.36, 6.11, 5.79, 4.98, 4.18]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.17, -0.4, -0.97, -1.55, -2.08, -2.31, -2.54]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a pedestrian and another police car.\n\n",
+    "BDDX_Processed/video/14ba8733-d8b2dbba_14041.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.22, 0.0, 0.0, 0.49]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.03, 0.0, 0.07, 0.33]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked on the left side of the road.\n\n",
+    "BDDX_Processed/video/17199913-ab22f1e0_15596.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.96, 9.48, 9.0, 8.52, 8.12, 7.82, 7.52]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.02, 0.03]\n Acceleration: [-1.8, -1.72, -1.63, -1.55, -1.47, -1.36, -1.26]\n Course: [0.0, 0.09, 0.09, 0.09, -0.08, -0.16, -0.16]\nWhat is the action of ego car?\nAssistant: The car is moving to the right\n\nHuman: Why does the ego car doing this?\nAssistant: as it gets back into the correct lane\n\n",
+    "BDDX_Processed/video/117138e4-033d0d25_12523.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.54, 7.74, 9.95, 11.71, 12.73, 14.35, 14.46]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.19, 0.97, 1.06, 0.45, 0.81, 0.36, 0.0]\n Course: [0.0, 0.0, -0.16, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car proceeds down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the street is clear.\n\n",
+    "BDDX_Processed/video/03b58b4e-7fc6e32a_05660.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.72, 9.91, 10.16, 10.33, 10.56, 10.65, 10.59]\n Curvature: [-0.0, 0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.15, 0.21, 0.2, 0.19, 0.15, 0.03, -0.22]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.32]\nWhat is the action of ego car?\nAssistant: The car is accelerating smoothly down the dark road\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no traffic and the light is green.\n\n",
+    "BDDX_Processed/video/2100be1f-834f2c83_20337.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.75, 1.39, 0.81, 0.61, 0.89, 2.68, 0.0]\n Curvature: [-0.0, 0.0, 0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-3.32, -0.37, -0.07, -0.44, 0.33, 0.63, 0.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light is red.\n\n",
+    "BDDX_Processed/video/0f89108b-3ab59119_11454.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.95, 6.16, 6.44, 6.77, 6.84, 6.82, 6.65]\n Curvature: [-0.2, -0.05, 0.03, 0.03, 0.04, 0.03, 0.01]\n Acceleration: [0.33, 0.39, 0.39, 0.3, 0.07, -0.09, -0.07]\n Course: [0.0, -0.06, -0.86, -0.68, -0.66, -0.78, -0.97]\nWhat is the action of ego car?\nAssistant: The car starts to accelerate\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is speeding up.\n\n",
+    "BDDX_Processed/video/0f21e7e2-b9153f33_11315.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.37, 7.05, 6.72, 6.41, 5.7, 4.93, 4.04]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.39, -0.46, -0.59, -0.79, -1.07, -1.35, -1.68]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down to a rolling stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because it is approaching an intersection with a stop sign.\n\n",
+    "BDDX_Processed/video/12cf075a-f9054e11_13091.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.38, 0.88, 1.33, 1.88, 2.52, 3.15, 3.84]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.51, 0.87, 1.2, 1.47, 1.62, 1.76, 1.76]\n Course: [0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is beginning to move forward again.\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has started to move.\n\n",
+    "BDDX_Processed/video/182dc406-c8968ae9_16100.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.14, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: due to cross traffic and pedestrians in the cross walk.\n\n",
+    "BDDX_Processed/video/0a7d8340-c8520b7f_08821.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.3, 2.48, 2.68, 3.11, 3.55, 3.98, 4.47]\n Curvature: [-0.02, 0.01, 0.03, 0.0, -0.02, -0.01, 0.0]\n Acceleration: [0.34, 0.48, 0.62, 0.74, 0.85, 0.93, 1.01]\n Course: [0.0, 0.05, 0.05, 0.04, -0.0, -0.07, -0.1]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in front of it begins to move more quickly.\n\n",
+    "BDDX_Processed/video/0432d284-c924667e_05902.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.7, 3.87, 4.03, 3.95, 3.58, 3.25, 2.94]\n Curvature: [-0.0, -0.0, -0.0, 0.03, 0.15, 0.66, 0.27]\n Acceleration: [0.06, 0.17, 0.0, -0.25, -0.46, -0.42, -0.08]\n Course: [0.0, 0.0, 0.0, -9.36, -7.7, -9.05, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning left\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/1ebca830-d04fa3a1_19242.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.97, 13.92, 12.96, 11.94, 10.97, 9.99, 9.43]\n Curvature: [-0.0, 0.0, 0.01, 0.01, -0.01, -0.02, -0.01]\n Acceleration: [-2.59, -2.57, -2.54, -2.44, -2.21, -1.98, -1.59]\n Course: [0.0, 0.0, 0.0, -0.04, -0.04, -0.04, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because a two cars ahead the car is slowing to make a right turn at a green light.\n\n",
+    "BDDX_Processed/video/076399f2-4857e5a4_02261.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.97, 1.75, 1.53, 1.31, 1.11, 1.05, 0.98]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.74, -0.69, -0.64, -0.59, -0.54, -0.46, -0.39]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: due to a stop sign ahead.\n\n",
+    "BDDX_Processed/video/162995f1-c0af8a1c_14986.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [20.48, 21.05, 21.76, 22.4, 21.27, 22.56, 23.09]\n Curvature: [0.0, -0.0, 0.01, 0.0, 0.04, 0.05, 0.02]\n Acceleration: [0.02, 0.19, -0.09, -0.01, -0.16, 0.39, -0.13]\n Course: [0.0, -0.98, -3.07, -0.97, -4.88, -5.06, -8.64]\nWhat is the action of ego car?\nAssistant: The car drives down a highway\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane ahead is clear and traffic is moving steadily.\n\n",
+    "BDDX_Processed/video/1f67d11b-cf3f5bf0_19600.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.53, 0.12, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.02, 0.02, 0.02, 0.02, 0.03, 0.02, 0.01]\n Acceleration: [-0.6, -0.34, -0.12, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car ceases to move\n\nHuman: Why does the ego car doing this?\nAssistant: as it gets up to another car\n\n",
+    "BDDX_Processed/video/02fa8491-2a4967e3_05411.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [23.16, 22.36, 22.5, 21.82, 21.17, 18.61, 17.19]\n Curvature: [0.0, 0.01, -0.01, 0.01, 0.01, -0.01, -0.0]\n Acceleration: [-0.65, -0.1, 0.01, -0.4, -0.58, -1.27, -0.32]\n Course: [0.0, -2.04, 0.18, -0.68, -0.29, 0.49, 0.74]\nWhat is the action of ego car?\nAssistant: The car moves forward then merges right\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/0e653c17-0df33c8d_10962.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.76, 9.31, 8.86, 8.42, 7.99, 7.25, 6.51]\n Curvature: [0.02, 0.01, 0.01, 0.01, 0.0, 0.0, 0.0]\n Acceleration: [-1.38, -1.61, -1.85, -2.09, -2.3, -2.56, -2.83]\n Course: [0.0, -0.13, -0.13, -0.13, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car again decelerates\n\nHuman: Why does the ego car doing this?\nAssistant: when it comes up to another stop sign\n\n",
+    "BDDX_Processed/video/1c676e43-de296285_18070.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.57, 10.77, 11.09, 10.78, 10.77, 10.89, 10.6]\n Curvature: [0.15, -0.01, -0.14, -0.09, -0.01, 0.01, -0.01]\n Acceleration: [0.26, 0.2, -0.0, -0.18, 0.08, -0.08, -0.48]\n Course: [0.0, -1.49, 9.37, 4.72, 0.0, -0.55, 2.72]\nWhat is the action of ego car?\nAssistant: The car pulls over to the right along the parked cars\n\nHuman: Why does the ego car doing this?\nAssistant: because it is approaching its destination.\n\n",
+    "BDDX_Processed/video/083bc808-43718f9d_07516.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.83, 4.68, 4.76, 5.47, 5.74, 5.49, 6.19]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.25, 0.03, 0.16, 0.34, 0.05, 0.02, 0.84]\n Course: [0.0, -0.06, 0.0, 0.0, -0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the lights are green.\n\n",
+    "BDDX_Processed/video/01e9deb7-ed388859_00422.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.66, 2.61, 2.56, 2.52, 2.52, 2.8, 3.09]\n Curvature: [0.01, -0.01, -0.02, -0.03, -0.04, -0.03, -0.02]\n Acceleration: [-0.47, -0.22, 0.03, 0.28, 0.49, 0.65, 0.82]\n Course: [0.0, -0.02, -0.02, -0.02, -0.01, -0.02, -0.02]\nWhat is the action of ego car?\nAssistant: The car moves forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has opened up a little.\n\n",
+    "BDDX_Processed/video/08b5c3d0-b5d559fe_07840.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.16, 4.63, 4.15, 3.76, 3.57, 3.37, 3.38]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.67, -0.76, -0.85, -0.79, -0.52, -0.24, -0.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because a pedestrian is standing nearby.\n\n",
+    "BDDX_Processed/video/184b1b9a-1c367192_16135.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.57, 8.93, 9.1, 8.92, 9.21, 9.08, 9.01]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.05, 0.25, -0.03, 0.06, 0.11, -0.12, 0.33]\n Course: [0.0, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves one lane to the left\n\nHuman: Why does the ego car doing this?\nAssistant: because there is an opening in the lane.\n\n",
+    "BDDX_Processed/video/101c9c8b-738ae7ec_11734.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.3, 2.67, 3.03, 3.4, 3.75, 3.96, 4.18]\n Curvature: [0.0, -0.29, -0.58, -0.87, -1.09, -0.83, -0.57]\n Acceleration: [1.51, 1.41, 1.31, 1.21, 1.12, 1.04, 0.96]\n Course: [0.0, 0.0, 0.0, 0.0, -9.91, -19.82, -19.82]\nWhat is the action of ego car?\nAssistant: The car is turning right.\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is entering another street.\n\n",
+    "BDDX_Processed/video/16a98dc3-14e12bce_15351.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.87, 2.79, 4.2, 5.34, 6.11, 5.72, 4.29]\n Curvature: [0.0, -0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.09, 0.78, 1.31, 1.08, 0.2, -0.99, -1.66]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: to pass a car moving slowly in the right lane.\n\n",
+    "BDDX_Processed/video/12cf075a-ff5e2b67_13094.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.44, 13.94, 13.41, 12.28, 11.13, 9.78, 8.5]\n Curvature: [0.0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.0]\n Acceleration: [-0.96, -1.27, -1.6, -2.03, -2.39, -2.3, -2.21]\n Course: [0.0, 0.0, 0.01, 0.05, -0.02, -0.09, 0.05]\nWhat is the action of ego car?\nAssistant: The car is heading down the street at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/015f23ad-5049df48_04747.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.69, 14.45, 14.02, 13.58, 13.21, 12.74, 12.37]\n Curvature: [0.0, -0.02, -0.01, 0.01, -0.0, 0.01, 0.01]\n Acceleration: [-0.15, -0.26, -0.34, -0.3, -0.35, -0.3, -0.29]\n Course: [0.0, 0.02, -0.12, 0.11, 0.12, -0.59, -0.45]\nWhat is the action of ego car?\nAssistant: The car drives at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: as there is no traffic around the driver to hamper speed\n\n",
+    "BDDX_Processed/video/0fe850c9-6c8dc241_11594.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.81, 8.59, 9.53, 9.56, 11.67, 11.9, 12.35]\n Curvature: [-0.02, -0.0, -0.02, -0.04, -0.07, 0.01, -0.0]\n Acceleration: [-1.36, 0.38, -0.35, 0.81, 0.16, 0.42, -0.16]\n Course: [0.0, 0.48, 0.53, 2.58, 1.83, -1.09, 1.26]\nWhat is the action of ego car?\nAssistant: The car is moving down the road\n\nHuman: Why does the ego car doing this?\nAssistant: The car has completed its lane change and is moving forward with the flow of traffic\n\n",
+    "BDDX_Processed/video/05e4d323-6779ccbc_01603.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.29, 0.26, 0.13, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.05, -0.05, -0.1, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/1e272076-566f6711_18950.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.83, 4.45, 3.57, 2.98, 2.66, 2.26, 1.02]\n Curvature: [-0.0, -0.0, -0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [-3.02, -2.55, -0.72, -0.44, -0.33, -0.8, -1.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light ahead is red.\n\n",
+    "BDDX_Processed/video/1cc77c32-a2cb4cb9_18230.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.73, -0.73, -0.73, -0.73, -0.73, -0.73, -0.73]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.09, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/05acab14-b78b76c7_01327.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.23, 1.63, 3.78, 6.33, 7.13, 7.67, 8.71]\n Curvature: [8.86, 0.05, -0.02, -0.04, 0.01, -0.01, -0.01]\n Acceleration: [0.75, 0.84, 1.5, 1.08, 0.37, 0.4, 0.64]\n Course: [0.0, -0.46, 0.61, 1.15, 0.3, 0.02, -0.01]\nWhat is the action of ego car?\nAssistant: The car accelerates slowly to a constant speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving.\n\n",
+    "BDDX_Processed/video/1aa6dd36-83b93284_17202.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.78, 5.43, 6.27, 7.23, 8.31, 9.26, 10.1]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.38, 0.95, 1.37, 1.56, 1.5, 1.33, 0.99]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane ahead is now clear.\n\n",
+    "BDDX_Processed/video/0dce14f9-7de59bf3_10704.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.1, 6.68, 7.76, 8.71, 9.6, 10.02, 10.35]\n Curvature: [0.0, 0.02, -0.02, 0.0, 0.01, 0.01, -0.02]\n Acceleration: [1.41, 1.15, 0.86, 0.81, 0.58, 0.29, 0.24]\n Course: [0.0, 0.15, 0.21, -0.21, -0.38, -0.11, -0.21]\nWhat is the action of ego car?\nAssistant: The car is accelerating.\n\nHuman: Why does the ego car doing this?\nAssistant: Because traffic in its lane ahead of it is moving normally.\n\n",
+    "BDDX_Processed/video/083bc808-1e8e1d56_07512.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.33, 0.46, 1.41, 2.82, 3.87]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.18, 0.23, 0.65, 1.25, 1.34, 1.29]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light has turned green and the cars in front of it begin to accelerate.\n\n",
+    "BDDX_Processed/video/1bc82b26-7383dedd_17786.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.73, 6.67, 6.43, 6.38, 6.32, 6.16, 5.77]\n Curvature: [0.0, -0.01, -0.01, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-0.07, -0.17, -0.14, -0.07, -0.12, -0.3, -0.57]\n Course: [0.0, 0.0, 0.47, 0.0, -0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is heading down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/18794cdb-9f18cf18_16216.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.72, 12.8, 12.88, 12.95, 13.04, 13.14, 13.24]\n Curvature: [-0.01, -0.01, -0.02, -0.03, -0.03, -0.02, -0.02]\n Acceleration: [0.07, 0.14, 0.21, 0.28, 0.35, 0.36, 0.37]\n Course: [0.0, 0.11, 0.11, 0.11, 0.21, 0.2, 0.2]\nWhat is the action of ego car?\nAssistant: The car is moving forward through an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green\n\n",
+    "BDDX_Processed/video/109d5675-ccf79e0e_11857.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.29, 0.18, 0.07, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07]\n Acceleration: [-0.28, -0.23, -0.18, -0.12, -0.07, -0.01, 0.0]\n Course: [0.0, 0.0, 0.0, 0.04, 0.02, 0.04, 0.0]\nWhat is the action of ego car?\nAssistant: The car has stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: because the cars ahead are stopped because the light is red.\n\n",
+    "BDDX_Processed/video/05476b39-9d783f66_06249.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.07, 0.14, 0.21, 0.26, 0.19, 0.12]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.14, 0.1, 0.06, 0.03, -0.0, -0.04, -0.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves to the right slightly\n\nHuman: Why does the ego car doing this?\nAssistant: because road conditions are rough.\n\n",
+    "BDDX_Processed/video/006a7635-c42f9f97_04431.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.12, 1.25, 1.37, 1.28, 1.19, 1.13, 1.07]\n Curvature: [0.13, 0.02, -0.08, -0.1, -0.11, -0.06, -0.01]\n Acceleration: [0.17, 0.1, 0.03, -0.06, -0.15, -0.12, -0.06]\n Course: [0.0, 0.32, 0.24, 0.0, 0.05, 0.09, 0.43]\nWhat is the action of ego car?\nAssistant: The car continues to drive in reverse back down the street\n\nHuman: Why does the ego car doing this?\nAssistant: Because the car is looking for a different parking spot.\n\n",
+    "BDDX_Processed/video/13a06896-f492b349_13612.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.59, 7.04, 3.44, 0.87, 0.28, 0.51, 0.98]\n Curvature: [-0.02, -0.01, 0.06, 0.14, 0.07, -0.13, -0.17]\n Acceleration: [-2.81, -3.52, -2.72, -1.37, -0.09, 0.3, 0.43]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a police car with flashing lights making a u-turn.\n\n",
+    "BDDX_Processed/video/065992a2-2002dbea_02661.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.15, 0.3, 0.45, 0.61, 1.01, 1.42]\n Curvature: [0.27, 0.22, 0.17, 0.12, 0.08, 0.06, 0.04]\n Acceleration: [0.28, 0.49, 0.7, 0.9, 1.08, 1.21, 1.33]\n Course: [0.0, 0.02, 0.02, 0.02, -0.01, -0.02, -0.02]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/06d501fd-31889467_04214.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.21, 4.2, 4.12, 3.74, 3.06, 2.54, 2.26]\n Curvature: [-0.0, 0.0, 0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.06, -0.07, -0.32, -0.62, -0.77, -0.53, -0.22]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped.\n\n",
+    "BDDX_Processed/video/026501c2-16c41a5d_01745.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.11, 8.36, 8.83, 9.64, 10.42, 11.27, 12.14]\n Curvature: [-0.01, -0.01, -0.0, 0.0, -0.01, -0.01, -0.01]\n Acceleration: [0.38, 0.65, 0.92, 1.2, 1.26, 0.87, -0.29]\n Course: [0.0, 0.0, 0.06, 0.06, 0.23, 0.12, 0.12]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in front of it is moving more quickly.\n\n",
+    "BDDX_Processed/video/1be20f9b-c887ced4_17832.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.15, 8.29, 7.43, 6.57, 5.63, 4.13, 2.63]\n Curvature: [-0.01, -0.01, -0.02, -0.02, -0.03, -0.03, -0.03]\n Acceleration: [-3.25, -3.59, -3.93, -4.28, -4.52, -4.09, -3.66]\n Course: [0.0, 0.07, 0.07, 0.07, 0.05, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car stops abruptly\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is red\n\n",
+    "BDDX_Processed/video/0b4c8138-84032f91_09265.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.28, 10.37, 10.45, 10.55, 10.63, 10.03, 9.42]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.22, -0.08, -0.35, -0.65, -0.92, -1.32, -1.72]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because it has a free road ahead.\n\n",
+    "BDDX_Processed/video/09e4ac3a-a661ca55_08597.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.27, 0.21, 0.0, 0.0, 1.13, 1.82, 5.05]\n Curvature: [0.85, 1.25, 1.26, 1.26, 0.65, 0.18, -0.1]\n Acceleration: [-1.47, -0.13, -0.09, 0.45, 0.91, 1.58, 1.71]\n Course: [0.0, 0.0, 0.09, -0.11, -1.5, -3.59, 2.72]\nWhat is the action of ego car?\nAssistant: The car moves around the parked car\n\nHuman: Why does the ego car doing this?\nAssistant: since the oncoming lane is free.\n\n",
+    "BDDX_Processed/video/0a62ffee-85e97e08_08751.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.38, 13.58, 13.54, 13.23, 12.43, 11.42, 10.01]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.31, 0.06, -0.33, -0.84, -1.4, -1.81, -2.01]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is going down the street at a steady speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is empty.\n\n",
+    "BDDX_Processed/video/047e7f64-6df28989_03410.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.43, 4.36, 4.3, 4.24, 4.19, 4.3, 4.39]\n Curvature: [0.48, 0.35, 0.24, 0.11, 0.0, 0.0, 0.0]\n Acceleration: [-0.2, -0.13, -0.06, 0.01, 0.08, 0.14, 0.2]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/176c2785-ca2572b3_15764.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.76, 2.57, 2.38, 1.91, 1.46, 1.03, 0.61]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.22, -0.44, -0.64, -0.76, -0.86, -0.8, -0.7]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped at a red light.\n\n",
+    "BDDX_Processed/video/0e947ab4-9e87f729_11111.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.41, 13.05, 11.81, 10.35, 8.71, 7.06, 5.87]\n Curvature: [-0.01, -0.01, -0.02, -0.02, -0.01, -0.0, -0.01]\n Acceleration: [-3.16, -3.41, -3.64, -3.78, -3.69, -3.6, -3.12]\n Course: [0.0, 0.07, 0.05, 0.11, 0.11, 0.11, 0.09]\nWhat is the action of ego car?\nAssistant: The car merges into the lane to its right and slows\n\nHuman: Why does the ego car doing this?\nAssistant: while there is a gap in traffic before a car that is nearly stopped in the right lane.\n\n",
+    "BDDX_Processed/video/083bc808-43718f9d_07518.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.74, 12.65, 12.57, 12.49, 12.37, 11.99, 11.6]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.14, -0.34, -0.52, -0.73, -0.87, -0.81, -0.74]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car shifts to the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: since there are cars waiting to turn in the left.\n\n",
+    "BDDX_Processed/video/19167d13-0a0bca18_16562.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.52, 12.17, 10.91, 9.86, 8.08, 4.78, 1.46]\n Curvature: [-0.03, -0.0, 0.02, 0.02, 0.0, -0.0, 0.0]\n Acceleration: [-1.01, -1.4, -1.27, -1.61, -2.87, -3.57, -2.63]\n Course: [0.0, 0.0, 0.07, 0.08, 0.08, 0.08, 0.07]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light ahead is red.\n\n",
+    "BDDX_Processed/video/1b4258e6-8dafc26c_17622.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.82, 5.84, 3.98, 2.31, 1.17, 0.45, 0.22]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.89, -2.51, -2.67, -2.14, -1.41, -0.77, -0.26]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car reduces its speed and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic ahead is stopped at a red light.\n\n",
+    "BDDX_Processed/video/2038e4b0-4404bded_19999.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.57, 1.31, 0.94, 0.62, 0.56, 0.98, 1.16]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-1.68, -0.92, -0.43, -0.2, 0.19, 0.28, -0.19]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a line of cars waiting to get through a light.\n\n",
+    "BDDX_Processed/video/16d1557b-1bcf4dff_15441.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.28, 9.14, 9.0, 8.86, 8.74, 8.56, 8.39]\n Curvature: [0.04, 0.03, 0.01, 0.0, -0.01, -0.01, -0.0]\n Acceleration: [-0.16, -0.28, -0.4, -0.51, -0.6, -0.62, -0.64]\n Course: [0.0, -0.09, -0.09, -0.09, -0.03, -0.07, -0.07]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is empty.\n\n",
+    "BDDX_Processed/video/213344f6-5adddc66_20420.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.87, 9.59, 9.26, 8.72, 8.04, 6.76, 5.42]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.57, -0.7, -0.86, -1.36, -1.86, -2.19, -2.35]\n Course: [0.0, -0.03, -0.03, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves through the intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/14143b8a-71d3ff79_13789.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.4, 12.5, 12.6, 12.76, 13.0, 13.24, 13.62]\n Curvature: [0.06, 0.05, 0.04, 0.03, 0.01, -0.0, -0.01]\n Acceleration: [0.27, 0.34, 0.4, 0.5, 0.65, 0.8, 0.58]\n Course: [0.0, -0.61, -0.61, -0.2, -0.2, -0.2, 0.03]\nWhat is the action of ego car?\nAssistant: The car is now going straight\n\nHuman: Why does the ego car doing this?\nAssistant: while continuing down the road\n\n",
+    "BDDX_Processed/video/02fc82b3-279906a9_03629.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.69, 3.32, 4.4, 5.11, 5.77, 6.56, 7.43]\n Curvature: [-0.0, 0.0, 0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [1.75, 1.49, 1.02, 0.78, 0.8, 0.91, 0.91]\n Course: [0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates to a constant speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving.\n\n",
+    "BDDX_Processed/video/073d4e2a-55c1f733_02082.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, -0.15, 0.0, 0.0, 0.0, -0.15]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/15be9533-f4c2aac4_14683.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.55, 9.54, 9.55, 9.69, 9.86, 10.22, 10.56]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.14, 0.14, 0.14, 0.33, 0.5, 0.54, 0.55]\n Course: [0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is merging into the lane to its right\n\nHuman: Why does the ego car doing this?\nAssistant: to maneuver around a vehicle that is stopped in its lane while it waits to make a left hand turn.\n\n",
+    "BDDX_Processed/video/1127aff5-f54f5c7e_12309.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.17, 0.81, 0.96, 0.74, 0.41, 0.35]\n Curvature: [-0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01]\n Acceleration: [0.09, 0.43, 0.41, -0.02, -0.29, -0.19, 0.14]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is stopped at a red light.\n\n",
+    "BDDX_Processed/video/04076a65-89793286_02122.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [21.46, 21.23, 20.98, 20.9, 19.37, 16.69, 13.76]\n Curvature: [0.0, -0.02, 0.01, 0.0, 0.01, -0.0, -0.02]\n Acceleration: [-0.27, -0.24, -0.14, -0.77, -2.03, -2.74, -2.05]\n Course: [0.0, -0.08, 0.0, -0.04, 0.09, 0.16, 0.09]\nWhat is the action of ego car?\nAssistant: The car veers right\n\nHuman: Why does the ego car doing this?\nAssistant: to take the off ramp.\n\n",
+    "BDDX_Processed/video/09b5ab15-d01184c6_08469.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [25.31, 25.5, 25.7, 25.89, 26.08, 26.28, 26.47]\n Curvature: [0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0.01]\n Acceleration: [1.43, 1.44, 1.45, 1.46, 1.47, 1.48, 1.49]\n Course: [0.0, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04]\nWhat is the action of ego car?\nAssistant: to enter an on ramp.\n\nHuman: Why does the ego car doing this?\nAssistant: The car stays to the right\n\n",
+    "BDDX_Processed/video/02514ff2-5fafa3bc_01387.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [29.78, 29.91, 28.14, 18.32, 16.47, 15.1, 14.02]\n Curvature: [0.03, -0.01, -0.02, -0.01, 0.0, -0.01, -0.01]\n Acceleration: [0.14, -0.85, -3.03, -2.44, -1.09, -0.3, -1.63]\n Course: [0.0, 0.0, 0.0, -0.7, 0.33, 0.35, 0.49]\nWhat is the action of ego car?\nAssistant: The car starts to slow down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is slowing down.\n\n",
+    "BDDX_Processed/video/0718ab33-bbd7e0ae_01440.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.63, 10.2, 8.88, 7.69, 6.58, 5.47, 4.51]\n Curvature: [-0.01, -0.05, -0.08, -0.16, -0.21, -0.19, -0.16]\n Acceleration: [-2.7, -2.57, -2.45, -2.3, -2.14, -1.71, -1.18]\n Course: [0.0, 0.33, 0.7, 1.83, 1.68, 2.45, 3.18]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning to the right.\n\n",
+    "BDDX_Processed/video/047a7ecb-68221e4a_06029.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.67, 5.59, 5.35, 5.05, 4.75, 4.47, 4.26]\n Curvature: [-0.0, -0.01, 0.01, 0.02, 0.02, 0.03, 0.02]\n Acceleration: [-0.1, -0.21, -0.33, -0.39, -0.37, -0.31, -0.2]\n Course: [0.0, -0.2, -0.2, -0.51, -0.59, -0.66, -0.59]\nWhat is the action of ego car?\nAssistant: The car is moving slowly in the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because the road has some potholes.\n\n",
+    "BDDX_Processed/video/13d62f3f-49dd7dbf_13680.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.21, 7.25, 7.3, 7.35, 7.4, 7.49, 7.59]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.24, 0.25, 0.26, 0.27, 0.28, 0.32, 0.35]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car passes through a second green light\n\nHuman: Why does the ego car doing this?\nAssistant: .Because there are no obstructions in its path\n\n",
+    "BDDX_Processed/video/126e2493-ccfe99a3_12913.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.76, 6.47, 6.22, 5.93, 5.65, 5.44, 5.25]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.38, -0.53, -0.66, -0.81, -0.93, -0.88, -0.84]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing slightly.\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/1ddfceee-89178712_18784.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.02, 0.05, 0.0]\nWhat is the action of ego car?\nAssistant: The car isn't moving\n\nHuman: Why does the ego car doing this?\nAssistant: as it sits on the side of the road\n\n",
+    "BDDX_Processed/video/034ebec5-b2b76478_05523.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.97, 9.88, 10.49, 10.46, 10.13, 9.38, 8.51]\n Curvature: [0.01, 0.02, -0.01, 0.0, 0.0, 0.0, -0.03]\n Acceleration: [-0.22, 0.25, 0.29, -0.2, -0.43, -0.74, -0.79]\n Course: [0.0, -0.05, 0.75, 0.2, -1.12, -0.1, -0.19]\nWhat is the action of ego car?\nAssistant: The car is moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: as it drives down the street\n\n",
+    "BDDX_Processed/video/021f503c-012d941f_00932.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.0, 10.97, 11.18, 11.66, 12.29, 12.78, 12.86]\n Curvature: [-0.0, -0.0, 0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.3, 0.12, 0.51, 0.86, 0.72, 0.45, -0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.02, -0.02]\nWhat is the action of ego car?\nAssistant: The car slows and maintains spacing\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a vehicle ahead of it.\n\n",
+    "BDDX_Processed/video/0b669851-ebc3d3e9_09360.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.57, 2.78, 2.04, 1.88, 1.75, 1.84, 2.09]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [-1.61, -1.25, -0.89, -0.45, 0.0, 0.49, 0.99]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down again\n\nHuman: Why does the ego car doing this?\nAssistant: to stay in with the flow of the traffic.\n\n",
+    "BDDX_Processed/video/0024b742-83709bd4_00334.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.49, 18.91, 19.57, 19.83, 19.58, 17.39, 16.42]\n Curvature: [-0.0, -0.05, -0.03, -0.02, -0.01, 0.04, 0.06]\n Acceleration: [0.12, 0.16, 0.38, 0.08, -0.54, -1.24, 0.3]\n Course: [0.0, 2.72, 2.7, 1.41, 0.84, -2.11, -4.14]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: to be moving at the speed limit\n\n",
+    "BDDX_Processed/video/07b584c5-fff3b3bd_03170.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.99, 10.7, 10.45, 10.17, 9.81, 8.9, 8.11]\n Curvature: [-0.01, -0.0, 0.01, 0.02, 0.03, 0.01, -0.01]\n Acceleration: [-0.91, -1.26, -1.57, -1.93, -2.22, -2.09, -1.97]\n Course: [0.0, -0.05, -0.02, -0.05, -0.02, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: A vehicle in the right lane is merging into the left lane ahead of the car.\n\n",
+    "BDDX_Processed/video/0844afed-b7fa350e_07552.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.57, 13.63, 12.69, 11.75, 10.86, 9.35, 7.85]\n Curvature: [-0.0, 0.0, 0.01, 0.01, -0.05, -0.56, -1.07]\n Acceleration: [-2.97, -3.43, -3.88, -4.34, -4.7, -4.87, -5.04]\n Course: [0.0, -0.05, -0.05, -0.05, -0.01, -0.02, -0.02]\nWhat is the action of ego car?\nAssistant: The car is braking to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has changed from yellow to red.\n\n",
+    "BDDX_Processed/video/12fee2da-a2d786b7_13177.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.51, 18.97, 20.64, 20.72, 19.87, 18.85, 19.77]\n Curvature: [-0.0, 0.0, -0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [-0.13, 0.83, 0.47, -0.18, -0.66, 0.13, -0.25]\n Course: [0.0, 0.48, 0.34, 0.16, 0.0, 0.49, 1.65]\nWhat is the action of ego car?\nAssistant: The car proceeds down the road\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is clear ahead.\n\n",
+    "BDDX_Processed/video/1bcf2906-50102fca_17807.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.02, 0.21, 0.37]\n Curvature: [-3.17, -2.95, -2.76, -2.55, -2.31, -1.94, -1.62]\n Acceleration: [-0.28, -0.11, 0.04, 0.2, 0.38, 0.66, 0.91]\n Course: [0.0, 0.0, 0.0, 0.0, -2.78, -5.55, -2.78]\nWhat is the action of ego car?\nAssistant: The car veers left a little more\n\nHuman: Why does the ego car doing this?\nAssistant: while going around the last pedestrian\n\n",
+    "BDDX_Processed/video/0252f2b5-2e69185a_05082.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 1.09, 3.47, 5.74, 7.33, 8.58]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.27, 1.48, 1.86, 1.46, 1.17, 0.3]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.23]\nWhat is the action of ego car?\nAssistant: The car moves forward then merges right\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green and another vehicle is in the way.\n\n",
+    "BDDX_Processed/video/16acca2d-502e153f_15393.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.34, 0.56, 0.76, 0.9, 1.04, 1.22, 1.35]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.2, 0.27, 0.34, 0.32, 0.29, 0.21, 0.13]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues slowly down the road\n\nHuman: Why does the ego car doing this?\nAssistant: since traffic ahead is barely moving.\n\n",
+    "BDDX_Processed/video/1dd4ad3f-f04214e4_18773.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.39, 0.3, 0.44, 0.56, 0.61, 0.67, 0.73]\n Curvature: [0.08, 0.05, 0.01, -0.03, -0.04, 0.01, 0.1]\n Acceleration: [-0.33, 0.0, 0.13, 0.1, 0.07, 0.06, -0.02]\n Course: [0.0, 0.08, -0.24, -0.08, -0.08, -0.08, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: to allow another car to pull out in front of it.\n\n",
+    "BDDX_Processed/video/07e49e2a-08324f2c_03596.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.88, 1.8, 0.18, 0.0, 0.0, 0.0, 0.0]\n Curvature: [7.21, 0.84, -1.38, -1.38, -1.37, -1.37, -1.37]\n Acceleration: [-4.2, -3.27, -1.47, -0.13, 0.0, 0.0, 0.0]\n Course: [0.0, 0.23, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/162995f1-691e9bb1_14983.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [22.26, 22.74, 23.02, 23.24, 23.5, 23.77, 23.79]\n Curvature: [-0.01, 0.01, 0.01, 0.0, -0.01, 0.02, -0.02]\n Acceleration: [0.47, 0.44, 0.36, 0.31, 0.32, 0.18, 0.09]\n Course: [0.0, 0.07, -0.27, 0.07, -0.03, -0.14, -0.61]\nWhat is the action of ego car?\nAssistant: The car now moves straight\n\nHuman: Why does the ego car doing this?\nAssistant: while continuing down the highway\n\n",
+    "BDDX_Processed/video/099db658-a2edb966_08273.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped behind a police car\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is not moving.\n\n",
+    "BDDX_Processed/video/0308ce2b-8fc462fd_05445.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.92, 5.06, 5.02, 5.17, 4.93, 4.53, 4.28]\n Curvature: [0.0, -0.0, -0.01, -0.01, -0.01, -0.01, -0.0]\n Acceleration: [0.4, 0.08, 0.07, -0.08, -0.32, -0.35, -0.21]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.38, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: due to a clear road in front of it.\n\n",
+    "BDDX_Processed/video/14098efb-9a58bca3_13764.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.18, -0.18, -0.18, -0.18, -0.18, -0.18, -0.18]\n Acceleration: [-0.12, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.11, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car remains at a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic light remains red.\n\n",
+    "BDDX_Processed/video/0b8d1c37-952dba30_09463.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.73, 8.95, 8.17, 7.24, 6.1, 4.96, 3.35]\n Curvature: [-0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.93, -2.15, -2.36, -2.67, -3.14, -3.6, -3.12]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a stop sign.\n\n",
+    "BDDX_Processed/video/1e6a6965-44d99d0a_19069.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.84, 1.56, 2.23, 2.79, 3.09, 3.42, 3.33]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [1.33, 1.32, 1.32, 1.13, 0.72, 0.27, -0.11]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the road allows the car to stop.\n\n",
+    "BDDX_Processed/video/11e60919-a44a764e_12732.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.34, 7.75, 10.62, 10.64, 10.67, 11.98, 10.92]\n Curvature: [-0.01, 0.02, 0.0, -0.01, -0.01, -0.01, -0.0]\n Acceleration: [0.72, 1.25, 0.63, -0.2, 0.32, 0.13, -0.72]\n Course: [0.0, -0.27, -0.14, 0.78, 1.12, 0.49, 0.14]\nWhat is the action of ego car?\nAssistant: The car is driving forward.\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving well.\n\n",
+    "BDDX_Processed/video/0a06d3bc-7f823a5a_08639.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.44, 17.23, 17.03, 16.94, 16.84, 16.72, 16.64]\n Curvature: [-0.0, -0.01, -0.01, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.3, -0.3, -0.29, -0.25, -0.2, -0.12, -0.05]\n Course: [0.0, 0.09, 0.07, -0.04, -0.04, -0.04, -0.01]\nWhat is the action of ego car?\nAssistant: The car moves into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it is getting ready to exit the highway.\n\n",
+    "BDDX_Processed/video/126e2493-9876716c_12908.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.54, 7.59, 6.64, 5.81, 4.86, 3.85, 2.6]\n Curvature: [0.0, -0.0, -0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.93, -1.24, -1.42, -1.38, -1.54, -1.58, -1.31]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a near stop\n\nHuman: Why does the ego car doing this?\nAssistant: as the car in the lane ahead is braking.\n\n",
+    "BDDX_Processed/video/1583eb60-684157ec_14573.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.33, 6.88, 5.55, 4.38, 2.51, 1.05, 0.62]\n Curvature: [0.01, 0.01, 0.03, 0.09, 0.13, -0.32, -1.69]\n Acceleration: [-2.14, -2.05, -2.14, -2.43, -2.12, -1.58, -0.68]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is slowing down.\n\n",
+    "BDDX_Processed/video/1a7df43e-e4d4c005_17126.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.49, 10.41, 11.09, 12.22, 12.46, 11.97, 13.14]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.95, 0.5, 0.56, 0.29, -0.23, 0.23, 0.62]\n Course: [0.0, -0.04, 0.25, 0.0, 0.0, 0.0, -0.02]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane ahead is clear and traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/1aaf831d-19b678eb_17289.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 1.63, 5.57, 7.74, 10.73, 13.09, 14.65]\n Curvature: [0.0, 0.0, 0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.54, 1.91, 2.17, 1.61, 1.79, 1.36, 0.85]\n Course: [0.0, 0.0, -0.03, 0.4, -0.07, -0.06, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates down the street\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is green and the road is clear ahead.\n\n",
+    "BDDX_Processed/video/06924f46-1708b96f_06639.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.1, 3.71, 5.33, 6.79, 8.36, 9.32, 9.38]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.39, 2.04, 2.39, 2.34, 1.67, 0.77, -0.53]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is not clear.\n\n",
+    "BDDX_Processed/video/0c7c6dd2-568be30a_09886.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.2, 6.14, 5.92, 5.48, 4.75, 3.77, 2.35]\n Curvature: [-0.02, -0.01, -0.01, -0.0, 0.03, 0.02, -0.09]\n Acceleration: [-0.1, -0.28, -0.54, -0.89, -1.37, -1.74, -1.9]\n Course: [0.0, 0.34, 0.23, 0.23, -0.51, -0.62, -0.62]\nWhat is the action of ego car?\nAssistant: The car is merging to the right and coming to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: since the light is turning red\n\n",
+    "BDDX_Processed/video/1eb33c1c-792725a6_19213.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.37, 4.84, 6.39, 7.78, 9.11, 10.16, 11.08]\n Curvature: [0.02, 0.02, 0.01, 0.02, 0.02, -0.0, -0.02]\n Acceleration: [2.26, 2.53, 2.79, 2.54, 2.28, 1.93, 1.55]\n Course: [0.0, -0.11, -0.13, -0.1, -0.02, 0.0, -0.14]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear of traffic.\n\n",
+    "BDDX_Processed/video/07d06160-25326c3c_07329.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.74, 3.71, 5.7, 6.86, 7.12, 7.1, 7.49]\n Curvature: [0.69, 0.02, -0.05, -0.03, 0.06, -0.03, 0.0]\n Acceleration: [1.6, 1.59, 1.18, 0.54, 0.07, 0.09, 0.4]\n Course: [0.0, 0.01, 0.0, 0.66, 0.05, 0.0, 0.12]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: after the light turned green.\n\n",
+    "BDDX_Processed/video/00721168-56efa5c2_01461.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.41, 1.0, 1.75, 2.76, 3.65, 4.43]\n Curvature: [0.0, -0.0, -0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.11, 0.62, 1.05, 1.34, 1.36, 1.25, 0.95]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: since the light has turned green\n\n",
+    "BDDX_Processed/video/1d2090ea-98b3b88d_18353.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.83, 12.85, 12.86, 12.88, 12.9, 12.97, 13.02]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.01, -0.02]\n Acceleration: [-0.02, 0.02, 0.07, 0.11, 0.16, 0.18, 0.2]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is still moving down the road\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches a bend in the road\n\n",
+    "BDDX_Processed/video/06cdf0b1-6fec3924_06728.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.57, 0.8, 0.2, 0.0, 0.0, 0.15, 0.03]\n Curvature: [-3.48, 0.72, 2.59, 2.58, 2.56, 0.26, 0.27]\n Acceleration: [0.02, 0.07, -0.36, 0.0, 0.0, 0.09, -0.0]\n Course: [0.0, 0.31, 0.06, 0.19, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: for the red light.\n\n",
+    "BDDX_Processed/video/084071b2-5c01f0f9_07542.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.26, 6.11, 3.13, 2.64, 3.1, 3.85, 5.23]\n Curvature: [-0.0, 0.0, -0.61, -1.13, -0.01, -0.0, 0.0]\n Acceleration: [-1.24, 0.96, -1.25, 0.34, 0.14, 0.53, 0.21]\n Course: [0.0, 0.0, 24.48, -2.39, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: Driver takes camera down off dashboard.\n\nHuman: Why does the ego car doing this?\nAssistant: Driver takes camera down off dashboard.\n\n",
+    "BDDX_Processed/video/16d131ad-238fde29_15428.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.52, 4.46, 5.61, 6.63, 7.19, 7.22, 7.39]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [1.19, 1.37, 1.32, 0.97, 0.41, 0.18, 0.42]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car travels slowly down a busy city street\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is driving slowly.\n\n",
+    "BDDX_Processed/video/077192f8-4d59e803_07175.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.93, 3.18, 3.42, 3.44, 3.46, 3.46, 3.44]\n Curvature: [-0.5, -0.21, 0.06, 0.07, 0.08, 0.04, 0.01]\n Acceleration: [-1.37, -0.53, 0.26, 0.13, 0.01, -0.06, -0.13]\n Course: [0.0, 0.14, 0.22, 0.45, 0.34, 0.23, 0.26]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light ahead is red.\n\n",
+    "BDDX_Processed/video/06db23de-8789538f_00060.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.22, 1.0, 2.35, 3.72, 4.93, 6.12, 6.79]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.11, 0.67, 1.43, 1.45, 1.33, 0.99, 0.45]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turned green.\n\n",
+    "BDDX_Processed/video/0b84641a-c360bebd_09406.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [24.39, 24.36, 24.42, 24.56, 24.78, 25.03, 25.27]\n Curvature: [-0.02, -0.02, -0.02, -0.0, -0.01, -0.02, -0.02]\n Acceleration: [0.26, 0.15, 0.15, 0.28, 0.34, 0.38, 0.41]\n Course: [0.0, 0.39, 0.4, 0.4, 0.0, 1.11, 1.11]\nWhat is the action of ego car?\nAssistant: The car moves into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: as it passed the car and the lane is now clear.\n\n",
+    "BDDX_Processed/video/0521fd41-39bc52be_04218.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because it is parked\n\n",
+    "BDDX_Processed/video/05266828-79a6ed22_06221.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.3, 0.5, 0.61, 0.56, 0.62, 0.93]\n Curvature: [-0.57, -0.74, -0.85, -0.89, -0.57, -0.23, -0.0]\n Acceleration: [0.22, 0.28, 0.23, 0.05, 0.12, 0.28, 0.57]\n Course: [0.0, -0.12, -0.11, -0.11, -0.19, -0.29, -0.29]\nWhat is the action of ego car?\nAssistant: The car is starting to drive forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving\n\n",
+    "BDDX_Processed/video/18b6d1e2-5e003c1f_16319.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.83, 10.37, 10.72, 11.62, 12.13, 11.5, 11.02]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.16, -0.12, 0.38, 0.36, 0.11, -0.59, 0.32]\n Course: [0.0, 0.0, 0.0, 0.24, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/01c6a5ce-7dba0d93_00069.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.25, -0.29, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is at an intersection and the light is red.\n\n",
+    "BDDX_Processed/video/2090b4bf-cadb8062_20168.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.78, 7.22, 6.68, 6.48, 6.29, 5.96, 5.64]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.85, -0.79, -0.73, -0.6, -0.5, -0.57, -0.59]\n Course: [0.0, 0.0, 0.0, 0.0, -0.01, -0.03, -0.01]\nWhat is the action of ego car?\nAssistant: The car is slowing\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic in front of the car is braking.\n\n",
+    "BDDX_Processed/video/047e732b-aa79a87d_06041.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.72, 13.08, 12.43, 11.65, 10.88, 10.19, 9.61]\n Curvature: [0.0, 0.01, 0.02, 0.01, 0.0, -0.01, -0.02]\n Acceleration: [-0.73, -1.06, -1.38, -1.4, -1.38, -1.05, -0.73]\n Course: [0.0, 0.05, 0.0, -0.14, -0.07, 0.0, 0.07]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in front of it slows.\n\n",
+    "BDDX_Processed/video/1be932d6-cc8dbce1_17858.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.72, 3.55, 6.0, 6.94, 5.67, 4.16]\n Curvature: [0.0, -0.19, -0.05, -0.01, -0.01, -2.31, 0.22]\n Acceleration: [0.11, 0.75, 1.37, 0.91, -0.35, -0.57, -0.49]\n Course: [0.0, -0.12, 0.66, 0.6, 0.77, -4.01, 2.21]\nWhat is the action of ego car?\nAssistant: The car accelerates and drives down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the light turns green and traffic starts moving.\n\n",
+    "BDDX_Processed/video/1accee17-e156848c_17428.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [27.82, 27.47, 26.76, 26.47, 25.4, 25.75, 25.92]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.01, -0.02, 0.01]\n Acceleration: [-0.06, -0.32, -0.2, -0.29, -0.25, 0.21, 0.03]\n Course: [0.0, 0.06, -0.11, -0.07, 0.0, 0.0, -0.42]\nWhat is the action of ego car?\nAssistant: The car is driving down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/19906a63-e2ba347a_16782.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.2, 6.87, 6.59, 6.26, 5.94, 5.74, 5.56]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.37, -2.0, -1.68, -1.31, -0.97, -0.81, -0.66]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car has slowed down\n\nHuman: Why does the ego car doing this?\nAssistant: because a pedestrian is walking across the street\n\n",
+    "BDDX_Processed/video/065992a2-5bef4d51_02675.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.51, 1.33, 2.09, 2.7, 3.29, 4.07, 4.32]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.02, 0.04]\n Acceleration: [1.54, 1.43, 1.34, 1.32, 1.18, 0.2, -0.73]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -5.17]\nWhat is the action of ego car?\nAssistant: The car accelerates slowly\n\nHuman: Why does the ego car doing this?\nAssistant: to turn left.\n\n",
+    "BDDX_Processed/video/162d2a2a-5cc97bc3_14988.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/011ad6b2-1dfff443_04661.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.62, 5.51, 6.35, 7.09, 7.78, 8.65, 9.46]\n Curvature: [0.02, 0.01, 0.01, -0.0, -0.01, -0.01, -0.01]\n Acceleration: [1.61, 1.58, 1.56, 1.53, 1.5, 1.39, 1.24]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04]\nWhat is the action of ego car?\nAssistant: The car increases speed\n\nHuman: Why does the ego car doing this?\nAssistant: since the road is clear again\n\n",
+    "BDDX_Processed/video/1aa6dd36-aa7f0108_17214.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.61, 1.5, 1.42, 1.95, 2.58, 3.54, 4.58]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [-0.23, 0.09, 0.46, 0.99, 1.53, 1.78, 1.97]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowly accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: because it's making a tight pass from the car in the next lane.\n\n",
+    "BDDX_Processed/video/1d56a6ef-86725847_18500.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.57, 9.69, 9.79, 9.91, 10.04, 10.31, 10.55]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.44, 0.52, 0.58, 0.66, 0.73, 0.78, 0.83]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving\n\nHuman: Why does the ego car doing this?\nAssistant: while keeping up with traffic\n\n",
+    "BDDX_Processed/video/03d34d05-4a61174c_01565.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.14, 15.26, 13.26, 11.24, 9.23, 8.26, 7.28]\n Curvature: [-0.01, -0.0, 0.0, -0.0, -0.01, -0.01, -0.03]\n Acceleration: [-2.28, -3.08, -3.86, -3.41, -2.9, -2.24, -1.68]\n Course: [0.0, 0.04, 0.05, 0.04, 0.05, 0.04, 0.05]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0879ba7f-550d7ffa_07672.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.05, 3.94, 3.89, 3.99, 4.25, 4.6, 4.99]\n Curvature: [0.0, 0.02, 0.02, -0.01, -0.04, -0.02, -0.01]\n Acceleration: [-0.14, -0.1, 0.04, 0.22, 0.4, 0.48, 0.31]\n Course: [0.0, -0.07, -0.06, 0.14, 0.22, 0.29, 0.53]\nWhat is the action of ego car?\nAssistant: The car is riding down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic is moving.\n\n",
+    "BDDX_Processed/video/0378d8dc-92df7b3e_05577.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.51, 9.57, 9.63, 9.69, 9.78, 10.03, 10.25]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.34, -0.1, 0.12, 0.37, 0.6, 0.7, 0.78]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges into the lane to its left\n\nHuman: Why does the ego car doing this?\nAssistant: due to lighter traffic in that lane.\n\n",
+    "BDDX_Processed/video/11b47a8a-24a77ef8_12652.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.88, 6.34, 5.88, 5.34, 4.81, 4.3, 3.86]\n Curvature: [0.04, 0.03, 0.02, 0.01, 0.01, 0.01, 0.02]\n Acceleration: [-1.89, -1.9, -1.92, -1.94, -1.96, -1.97, -1.98]\n Course: [0.0, -0.23, -0.12, -0.23, -0.25, -0.26, -0.13]\nWhat is the action of ego car?\nAssistant: The car is slowing down.\n\nHuman: Why does the ego car doing this?\nAssistant: because there are two pedestrians and there is a red light.\n\n",
+    "BDDX_Processed/video/166cfee3-421e6a94_15132.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.38, 0.72, 0.63, 0.2, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.12, 0.37, 0.13, -0.26, -0.32, -0.13, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red.\n\n",
+    "BDDX_Processed/video/0ff07b90-023a3fe1_11619.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.36, 0.63, 0.89, 1.29, 1.68, 2.22, 2.76]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.01, -0.12, -0.24]\n Acceleration: [0.44, 0.54, 0.63, 0.78, 0.9, 0.98, 1.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -31.18]\nWhat is the action of ego car?\nAssistant: The car is turning right.\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is entering a new street.\n\n",
+    "BDDX_Processed/video/0b7cbaa7-d677bc73_09392.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.57, 0.69, 0.8, 0.92, 1.02, 1.13, 1.25]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.51, 0.49, 0.47, 0.46, 0.44, 0.35, 0.27]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a parked car in its current lane.\n\n",
+    "BDDX_Processed/video/0a34cf13-0309971f_08687.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.5, 8.39, 9.87, 11.29, 11.57, 11.45, 10.59]\n Curvature: [-0.38, 0.03, 0.04, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [1.29, 0.8, 0.89, 0.48, -0.03, -0.26, -0.47]\n Course: [0.0, 0.64, -0.18, 0.0, 0.0, 0.0, 0.01]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is clear ahead.\n\n",
+    "BDDX_Processed/video/1c73e4c3-014ac969_18112.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.17, 0.73, 0.69, 0.18, 0.53, 1.06]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.0, 0.33, 0.19, -0.28, -0.12, 0.4, 0.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward and one lane left\n\nHuman: Why does the ego car doing this?\nAssistant: to go around vehicles stopped in the lane ahead.\n\n",
+    "BDDX_Processed/video/1189ae68-4742a8fa_12594.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.14, 5.58, 5.78, 6.13, 5.9, 5.19, 5.07]\n Curvature: [-0.02, 0.01, -0.01, -0.0, -0.01, 0.0, -0.0]\n Acceleration: [0.15, 0.21, 0.22, 0.02, -0.39, -0.26, 0.14]\n Course: [0.0, 0.27, 0.0, 0.27, -0.07, 0.0, 0.14]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: while it has room between it and stopped vehicles in front of it.\n\n",
+    "BDDX_Processed/video/11815183-57a97711_12572.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.11, 3.85, 3.55, 3.59, 4.18, 4.65, 4.74]\n Curvature: [-0.03, 0.07, 0.05, 0.11, 0.13, 0.14, 0.15]\n Acceleration: [0.03, -0.28, -0.14, 0.35, 0.55, 0.32, -0.19]\n Course: [0.0, 0.66, -1.12, -2.3, -4.92, -5.33, -4.01]\nWhat is the action of ego car?\nAssistant: The car proceeds into the parking lot\n\nHuman: Why does the ego car doing this?\nAssistant: as the area is clear of other cars.\n\n",
+    "BDDX_Processed/video/08245bc2-6167a549_07476.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 2.31, 0.97, 1.08, 4.71, 8.31, 10.46]\n Curvature: [0.07, 0.0, -0.02, -0.09, -0.08, -0.0, 0.0]\n Acceleration: [0.13, -0.14, 0.0, 0.48, 1.91, 1.26, 0.1]\n Course: [0.0, -0.33, 0.0, 0.8, 6.18, 1.59, -0.21]\nWhat is the action of ego car?\nAssistant: The car slowly accelerates and proceeds down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is clear.\n\n",
+    "BDDX_Processed/video/184b1b9a-1c367192_16136.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.29, 12.11, 12.44, 12.98, 13.26, 13.26, 12.87]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.63, 0.44, 0.25, 0.33, 0.09, -0.12, -0.34]\n Course: [0.0, 0.0, 0.0, -0.0, 0.0, -0.05, 0.0]\nWhat is the action of ego car?\nAssistant: The car proceeds down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/0c140522-57d3aac5_09715.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.28, 2.61, 1.94, 1.26, 0.74, 0.58, 0.43]\n Curvature: [0.08, 0.06, 0.04, 0.03, 0.0, -0.1, -0.21]\n Acceleration: [-2.91, -2.55, -2.19, -1.83, -1.52, -1.18, -0.84]\n Course: [0.0, -0.02, -0.02, -0.02, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: for the red light.\n\n",
+    "BDDX_Processed/video/097450c5-87ec5fcf_08186.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.36, 5.76, 5.22, 4.86, 4.84, 4.81, 5.27]\n Curvature: [0.0, -0.39, -0.74, -0.83, -0.51, -0.16, -0.07]\n Acceleration: [-1.49, -1.2, -0.94, -0.54, -0.01, 0.56, 0.99]\n Course: [0.0, 4.15, 4.15, 4.74, 3.16, 4.74, 0.0]\nWhat is the action of ego car?\nAssistant: The car makes a right turn\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear.\n\n",
+    "BDDX_Processed/video/137ec1c1-eee6f6be_13559.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.28, 7.05, 8.74, 12.02, 14.38, 15.08, 15.54]\n Curvature: [0.0, 0.01, 0.01, 0.02, -0.01, -0.0, 0.0]\n Acceleration: [0.3, 0.44, 1.21, 1.44, 0.63, 0.28, 0.01]\n Course: [0.0, -0.77, -0.61, 0.0, 0.0, 0.15, -0.31]\nWhat is the action of ego car?\nAssistant: The car drives speedily along the road\n\nHuman: Why does the ego car doing this?\nAssistant: there are no other cars ahead of it.\n\n",
+    "BDDX_Processed/video/084614d0-f3b8de35_07565.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.07, 8.71, 9.95, 10.12, 9.66, 9.28, 8.96]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [1.28, 1.16, 0.52, -0.2, -0.3, -0.32, -0.12]\n Course: [0.0, 0.0, 0.28, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic in front of it is moving freely.\n\n",
+    "BDDX_Processed/video/1698c1ee-3c92ecc7_15274.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.13, 6.45, 5.83, 4.98, 3.97, 2.86, 2.26]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-1.69, -1.9, -2.1, -2.19, -2.11, -2.02, -1.55]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a near stop\n\nHuman: Why does the ego car doing this?\nAssistant: as there is a car stopped at an intersection in the lane ahead.\n\n",
+    "BDDX_Processed/video/14306a74-dbad8809_13843.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.69, 4.38, 4.76, 4.71, 4.81, 4.83, 4.62]\n Curvature: [0.0, -0.16, -0.17, 0.03, 0.13, 0.14, -0.01]\n Acceleration: [1.09, 0.72, 0.37, 0.08, -0.01, -0.08, -0.16]\n Course: [0.0, -0.14, -0.06, -0.05, 0.0, -0.14, -0.18]\nWhat is the action of ego car?\nAssistant: The car moves one lane left as it passes through an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the left lane is clear.\n\n",
+    "BDDX_Processed/video/017b02cb-477b66fa_03753.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.37, 0.37, 0.37, 0.36, 0.45, 1.14, 1.87]\n Curvature: [-0.04, -0.05, -0.06, -0.06, -0.07, -0.07, -0.07]\n Acceleration: [0.19, 0.09, -0.0, 0.38, 0.75, 1.21, 1.6]\n Course: [0.0, -0.05, -0.04, -0.05, -0.19, -0.33, 0.16]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: after there is a gap in traffic.\n\n",
+    "BDDX_Processed/video/149c3357-7fda9297_14016.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.3, 9.2, 9.1, 8.93, 8.84, 9.32, 9.78]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.17, -0.04, -0.24, 0.05, 0.34, 0.52, 0.64]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it wants to pass the cars in front of it.\n\n",
+    "BDDX_Processed/video/0d1fc580-61bbac5d_10370.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.12, 6.87, 7.53, 7.56, 7.6, 7.69, 7.67]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.44, 1.06, 0.71, 0.38, 0.09, -0.1, -0.42]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates quickly\n\nHuman: Why does the ego car doing this?\nAssistant: in order to pass the car on the right.\n\n",
+    "BDDX_Processed/video/0723c4dd-7a1168dc_07039.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.01, 0.18, 0.35, 0.48, 0.53]\n Curvature: [0.0, 0.0, 0.01, 0.1, 0.18, 0.2, 0.22]\n Acceleration: [0.0, 0.09, 0.17, 0.24, 0.27, 0.03, -0.15]\n Course: [0.0, -0.5, -0.77, -1.59, -1.27, -0.95, -0.27]\nWhat is the action of ego car?\nAssistant: The car backs up and begins moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is in the parking lot\n\n",
+    "BDDX_Processed/video/07919504-00f3c313_07200.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.03, 7.94, 7.86, 7.78, 7.73, 7.93, 8.12]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.87, -0.58, -0.32, -0.03, 0.26, 0.5, 0.72]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is braking and merging into left oncoming lane\n\nHuman: Why does the ego car doing this?\nAssistant: because there a car waiting to turn left and cyclist in the road\n\n",
+    "BDDX_Processed/video/002ab96a-ea678692_04320.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.14, 3.92, 4.68, 2.65, 0.41, 2.15, 2.89]\n Curvature: [0.09, -0.01, 0.0, 0.16, -0.45, 0.09, 0.24]\n Acceleration: [0.97, 0.64, -0.4, -1.35, -0.35, 1.28, -0.19]\n Course: [0.0, 0.0, -0.35, 0.18, -0.33, -12.3, -9.14]\nWhat is the action of ego car?\nAssistant: The car moves slowly in the garage\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is seen and the car can safely move onward.\n\n",
+    "BDDX_Processed/video/1527851b-1eef42c5_14300.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [21.98, 22.51, 22.99, 23.45, 23.8, 24.14, 24.42]\n Curvature: [-0.02, -0.02, -0.02, -0.02, -0.01, 0.0, 0.0]\n Acceleration: [1.47, 1.33, 1.2, 1.07, 0.94, 0.81, 0.79]\n Course: [0.0, 0.0, 0.0, 0.72, 0.72, 0.72, 0.0]\nWhat is the action of ego car?\nAssistant: The car veers left\n\nHuman: Why does the ego car doing this?\nAssistant: to merge onto the road.\n\n",
+    "BDDX_Processed/video/13980959-e2ec7280_13598.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.28, 0.51, 0.48, 0.2, 0.23, 0.62, 0.33]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.03, 0.04, -0.08, -0.08, 0.09, 0.15, -0.28]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red and pedestrians are crossing the street.\n\n",
+    "BDDX_Processed/video/0d9ca9fa-ee783c7c_10619.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.89, 4.88, 3.77, 2.96, 2.47, 2.01, 2.26]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.24, -2.14, -2.04, -1.72, -0.95, -0.24, 0.6]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's going over a speed bump.\n\n",
+    "BDDX_Processed/video/0e46d6a2-317f2c38_10918.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.26, 0.49, 0.71, 0.89, 1.08, 1.3, 1.6]\n Curvature: [0.2, 0.15, 0.09, -0.0, -0.01, -0.01, -0.04]\n Acceleration: [0.31, 0.32, 0.32, 0.3, 0.34, 0.43, 0.65]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic in front of it begins to move after the light turned green.\n\n",
+    "BDDX_Processed/video/11a61ba2-adf5fd04_12620.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.73, 8.71, 8.67, 8.67, 8.73, 8.81, 8.76]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.03, -0.04, -0.01, 0.04, 0.08, 0.02, -0.18]\n Course: [0.0, 0.0, 0.0, 0.0, -0.04, -0.09, 0.0]\nWhat is the action of ego car?\nAssistant: The car maintains speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/06fce5b8-efc1a0dd_06840.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.51, 5.08, 8.26, 10.73, 11.56, 11.46, 10.89]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, -0.0, 0.0]\n Acceleration: [1.48, 1.89, 2.07, 0.92, 0.35, -0.35, -0.43]\n Course: [0.0, 0.0, 0.01, 0.0, 0.0, 0.0, -0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no traffic nearby.\n\n",
+    "BDDX_Processed/video/01b4e4b9-fc7a5264_04882.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.7, 1.93, 2.04, 1.87, 1.67, 1.57, 1.52]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.46, 0.21, -0.03, -0.19, -0.16, -0.09, -0.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving even more slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because a car is merging into the lane from the right.\n\n",
+    "BDDX_Processed/video/1337f4dd-d686ded0_13314.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.55, 9.11, 10.01, 10.11, 10.47, 10.3, 9.91]\n Curvature: [-0.8, 0.34, 0.0, 0.0, 0.03, 0.02, -0.01]\n Acceleration: [1.63, 0.83, 0.32, 0.18, 0.05, -0.23, -0.5]\n Course: [0.0, 0.0, 0.0, -0.07, -0.12, -0.3, 0.64]\nWhat is the action of ego car?\nAssistant: The car drives straight down the road\n\nHuman: Why does the ego car doing this?\nAssistant: since there is nothing blocking it\n\n",
+    "BDDX_Processed/video/00378858-c5f802ac_04341.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.84, 11.24, 10.83, 11.04, 10.85, 10.54, 7.87]\n Curvature: [0.0, -0.0, -0.0, 0.01, 0.0, 0.0, 0.01]\n Acceleration: [-0.28, -0.2, -0.1, 0.03, -0.14, -0.41, -1.45]\n Course: [0.0, 0.12, -0.41, -0.2, -0.19, -0.41, -0.82]\nWhat is the action of ego car?\nAssistant: The car moves forward then merges left\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/154907da-415c25ec_14389.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.08, 5.89, 6.81, 7.75, 8.75, 9.69, 10.66]\n Curvature: [0.0, 0.03, 0.03, 0.0, -0.01, -0.02, -0.02]\n Acceleration: [0.87, 1.2, 1.42, 1.49, 1.48, 1.45, 1.38]\n Course: [0.0, 0.0, -0.59, -0.47, -0.23, 0.28, 0.35]\nWhat is the action of ego car?\nAssistant: The car changes to the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it's passing another car.\n\n",
+    "BDDX_Processed/video/043026cb-3a39d0b7_02451.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.41, 13.48, 13.74, 14.05, 14.41, 14.44, 14.68]\n Curvature: [-0.04, 0.02, -0.04, -0.01, 0.0, -0.0, -0.01]\n Acceleration: [1.03, 0.11, 0.25, 0.25, 0.17, 0.07, 0.3]\n Course: [0.0, 0.61, 0.25, 1.58, -0.04, 0.0, 0.28]\nWhat is the action of ego car?\nAssistant: The car maintains speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/097120a3-de3c12af_08176.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.49, 13.93, 14.37, 14.81, 15.24, 15.53, 15.81]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [1.18, 1.24, 1.3, 1.36, 1.4, 1.3, 1.19]\n Course: [0.0, 0.07, 0.07, 0.07, 0.05, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car merges into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it is free of traffic and there is a slower car ahead of it in its current lane.\n\n",
+    "BDDX_Processed/video/20bc7470-becc206e_20235.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.07, 7.28, 6.59, 5.8, 5.11, 5.1, 5.08]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.95, -2.56, -2.23, -1.84, -1.46, -1.07, -0.73]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because even though the light is green, traffic is backed up to the end of the intersection ahead.\n\n",
+    "BDDX_Processed/video/04904d8d-d7add19f_06071.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.34, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04]\n Acceleration: [-2.75, -0.65, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.02, 0.06, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light remains red.\n\n",
+    "BDDX_Processed/video/156dcd52-4e8cdda6_14517.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [16.13, 14.35, 12.12, 9.2, 5.51, 4.02, 1.66]\n Curvature: [-0.13, 0.04, 0.05, 0.13, 0.36, -0.35, -0.68]\n Acceleration: [-1.74, -2.47, -3.46, -3.91, -3.42, -2.71, -2.15]\n Course: [0.0, 0.19, 0.14, -0.07, -0.09, -0.17, 0.0]\nWhat is the action of ego car?\nAssistant: The car reduces its speed and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light ahead is red.\n\n",
+    "BDDX_Processed/video/0cfcbb80-015af547_10239.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.44, 7.98, 7.51, 6.8, 5.75, 4.7, 3.27]\n Curvature: [-0.04, -0.03, -0.03, -0.05, -0.09, -0.13, -0.07]\n Acceleration: [-0.41, -1.0, -1.6, -2.17, -2.7, -3.22, -2.95]\n Course: [0.0, 0.44, 0.44, 0.27, 0.27, 0.27, 0.14]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no other traffic on its road.\n\n",
+    "BDDX_Processed/video/13796ade-6b89dc0d_13480.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.87, 0.52, 0.2, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.6, -0.53, -0.47, -0.36, -0.2, -0.03, 0.06]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a sudden stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the taxi in front of it has stopped\n\n",
+    "BDDX_Processed/video/07184fbb-1bf770ba_01200.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.15, 0.31, 0.55, 0.74, 0.64, 0.73]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.14, 0.27, 0.37, 0.24, 0.16, 0.39, 0.65]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: to complete the left turn.\n\n",
+    "BDDX_Processed/video/16504799-1e495b19_15044.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.08, 2.59, 3.01, 3.2, 3.15, 2.94, 1.2]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.66, 0.59, 0.37, 0.1, -0.28, -1.33, -1.24]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0e05e1d7-51f68c07_10839.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.8, 1.68, 1.57, 1.64, 1.73, 2.03, 2.41]\n Curvature: [-0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.06, -0.06, -0.04, 0.17, 0.4, 0.73, 1.02]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving more freely.\n\n",
+    "BDDX_Processed/video/21ce51f8-eb98df70_20678.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.8, 3.63, 4.47, 5.31, 6.08, 6.52, 6.98]\n Curvature: [0.0, 0.07, 0.34, 0.42, 0.1, 0.01, 0.0]\n Acceleration: [1.07, 1.08, 1.08, 0.99, 0.79, 0.61, 0.85]\n Course: [0.0, 0.0, -9.96, -7.26, -3.63, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car makes a left slow\n\nHuman: Why does the ego car doing this?\nAssistant: because it's dark but good conditions.\n\n",
+    "BDDX_Processed/video/0b88820f-96208d1e_09431.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.32, 1.02, 2.06, 2.99, 3.69]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, 0.0, -0.0]\n Acceleration: [-0.08, 0.2, 0.67, 1.07, 1.25, 1.06, 0.82]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward and slightly to the right\n\nHuman: Why does the ego car doing this?\nAssistant: since the light has changed to green\n\n",
+    "BDDX_Processed/video/013aba02-3fbd03ff_04704.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [20.04, 19.36, 18.69, 17.91, 17.01, 16.11, 14.68]\n Curvature: [-0.01, -0.0, -0.0, -0.0, 0.0, 0.01, 0.0]\n Acceleration: [-0.88, -1.33, -1.78, -2.22, -2.65, -3.08, -2.89]\n Course: [0.0, 0.21, 0.21, -0.19, -0.19, -0.19, -0.02]\nWhat is the action of ego car?\nAssistant: The car brakes slightly\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic ahead slows down.\n\n",
+    "BDDX_Processed/video/03e20a2f-c96c4ace_01699.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: for an unknown reason.\n\n",
+    "BDDX_Processed/video/12e9c862-b4a17d87_13127.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.79, 2.21, 2.06, 1.41, 1.09, 0.95, 0.86]\n Curvature: [0.01, 0.14, -0.04, 0.04, 0.06, -0.02, -0.05]\n Acceleration: [-0.44, -0.38, -0.46, -0.5, -0.29, -0.12, -0.14]\n Course: [0.0, -0.57, -1.48, -1.78, -1.05, -0.08, 1.95]\nWhat is the action of ego car?\nAssistant: The car is veering left.\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is pulling over to the curb.\n\n",
+    "BDDX_Processed/video/1371f6a6-46053308_13445.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.17, 4.5, 3.74, 3.08, 2.39, 2.24, 2.07]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-2.78, -2.52, -2.22, -1.95, -1.65, -1.35, -1.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because there is another car blocking the road\n\n",
+    "BDDX_Processed/video/15626dcf-0fb1ebe9_14476.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.05, 4.76, 3.48, 2.19, 1.04, 0.76, 0.48]\n Curvature: [0.05, -0.01, -0.08, -0.15, -0.21, -0.21, -0.21]\n Acceleration: [-4.45, -4.08, -3.71, -3.35, -2.94, -2.26, -1.59]\n Course: [0.0, 0.09, 0.09, 0.09, 0.06, 0.02, 0.02]\nWhat is the action of ego car?\nAssistant: The car comes to a very fast stop\n\nHuman: Why does the ego car doing this?\nAssistant: since the car in front of it is no longer moving\n\n",
+    "BDDX_Processed/video/012fdff1-9d1d0d1d_04686.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.75, 2.65, 2.57, 2.47, 2.37, 2.29, 2.23]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.01, -0.0, -0.01]\n Acceleration: [-0.4, -0.38, -0.37, -0.35, -0.33, -0.3, -0.28]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car ahead has its brakes on.\n\n",
+    "BDDX_Processed/video/06c2ee7b-b7b17d24_03939.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.43, -0.04, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/06ff7d61-1fa52d1c_00472.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.29, 6.17, 7.87, 9.29, 9.75, 9.37, 8.1]\n Curvature: [-0.0, -0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.39, 1.77, 1.77, 1.17, 0.04, -1.0, -1.64]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions in the roadway ahead.\n\n",
+    "BDDX_Processed/video/05c1c47a-cb817b64_06396.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.02, 0.27, 0.57, 1.47, 2.39]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.13, 0.28, 0.72, 1.14, 1.35, 1.53]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned green.\n\n",
+    "BDDX_Processed/video/00f42f6b-699a498c_02477.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.3, 1.44, 1.58, 1.72, 1.86, 2.0, 2.14]\n Curvature: [0.04, 0.03, 0.03, 0.02, 0.02, 0.01, 0.01]\n Acceleration: [0.4, 0.49, 0.58, 0.67, 0.76, 0.85, 0.93]\n Course: [0.0, -0.02, -0.02, -0.02, -0.02, -0.02, -0.02]\nWhat is the action of ego car?\nAssistant: The car is moving forward\n\nHuman: Why does the ego car doing this?\nAssistant: The car has executed a rolling stop and is proceeding through the intersection.\n\n",
+    "BDDX_Processed/video/0672c229-4d6e08a2_06594.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.14, 0.26, 0.12, 0.03, 0.27, 0.57]\n Curvature: [1.1, 0.98, 0.81, -0.38, -1.33, -1.15, -0.91]\n Acceleration: [0.14, 0.06, 0.0, 0.06, 0.14, 0.48, 0.82]\n Course: [0.0, 0.0, 0.99, 3.95, 1.05, 0.14, 0.7]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: as pedestrians cross the road.\n\n",
+    "BDDX_Processed/video/0062f18d-f8cd3a65_01163.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.46, 6.69, 6.7, 6.49, 6.26, 6.0, 5.72]\n Curvature: [0.03, 0.0, -0.01, -0.01, -0.0, 0.01, 0.01]\n Acceleration: [0.23, 0.08, -0.1, -0.33, -0.38, -0.6, -1.08]\n Course: [0.0, -0.14, 0.05, 0.07, 0.05, -0.13, -0.11]\nWhat is the action of ego car?\nAssistant: The car maintains a constant speed\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane is clear.\n\n",
+    "BDDX_Processed/video/1c5ec9cb-ac0cc8ac_18046.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.75, 2.16, 1.51, 2.0, 2.51, 3.0, 3.46]\n Curvature: [0.09, 0.1, 0.14, 0.22, 0.26, 0.29, 0.31]\n Acceleration: [-2.52, -1.44, -0.38, 0.67, 0.76, 0.7, 0.58]\n Course: [0.0, 0.0, -5.63, -5.63, -4.88, -3.06, -3.06]\nWhat is the action of ego car?\nAssistant: The car slows and steers to the left\n\nHuman: Why does the ego car doing this?\nAssistant: to make a left hand turn onto another road.\n\n",
+    "BDDX_Processed/video/0066b72f-974f6883_04425.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.35, 6.53, 7.53, 8.62, 9.45, 10.22, 10.89]\n Curvature: [0.1, -0.04, 0.05, 0.04, 0.1, 0.09, 0.08]\n Acceleration: [1.41, 1.05, 1.02, 0.93, 0.77, 0.71, 0.32]\n Course: [0.0, -0.68, -0.52, -1.24, -2.63, -3.69, -2.15]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is green.\n\n",
+    "BDDX_Processed/video/222d61fa-ee7b4d29_20909.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.19, 4.33, 3.06, 2.08, 1.6, 0.4, 0.07]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.04, -1.37, -1.3, -1.1, -1.0, -0.9, -0.37]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a line of stopped cars ahead at a light.\n\n",
+    "BDDX_Processed/video/14df900d-bdfe5ec0_14104.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.55, 7.34, 6.22, 5.37, 4.51, 3.51, 2.45]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.96, -1.28, -1.1, -0.95, -1.03, -1.14, -1.17]\n Course: [0.0, 0.02, 0.0, -0.02, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows before coming to a stop.\n\nHuman: Why does the ego car doing this?\nAssistant: Because the traffic ahead is stopped at a red light.\n\n",
+    "BDDX_Processed/video/072ca47b-0cb52c7f_01768.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.33, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.03, -1.08, 8.36, 8.33, 8.3, 8.27, 8.24]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, -102.15, 0.68, 0.0, 0.0, 50.08]\nWhat is the action of ego car?\nAssistant: The car is parked in a plaza parking lot\n\nHuman: Why does the ego car doing this?\nAssistant: because the driver is stopping at the plaza.\n\n",
+    "BDDX_Processed/video/1fe4fb48-a9281047_19915.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because although the light is green and the car in front has moved, the driver has not started driving yet\n\n",
+    "BDDX_Processed/video/212f5e63-fe607059_20414.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.44]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.03, 0.3, 1.05]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is at rest\n\nHuman: Why does the ego car doing this?\nAssistant: because of a red light.\n\n",
+    "BDDX_Processed/video/0453391a-f15e9e30_05961.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [18.82, 16.87, 14.39, 6.43, -1.0, -1.0, -1.0]\n Curvature: [0.02, 0.0, -0.01, 0.0, 0.01, 0.01, 0.01]\n Acceleration: [-2.01, -5.96, -9.85, -8.87, -7.43, -3.45, 0.0]\n Course: [0.0, 0.11, -7.1, -21.62, -14.41, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car ahead is slowing down.\n\n",
+    "BDDX_Processed/video/072876e5-c10bfc30_01758.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.13, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped.\n\n",
+    "BDDX_Processed/video/1df69db3-88bd38e5_18872.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.87, 7.88, 6.86, 5.92, 5.3, 4.81, 4.41]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-1.4, -1.48, -1.42, -1.2, -0.91, -0.63, -0.34]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches traffic on the road ahead.\n\n",
+    "BDDX_Processed/video/06727591-79344a7e_06586.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.08, 12.53, 12.68, 12.7, 10.77, 8.51, 6.82]\n Curvature: [0.0, 0.01, 0.0, -0.01, -0.01, -0.08, -0.2]\n Acceleration: [0.52, 0.14, 0.15, -0.81, -1.67, -1.62, -0.41]\n Course: [0.0, 0.12, 0.0, -0.12, 0.02, 0.0, 9.96]\nWhat is the action of ego car?\nAssistant: The car moves forward then turns right\n\nHuman: Why does the ego car doing this?\nAssistant: because no traffic is blocking the way and the car can safely move forward.\n\n",
+    "BDDX_Processed/video/0db65573-d7355381_10649.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.21, 0.13, 0.05, 0.05, 0.16, 0.28, 0.56]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.12, -0.06, 0.0, 0.13, 0.33, 0.54, 0.65]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car gradually accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic starts moving.\n\n",
+    "BDDX_Processed/video/002ab96a-ea678692_00504.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.2, 8.99, 9.63, 10.6, 12.11, 13.11, 13.49]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [-0.88, -0.17, 0.53, 0.76, 0.81, 0.4, 0.19]\n Course: [0.0, 0.0, 0.0, -0.42, 0.0, 0.0, -0.0]\nWhat is the action of ego car?\nAssistant: The car maintains a steady speed and spacing with the vehicle ahead\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving smoothly.\n\n",
+    "BDDX_Processed/video/18c4d009-c765b465_16374.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.18, 4.49, 4.58, 4.66, 4.7, 5.07, 5.21]\n Curvature: [-0.01, 0.01, -0.02, 0.02, -0.01, -0.02, -0.04]\n Acceleration: [-0.8, -0.23, 0.09, 0.03, 0.05, 0.23, -0.43]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has slowed down.\n\n",
+    "BDDX_Processed/video/17fb67eb-ec0ce6f2_16011.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: for the red light.\n\n",
+    "BDDX_Processed/video/07363609-dd3a8645_07070.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.5, 1.78, 0.0, 0.06, 0.4, 0.15, 0.0]\n Curvature: [-0.0, -0.83, -1.6, -1.41, -0.43, 16.4, 16.54]\n Acceleration: [-2.84, -2.77, -0.01, 0.12, 0.09, -0.24, 0.0]\n Course: [0.0, -0.06, -0.21, 0.0, -0.42, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping quickly\n\nHuman: Why does the ego car doing this?\nAssistant: because the light has turned red.\n\n",
+    "BDDX_Processed/video/15eb3650-16b6a9ca_14783.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.85, 6.81, 5.76, 5.53, 5.35, 5.55, 5.84]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.06, 0.49, 0.78]\n Acceleration: [-1.94, -1.6, -1.22, -0.6, 0.03, 0.43, 0.84]\n Course: [0.0, 0.0, 0.0, 0.0, -2.97, -4.45, 29.96]\nWhat is the action of ego car?\nAssistant: The car is turning left\n\nHuman: Why does the ego car doing this?\nAssistant: as it gets onto another road\n\n",
+    "BDDX_Processed/video/028b5d16-45f7e11a_02032.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.57, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is at an intersection with a red light.\n\n",
+    "BDDX_Processed/video/13ded202-27de718f_13693.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.11, 5.97, 7.92, 9.48, 10.73, 10.9, 11.25]\n Curvature: [-0.07, 0.0, 0.0, 0.02, -0.04, 0.04, 0.0]\n Acceleration: [1.34, 1.67, 1.23, 1.0, 0.49, 0.05, 0.36]\n Course: [0.0, 0.0, 0.35, -0.28, -0.64, 0.2, -0.82]\nWhat is the action of ego car?\nAssistant: The car straightens out\n\nHuman: Why does the ego car doing this?\nAssistant: since it is in a clear lane now\n\n",
+    "BDDX_Processed/video/066112a8-cdf390dc_02787.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.06, -0.06, -0.06, -0.06, -0.06, -0.06, -0.06]\n Acceleration: [-0.41, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: due to traffic in front of it being stopped.\n\n",
+    "BDDX_Processed/video/036f07d5-92c52ed8_05545.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.25, 0.63, 1.15, 1.85, 2.32, 2.39]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.19, 0.47, 0.71, 0.92, 0.74, 0.46, 0.09]\n Course: [0.0, 0.16, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is pulling forward slowly\n\nHuman: Why does the ego car doing this?\nAssistant: to approach a stop sign.\n\n",
+    "BDDX_Processed/video/0a8368e0-abaca068_08832.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 3.03, 0.41, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.01, -0.08, -0.16, -0.16, -0.2, -0.14]\n Acceleration: [1.4, 0.15, -1.29, -0.03, 0.0, 0.0, 0.0]\n Course: [0.0, -32.09, 25.45, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: the camera started moving then the video went black.\n\nHuman: Why does the ego car doing this?\nAssistant: the camera started moving then the video went black.\n\n",
+    "BDDX_Processed/video/055e339a-1c6cb352_00669.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.84, 10.99, 11.14, 11.17, 11.03, 10.88, 10.27]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.39, 0.24, 0.1, -0.17, -0.6, -1.03, -1.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as its way is clear.\n\n",
+    "BDDX_Processed/video/013aba02-3fbd03ff_03120.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.25, 8.44, 8.64, 8.95, 9.45, 9.94, 10.17]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.29, 0.52, 0.75, 0.88, 0.88, 0.87, 0.63]\n Course: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car begins to speed up a little again.\n\nHuman: Why does the ego car doing this?\nAssistant: The car in front has started to speed up a little.\n\n",
+    "BDDX_Processed/video/1e9e59ca-cf8bb9ed_19176.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.36, 0.69, 1.34, 2.01, 2.93, 3.85]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.34, 0.66, 0.95, 1.22, 1.5, 1.86, 2.08]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car starts to accelerate and merges into a lane to the left\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turned green and then the lane it was in became obstructed by snow.\n\n",
+    "BDDX_Processed/video/0478d053-f5bb4831_06020.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.44, 3.6, 4.21, 4.16, 4.2, 4.13, 3.83]\n Curvature: [0.01, -0.01, -0.01, 0.01, 0.01, 0.01, 0.02]\n Acceleration: [1.89, 1.22, 0.61, 0.05, -0.11, -0.33, -0.72]\n Course: [0.0, 0.23, 0.11, 0.11, -0.17, -0.06, -0.06]\nWhat is the action of ego car?\nAssistant: The car accelerates slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic ahead is moving forward slowly.\n\n",
+    "BDDX_Processed/video/225dad3c-fd2a7987_21006.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.69, 9.15, 8.33, 7.18, 5.8, 4.34, 2.69]\n Curvature: [0.01, 0.01, -0.0, -0.03, -0.04, -0.3, -0.92]\n Acceleration: [-0.43, -1.0, -1.49, -1.89, -2.19, -2.35, -2.32]\n Course: [0.0, -0.11, 0.06, 0.06, 0.82, 1.7, 1.7]\nWhat is the action of ego car?\nAssistant: The car pulls over to the side of the road\n\nHuman: Why does the ego car doing this?\nAssistant: because there is space to do so, but it is blocking a driveway.\n\n",
+    "BDDX_Processed/video/0d22eba4-90037346_10413.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.06]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic in front of it is stopped.\n\n",
+    "BDDX_Processed/video/12dc1ad6-e8d7e789_13113.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.09, 18.87, 18.67, 18.58, 18.49, 18.35, 18.16]\n Curvature: [-0.02, -0.01, -0.01, -0.01, -0.01, -0.01, -0.0]\n Acceleration: [-0.5, -0.39, -0.29, -0.25, -0.24, -0.39, -0.53]\n Course: [0.0, 0.0, 0.01, 0.05, 0.06, 0.09, 0.09]\nWhat is the action of ego car?\nAssistant: The car turns into the far right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning.\n\n",
+    "BDDX_Processed/video/1f864b3f-e4948386_19729.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.28, 3.61, 3.19, 2.92, 2.71, 2.5, 2.3]\n Curvature: [-0.0, 0.0, -0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.87, -0.61, -0.38, -0.28, -0.23, -0.23, -0.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car almost comes to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: because it is approaching the vehicle in front of it.\n\n",
+    "BDDX_Processed/video/1816310a-99261060_16067.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.54, -0.39, -0.25, -0.11, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is stopped at a light further down the road.\n\n",
+    "BDDX_Processed/video/14b3e729-b1d0738c_14031.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.04, 0.08, 0.12, 0.15, 0.11, 0.07]\n Curvature: [0.07, 1.01, 1.83, 2.76, 3.58, 3.58, 3.59]\n Acceleration: [0.16, 0.12, 0.08, 0.04, 0.0, 0.0, 0.0]\n Course: [0.0, 1.73, 0.87, 1.73, 0.85, -0.02, -0.01]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: Because it is parked on the left side of the street.\n\n",
+    "BDDX_Processed/video/047c278b-452d36f8_06032.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.23, 3.04, 1.84]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.38, -0.08, 0.25]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.06, 0.0]\nWhat is the action of ego car?\nAssistant: The car sits in traffic behind several other cars\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is not moving.\n\n",
+    "BDDX_Processed/video/129d7734-cbd6b04d_13009.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.94, 0.3, 0.16, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.63, -0.32, -0.14, -0.06, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a red stoplight.\n\n",
+    "BDDX_Processed/video/16599465-82985baf_15080.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [15.73, 16.7, 17.56, 18.42, 19.16, 19.48, 20.05]\n Curvature: [0.0, -0.01, -0.01, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [0.72, 0.28, 0.38, 0.39, 0.23, 0.18, 0.26]\n Course: [0.0, 0.52, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down a road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear of traffic.\n\n",
+    "BDDX_Processed/video/0fc0c815-8ba80d60_11545.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.59, 0.85, 0.64, 0.37, 0.0, 0.0, 0.0]\n Curvature: [1.33, 0.07, 0.02, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.5, -0.35, -0.04, -0.31, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, -0.15, -0.01, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/0d413bc5-aa945f9e_10489.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.01, 0.1, 0.24]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is red and a pedestrian is crossing the road.\n\n",
+    "BDDX_Processed/video/168d16ae-66244a56_15253.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.58, 11.7, 12.81, 13.55, 13.85, 13.57, 12.99]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0]\n Acceleration: [1.53, 1.43, 1.13, 0.64, 0.01, -0.51, -1.15]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is clear.\n\n",
+    "BDDX_Processed/video/01b4e4b9-fc7a5264_00025.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.42, 4.5, 4.57, 4.61, 4.64, 4.62, 4.61]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.14, 0.12, 0.11, 0.06, 0.02, -0.01, -0.05]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car merges into the lane to its left\n\nHuman: Why does the ego car doing this?\nAssistant: to get around slow traffic in its current lane.\n\n",
+    "BDDX_Processed/video/073d4e2a-55c1f733_02081.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.78, 4.81, 2.67, 1.23, 0.66, 0.14, 0.0]\n Curvature: [-0.01, -0.01, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-4.14, -3.87, -3.57, -3.03, -1.96, -0.98, -0.49]\n Course: [0.0, 0.02, 0.04, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/1a5dc090-831816e0_17060.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.62, 9.36, 6.71, 3.95, 1.33, 1.18, 0.61]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.01]\n Acceleration: [-0.18, -1.41, -2.62, -2.65, -1.33, -0.34, -0.57]\n Course: [0.0, 0.0, 0.0, 0.0, -0.34, -0.26, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop as it approaches an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light ahead turns red.\n\n",
+    "BDDX_Processed/video/0c4586e7-b8ecba58_09781.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.27]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light ahead is red.\n\n",
+    "BDDX_Processed/video/02d478d1-9ce5120d_05345.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.77, 6.26, 8.56, 10.83, 11.23, 9.38, 5.9]\n Curvature: [0.49, 0.16, -0.0, 0.0, -0.0, 0.0, 0.0]\n Acceleration: [0.45, 1.28, 1.8, 1.0, -0.51, -2.0, -2.55]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/05cb8e1c-e064ca63_01502.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.6, 14.91, 15.18, 15.49, 15.79, 16.0, 16.19]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.83, 0.87, 0.9, 0.94, 0.97, 0.92, 0.87]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is merging into the right hand lane.\n\nHuman: Why does the ego car doing this?\nAssistant: The car is preparing to make a right hand turn at the upcoming intersection.\n\n",
+    "BDDX_Processed/video/1aa6dd36-b5ba6631_17223.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [17.77, 17.58, 17.43, 17.35, 16.99, 16.42, 15.47]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.3, -0.25, -0.26, -0.35, -0.76, -1.14, -1.46]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down a road\n\nHuman: Why does the ego car doing this?\nAssistant: because the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/16ca9fed-7d8bb556_15420.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.01, 7.28, 7.54, 7.63, 7.48, 7.32, 7.45]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.1, 0.73, 0.36, 0.13, 0.09, 0.06, 0.15]\n Course: [0.0, -0.01, -0.01, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car pulls into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because it's getting ready to turn.\n\n",
+    "BDDX_Processed/video/1481f91e-5647c157_13974.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.25, 6.36, 5.46, 4.54, 3.48, 2.32, 1.08]\n Curvature: [-0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.26, -1.35, -1.46, -1.58, -1.73, -1.74, -1.42]\n Course: [0.0, 0.0, 0.0, 0.0, -0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to stop\n\nHuman: Why does the ego car doing this?\nAssistant: as the light ahead is red.\n\n",
+    "BDDX_Processed/video/19167d13-f661c624_16578.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.43, 2.07, 1.72, 1.35, 1.02, 0.98, 1.12]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.51, -1.09, -0.7, -0.54, -0.32, 0.32, 0.94]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/087a33b7-ec85f56a_07689.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is parked on the left side of the street.\n\n",
+    "BDDX_Processed/video/1f44d8f2-8b61f64b_19446.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.58, 10.32, 10.99, 11.45, 11.57, 11.31, 10.31]\n Curvature: [0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.06, 0.94, 0.7, 0.33, -0.14, -0.8, -1.66]\n Course: [0.0, 0.0, 0.14, -0.07, -0.04, -0.02, 0.0]\nWhat is the action of ego car?\nAssistant: The driver pulls into the far left lane\n\nHuman: Why does the ego car doing this?\nAssistant: to make another turn.\n\n",
+    "BDDX_Processed/video/0183d6f9-806f64bd_03820.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.79, 2.58, 3.36, 3.97, 4.31, 4.66, 4.68]\n Curvature: [0.0, -0.0, -0.0, -0.01, -0.01, -0.02, -0.01]\n Acceleration: [1.81, 1.68, 1.54, 1.29, 0.88, 0.46, 0.21]\n Course: [0.0, 0.0, 0.0, 0.28, 0.28, 0.28, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has begun to move\n\n",
+    "BDDX_Processed/video/00f42f6b-699a498c_04614.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.23, 2.47, 1.74, 1.29, 0.89, 0.61, 0.34]\n Curvature: [0.0, -0.05, -0.1, -0.13, -0.15, -0.08, -0.01]\n Acceleration: [-1.43, -1.28, -1.12, -0.89, -0.69, -0.57, -0.44]\n Course: [0.0, -0.09, -0.05, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: for a red light.\n\n",
+    "BDDX_Processed/video/1a957f57-95b86b42_17141.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.17, 4.73, 2.02, 0.18, 0.0, 0.0, 0.0]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-3.54, -3.89, -3.31, -1.59, -0.6, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car reduces its speed and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the vehicle ahead has stopped and is blocking the road.\n\n",
+    "BDDX_Processed/video/1c1c7174-c95618d4_17938.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.48, 3.59, 3.71, 3.79, 3.91, 4.17, 4.57]\n Curvature: [-0.0, 0.0, -0.02, -0.26, -0.46, -0.31, -0.17]\n Acceleration: [0.1, 0.15, 0.2, 0.28, 0.4, 0.78, 1.13]\n Course: [0.0, 0.0, 1.33, 3.98, 5.92, 4.89, 1.63]\nWhat is the action of ego car?\nAssistant: The car makes a right turn\n\nHuman: Why does the ego car doing this?\nAssistant: since the intersection is clear.\n\n",
+    "BDDX_Processed/video/15ec1879-06e3118e_14795.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.06, 11.7, 13.59, 14.63, 14.68, 14.5, 14.53]\n Curvature: [0.0, 0.0, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.26, 0.96, 0.69, 0.14, -0.12, 0.1, -0.28]\n Course: [0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down the street\n\nHuman: Why does the ego car doing this?\nAssistant: as the road ahead is clear.\n\n",
+    "BDDX_Processed/video/1e76c0fd-97fce8a3_19106.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.38, 3.36, 3.85, 3.48, 2.8, 1.71, 0.57]\n Curvature: [-0.0, -0.01, 0.01, 0.0, 0.01, 0.0, 0.0]\n Acceleration: [-0.02, 0.26, 0.03, -0.53, -1.0, -1.21, -0.94]\n Course: [0.0, 0.08, 0.0, 0.0, 0.08, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slowly moves forward\n\nHuman: Why does the ego car doing this?\nAssistant: to close the gap between him and the car in front.\n\n",
+    "BDDX_Processed/video/203cb65f-358093b4_20016.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.35, 0.12, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-1.73, -0.73, -0.12, -0.01, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car comes to a halt\n\nHuman: Why does the ego car doing this?\nAssistant: because a pedestrian is in the crosswalk\n\n",
+    "BDDX_Processed/video/10b038d4-a0efb156_11921.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.18, 9.08, 10.15, 10.39, 10.11, 9.32, 7.82]\n Curvature: [-0.01, 0.03, -0.0, -0.0, 0.03, 0.01, 0.02]\n Acceleration: [1.01, 0.97, 0.64, -0.01, -0.51, -1.06, -1.75]\n Course: [0.0, -0.48, -1.19, -0.77, -0.29, -0.01, -0.23]\nWhat is the action of ego car?\nAssistant: The car goes down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is empty.\n\n",
+    "BDDX_Processed/video/13dcefd6-8b81ace7_13682.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.33, 11.29, 11.23, 10.32, 10.26, 11.29, 11.6]\n Curvature: [-0.0, -0.01, -0.02, 0.01, -0.01, -0.0, 0.01]\n Acceleration: [-0.14, 0.16, -0.26, -0.28, 0.19, 0.31, 0.14]\n Course: [0.0, 0.12, -0.21, -0.2, 0.01, 0.2, -2.32]\nWhat is the action of ego car?\nAssistant: The car is heading down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady pace.\n\n",
+    "BDDX_Processed/video/0521fd41-40219907_04238.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.12, 0.12, 0.12, 0.12, 0.12, 0.11, 0.11]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.08]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: due to stopped traffic in front of it.\n\n",
+    "BDDX_Processed/video/15c70e70-e3b6a249_14694.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.02, 10.06, 10.1, 10.14, 10.19, 10.34, 10.47]\n Curvature: [-0.01, -0.02, -0.02, -0.03, -0.04, -0.03, -0.02]\n Acceleration: [-0.14, -0.01, 0.1, 0.24, 0.36, 0.39, 0.42]\n Course: [0.0, 0.33, 0.16, 0.33, 0.16, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car continues in it's lane\n\nHuman: Why does the ego car doing this?\nAssistant: while going down the road\n\n",
+    "BDDX_Processed/video/1e2d42a4-d0373ec9_18968.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.66, 7.65, 8.57, 9.57, 10.54, 11.86, 13.13]\n Curvature: [0.02, 0.03, 0.03, -0.0, -0.02, -0.01, 0.0]\n Acceleration: [1.77, 1.82, 1.87, 2.03, 2.18, 2.28, 2.34]\n Course: [0.0, 0.66, 0.49, 0.0, -0.05, -0.09, 0.05]\nWhat is the action of ego car?\nAssistant: The car accelerates down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road ahead is clear.\n\n",
+    "BDDX_Processed/video/04488604-72f099d0_02706.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.12, 12.86, 14.29, 12.43, 14.23, 16.36, 17.18]\n Curvature: [0.0, -0.01, 0.0, 0.0, -0.0, -0.01, -0.0]\n Acceleration: [1.4, 0.96, -0.07, -0.17, 1.1, 0.67, 0.07]\n Course: [0.0, 1.01, 0.0, 0.0, 0.0, 0.53, 0.0]\nWhat is the action of ego car?\nAssistant: The car is going down the street at a steady speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is clear.\n\n",
+    "BDDX_Processed/video/1f477602-88a69ccc_19466.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.02, 0.32, 0.62, 0.8, 1.0]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.15, 0.31, 0.4, 0.47, 0.41, 0.4]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The vehicle is taking a right turn\n\nHuman: Why does the ego car doing this?\nAssistant: since the light turned green and it is now safe to turn.\n\n",
+    "BDDX_Processed/video/16a98dc3-14e12bce_15352.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.11, 5.99, 9.27, 12.16, 12.23, 11.15, 10.33]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.02, -0.02, -0.0]\n Acceleration: [2.16, 2.98, 2.98, 1.51, -0.51, -0.91, -0.87]\n Course: [0.0, 0.0, 0.0, 0.0, 0.62, 0.63, 0.08]\nWhat is the action of ego car?\nAssistant: The car driving down the middle of the street\n\nHuman: Why does the ego car doing this?\nAssistant: to pass a car in the right lane is stopped with its hazard lights on.\n\n",
+    "BDDX_Processed/video/09ac7416-74752083_08346.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because he is parked on the side of the road\n\n",
+    "BDDX_Processed/video/06d501fd-31889467_06738.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.29, 7.52, 7.93, 7.57, 6.75, 4.99, 2.92]\n Curvature: [0.0, 0.01, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [0.74, 0.61, -0.06, -0.38, -0.79, -1.31, -1.21]\n Course: [0.0, 0.0, -0.42, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car moves forward then comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/064bad4e-ae51d829_02498.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.67, 8.95, 8.4, 8.37, 7.94, 7.5, 6.7]\n Curvature: [0.0, 0.01, 0.0, 0.0, 0.0, -0.0, -0.01]\n Acceleration: [-0.52, -0.54, -0.2, -0.16, -0.37, -0.44, -0.71]\n Course: [0.0, -0.35, -0.23, -0.32, 0.07, 0.21, 0.34]\nWhat is the action of ego car?\nAssistant: The car is driving in the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: so that it can turn right.\n\n",
+    "BDDX_Processed/video/11589021-ab3116a2_12434.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.85, 19.75, 19.83, 19.92, 19.94, 19.85, 19.68]\n Curvature: [-0.0, 0.0, -0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [-0.09, -0.01, 0.08, 0.06, -0.03, -0.13, -0.12]\n Course: [0.0, -0.08, 0.04, 0.0, 0.0, -0.0, -0.0]\nWhat is the action of ego car?\nAssistant: The car continues to drive\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no cars ahead of it.\n\n",
+    "BDDX_Processed/video/1eb33c1c-792725a6_19215.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.63, 3.8, 6.1, 8.28, 9.96, 11.19, 11.81]\n Curvature: [-0.04, 0.03, 0.0, -0.02, 0.01, 0.02, 0.01]\n Acceleration: [0.3, 1.83, 2.42, 2.13, 1.6, 1.0, 0.35]\n Course: [0.0, 0.0, -0.08, -0.16, -0.08, 0.25, -0.08]\nWhat is the action of ego car?\nAssistant: The car accelerates forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/121550c4-22b647a7_12805.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.64, 12.14, 11.84, 11.53, 11.15, 10.76, 10.07]\n Curvature: [-0.0, 0.01, -0.01, -0.01, -0.0, 0.02, -0.01]\n Acceleration: [0.87, 0.34, -0.28, -0.18, -0.15, -0.22, -0.35]\n Course: [0.0, -0.07, -0.52, 0.19, 0.38, 0.0, 0.57]\nWhat is the action of ego car?\nAssistant: The car is driving forward in the rain\n\nHuman: Why does the ego car doing this?\nAssistant: because the path is clear.\n\n",
+    "BDDX_Processed/video/0e1feef0-c870dc17_10866.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.12, 10.16, 10.2, 10.25, 10.32, 10.38, 10.52]\n Curvature: [0.01, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.3, 0.23, 0.17, 0.16, 0.22, 0.28, 0.29]\n Course: [0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is merging into the lane to its left\n\nHuman: Why does the ego car doing this?\nAssistant: because its lane ends.\n\n",
+    "BDDX_Processed/video/17728848-ee05ea62_15774.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.65, 8.24, 7.86, 7.35, 6.65, 5.83, 4.9]\n Curvature: [-0.0, -0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.41, -0.48, -0.59, -0.78, -1.0, -1.13, -1.19]\n Course: [0.0, 0.0, 0.0, -0.07, -0.03, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car gradually reduces its speed\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead has come to a stop at a red light.\n\n",
+    "BDDX_Processed/video/12a6dd7e-cc056c30_13021.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.67, 3.53, 2.51, 1.83, 0.81, 0.25, 0.22]\n Curvature: [0.0, 0.0, -0.0, -0.0, -0.0, -0.02, -0.02]\n Acceleration: [-0.5, -1.05, -0.82, -0.83, -0.77, -0.29, 0.03]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: as pedestrians are crossing the street.\n\n",
+    "BDDX_Processed/video/0ee642f9-75dd5c38_11254.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.12, 5.11, 5.48, 6.22, 7.28, 8.06, 8.69]\n Curvature: [-0.02, -0.08, -0.12, -0.14, -0.15, -0.12, -0.08]\n Acceleration: [-0.07, 0.24, 0.71, 1.08, 1.16, 0.92, 0.82]\n Course: [0.0, 2.2, 3.34, 4.12, 3.48, 1.68, 3.47]\nWhat is the action of ego car?\nAssistant: The car continues accelerating slowly\n\nHuman: Why does the ego car doing this?\nAssistant: As it drives along a curved road and traffic in front of it is slowly accelerating.\n\n",
+    "BDDX_Processed/video/07184fbb-0a78715a_01185.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.85, 5.86, 6.76, 7.43, 7.84, 8.01, 7.77]\n Curvature: [-0.04, 0.04, 0.05, 0.0, -0.01, -0.01, -0.03]\n Acceleration: [1.12, 1.26, 1.19, 0.87, 0.41, -0.08, -0.67]\n Course: [0.0, -0.45, 0.0, 0.0, 0.15, 0.25, 0.32]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/040dfa50-4082932d_02268.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.01, 11.79, 9.74, 8.98, 8.21, 6.97, 5.93]\n Curvature: [-0.01, -0.0, -0.0, -0.04, -0.05, 0.04, 0.1]\n Acceleration: [-3.47, -3.12, -2.77, -2.31, -1.9, -1.59, -1.21]\n Course: [0.0, -0.05, -0.0, 0.05, 0.01, 0.0, -0.03]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic ahead has slowed.\n\n",
+    "BDDX_Processed/video/18794cdb-11294703_16207.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.02, 5.15, 4.26, 3.45, 2.88, 2.2, 1.39]\n Curvature: [0.0, -0.0, -0.02, -0.03, 0.02, 0.05, 0.01]\n Acceleration: [-0.9, -1.17, -1.25, -1.09, -1.07, -1.12, -1.23]\n Course: [0.0, 0.09, 0.06, 0.06, 0.05, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: since traffic ahead has stopped.\n\n",
+    "BDDX_Processed/video/0710389f-9edbc057_00868.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.7, 3.45, 3.22, 2.9, 2.51, 2.11, 1.65]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0]\n Acceleration: [-0.52, -0.64, -0.74, -0.87, -1.01, -1.14, -0.98]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/1537b75f-db2958cd_14356.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.01, 2.45, 2.81, 3.05, 2.96, 3.05, 3.5]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.69, 0.59, 0.4, 0.12, 0.19, 0.48, 1.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slowly accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's pulling around a large parked truck.\n\n",
+    "BDDX_Processed/video/025b2ded-de608724_01614.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.75, 6.67, 5.53, 3.65, 1.93, 1.41, 0.89]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.01, -0.01, -0.01]\n Acceleration: [-0.89, -1.92, -2.86, -2.58, -2.25, -1.56, -0.95]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop.\n\nHuman: Why does the ego car doing this?\nAssistant: The traffic ahead of the car has come to a stop\n\n",
+    "BDDX_Processed/video/094647f9-8af4abe6_08134.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [24.84, 26.26, 26.95, 26.68, 24.91, 23.06, 21.09]\n Curvature: [0.02, 0.01, 0.01, 0.01, -0.0, -0.01, -0.01]\n Acceleration: [1.95, 1.29, 0.13, -1.49, -2.42, -2.89, -2.77]\n Course: [0.0, -0.33, 0.06, 0.05, 0.0, 0.14, 0.18]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: because there are no other cars in its lane.\n\n",
+    "BDDX_Processed/video/07d025f2-86b087d6_07325.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [19.05, 19.14, 18.99, 18.95, 18.64, 18.47, 18.2]\n Curvature: [0.0, -0.0, 0.0, 0.0, 0.0, -0.0, 0.0]\n Acceleration: [0.2, -0.14, 0.0, -0.1, -0.15, -0.12, -0.2]\n Course: [0.0, 0.0, 0.0, 0.0, -0.01, -11.98, 0.47]\nWhat is the action of ego car?\nAssistant: The car is driving at normal speed\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no one on the road.\n\n",
+    "BDDX_Processed/video/02d478d1-42ca3cfb_05314.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.61, 1.18, 1.45, 1.51, 3.6, 5.71, 7.16]\n Curvature: [-0.25, -0.2, -0.06, 0.51, 0.38, 0.09, 0.02]\n Acceleration: [0.42, 0.44, 0.19, 1.31, 2.18, 2.03, 1.54]\n Course: [0.0, -0.79, -2.3, -3.81, -4.7, -4.13, -2.14]\nWhat is the action of ego car?\nAssistant: The car accelerates and turns left\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/18fc7b57-4be80544_16488.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.81, 8.59, 8.37, 8.15, 7.94, 7.57, 7.2]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.72, -0.83, -0.94, -1.05, -1.13, -1.15, -1.18]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The pulls into the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: to pass a stow taxi.\n\n",
+    "BDDX_Processed/video/1c5c6a4f-65279a68_18038.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.64, 5.37, 5.12, 4.85, 4.57, 4.29, 4.04]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.01, -0.28, -0.52, -0.8, -1.04, -0.96, -0.9]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is turning.\n\n",
+    "BDDX_Processed/video/1c71863d-a34af0f4_18103.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.66, 1.55, 2.36, 2.37, 2.38, 2.7, 3.12]\n Curvature: [-0.15, -0.02, -0.07, -0.45, 0.26, 0.76, 0.18]\n Acceleration: [0.82, 0.92, 0.45, 0.06, 0.18, 0.4, 0.75]\n Course: [0.0, 0.08, 0.24, 1.11, -1.4, 9.18, 11.17]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because it's turning to the right.\n\n",
+    "BDDX_Processed/video/14258ad8-4d6de06d_13806.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.62, 10.61, 7.19, 5.05, 4.39, 4.01, 4.87]\n Curvature: [-0.01, -0.05, -0.09, -0.15, -0.21, -0.14, -0.11]\n Acceleration: [-6.13, -4.98, -3.47, -1.92, -0.58, 0.32, 1.02]\n Course: [0.0, 0.54, 2.52, 4.9, 4.25, 3.61, 5.44]\nWhat is the action of ego car?\nAssistant: The car slows and exits right off the main highway\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear.\n\n",
+    "BDDX_Processed/video/1a7189e1-cfbee7c1_17099.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.77, 11.62, 13.2, 12.99, 15.07, 16.63, 17.02]\n Curvature: [0.01, 0.02, 0.0, 0.01, -0.01, 0.0, 0.0]\n Acceleration: [-0.61, 0.89, 0.18, 0.22, 1.18, 0.46, -0.18]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates slightly and continues down the road\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead is moving normally.\n\n",
+    "BDDX_Processed/video/00c12bd0-bb46e479_04557.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.35, 1.45, 1.54, 1.42, 1.01, 0.57, 0.55]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.25, -0.02, -0.27, -0.43, -0.45, -0.47, -0.21]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is turning left and stopping\n\nHuman: Why does the ego car doing this?\nAssistant: so that it can turn around.\n\n",
+    "BDDX_Processed/video/1d288c57-918d5ae6_18404.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [29.83, 30.56, 31.26, 31.72, 32.24, 32.19, 32.01]\n Curvature: [-0.02, 0.0, -0.0, -0.01, 0.0, -0.0, -0.01]\n Acceleration: [0.15, 0.2, 0.14, 0.11, -0.01, -0.01, -0.05]\n Course: [0.0, -0.05, 0.0, 0.0, -0.05, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car proceeds down the highway\n\nHuman: Why does the ego car doing this?\nAssistant: since the lane ahead is clear of traffic.\n\n",
+    "BDDX_Processed/video/06cb522f-f1ff4334_04037.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.29, 3.24, 3.19, 3.14, 3.09, 2.67, 2.24]\n Curvature: [0.06, 0.04, 0.03, 0.01, -0.01, 0.01, 0.03]\n Acceleration: [0.19, -0.09, -0.34, -0.62, -0.87, -0.89, -0.92]\n Course: [0.0, -0.07, -0.07, -0.07, -0.04, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops\n\nHuman: Why does the ego car doing this?\nAssistant: when the car in front of it stops.\n\n",
+    "BDDX_Processed/video/1698c1ee-7a532c1a_15286.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.59, 0.95, 0.37, 0.15, 0.47, 0.82, 1.13]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.73, -0.58, -0.44, -0.16, 0.27, 0.74, 0.56]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is backing up\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is about to turn around\n\n",
+    "BDDX_Processed/video/1b883059-913ecef4_17705.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.52, 12.57, 12.64, 12.88, 13.17, 13.69, 14.14]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.07, 0.12, 0.3, 0.54, 0.74, 0.71, 0.63]\n Course: [0.0, -0.04, -0.03, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is moving down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is clear\n\n",
+    "BDDX_Processed/video/13c574d4-1df92435_13662.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.17, 0.0, 0.0, 0.13, 0.0, 0.0, 0.11]\n Curvature: [0.37, 4.09, 10.72, 8.44, -0.33, -3.76, -3.91]\n Acceleration: [0.0, -0.04, 0.01, 0.03, -0.08, 0.06, -0.02]\n Course: [0.0, 0.0, 0.0, 0.0, -0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is not moving\n\nHuman: Why does the ego car doing this?\nAssistant: as traffic ahead is stopped.\n\n",
+    "BDDX_Processed/video/17adc5ac-8bd58412_15823.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.95, 11.3, 10.62, 9.02, 10.29, 11.96, 12.83]\n Curvature: [-0.0, -0.0, 0.01, 0.09, 0.0, -0.02, 0.0]\n Acceleration: [0.13, 0.46, -0.58, -0.81, 0.95, 0.5, 0.26]\n Course: [0.0, 0.71, -0.27, -7.12, -0.24, -0.28, -0.09]\nWhat is the action of ego car?\nAssistant: The car heads down the street at a steady pace\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed\n\n",
+    "BDDX_Processed/video/14098efb-538670fc_13747.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.62, 9.45, 7.88, 6.25, 5.1, 5.04, 6.42]\n Curvature: [0.0, 0.06, 0.07, 0.03, -0.01, 0.12, 0.09]\n Acceleration: [-1.52, -1.81, -1.95, -1.63, -0.7, 0.8, 1.3]\n Course: [0.0, -1.69, -1.23, -1.27, 34.59, 70.45, -1.76]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it's making a left turn.\n\n",
+    "BDDX_Processed/video/03370efc-c690f4d8_00095.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.94, 8.54, 9.23, 10.07, 11.4, 12.2, 12.61]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, 0.0, -0.0]\n Acceleration: [0.54, 0.62, 0.73, 1.06, 1.04, 0.58, 0.36]\n Course: [0.0, 0.0, 0.02, 0.06, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the light is green.\n\n",
+    "BDDX_Processed/video/00c12bd0-bb46e479_02141.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.75, 3.18, 4.56, 5.6, 6.83, 8.19, 10.36]\n Curvature: [0.0, 0.19, -0.14, -0.11, 0.11, -0.0, 0.0]\n Acceleration: [1.22, 0.83, 0.7, 0.81, 0.57, 1.2, 0.28]\n Course: [0.0, 72.28, -0.13, 2.02, -0.17, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear of traffic.\n\n",
+    "BDDX_Processed/video/0aab75aa-69cf8c75_08892.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.15, 0.19, 0.22, 0.2, 0.0, 0.0, 0.0]\n Curvature: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [-0.11, 0.02, 0.01, -0.1, -0.08, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is blocked by traffic.\n\n",
+    "BDDX_Processed/video/1c570d0b-df0b2a5d_18035.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.84, 2.09, 3.04, 4.51, 6.25, 6.6, 6.9]\n Curvature: [0.1, -0.05, 0.0, -0.01, 0.0, 0.02, -0.02]\n Acceleration: [0.31, 0.19, 0.76, 0.9, 0.69, -0.02, 0.07]\n Course: [0.0, 0.0, 0.16, 0.16, 0.15, -0.02, -0.32]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving slowly forward.\n\n",
+    "BDDX_Processed/video/09fe84f2-5f2228d2_08629.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.09, 5.5, 5.88, 6.3, 6.71, 7.15, 7.6]\n Curvature: [0.0, 0.01, 0.02, 0.02, 0.0, -0.01, -0.02]\n Acceleration: [1.03, 1.04, 1.06, 1.07, 1.1, 1.12, 1.06]\n Course: [0.0, -0.14, -0.14, -0.11, -0.07, -0.11, 0.11]\nWhat is the action of ego car?\nAssistant: The car is merging into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no traffic in that lane.\n\n",
+    "BDDX_Processed/video/1e6a6965-44d99d0a_19071.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.32, -0.19, -0.08, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car stops once more\n\nHuman: Why does the ego car doing this?\nAssistant: because it needs to stop.\n\n",
+    "BDDX_Processed/video/02191150-4615d36f_00808.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04]\n Course: [0.0, -0.01, -0.06, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car remains still.\n\nHuman: Why does the ego car doing this?\nAssistant: The light still has not changed.\n\n",
+    "BDDX_Processed/video/1aa41265-a89ad4f3_17163.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.64, 7.87, 8.34, 8.69, 8.7, 8.7, 8.53]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.01, -0.0, 0.01]\n Acceleration: [0.3, 0.47, 0.45, 0.25, -0.01, -0.12, -0.34]\n Course: [0.0, -0.54, -0.04, -0.0, -0.09, -0.19, 0.03]\nWhat is the action of ego car?\nAssistant: The car heads down the street\n\nHuman: Why does the ego car doing this?\nAssistant: because the street is clear.\n\n",
+    "BDDX_Processed/video/1183ef80-67676319_12578.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.78, 6.44, 5.29, 4.58, 4.38, 4.23, 3.83]\n Curvature: [0.0, -0.0, 0.0, 0.0, -0.0, -0.0, 0.0]\n Acceleration: [-1.29, -1.5, -1.16, -0.63, -0.24, -0.36, -0.36]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: to pull around some cars parked by the street.\n\n",
+    "BDDX_Processed/video/0bb277a8-f68273d2_09532.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.47, 8.54, 8.6, 8.61, 8.6, 8.5, 8.43]\n Curvature: [-0.01, -0.01, -0.01, -0.01, 0.0, 0.01, 0.01]\n Acceleration: [-1.08, -0.49, 0.06, -0.02, -0.09, -0.05, 0.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car starts to slow\n\nHuman: Why does the ego car doing this?\nAssistant: as it approaches stopped traffic at a light\n\n",
+    "BDDX_Processed/video/0b0b93b3-5bb0e887_09170.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.68, 4.58, 5.1, 5.15, 3.99, 2.62, 1.22]\n Curvature: [0.05, 0.07, 0.09, 0.11, 0.01, 0.12, 0.77]\n Acceleration: [0.7, 0.72, 0.23, -0.91, -1.62, -1.99, -1.6]\n Course: [0.0, -1.76, -1.02, -1.02, -0.86, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: as it is turning to the left.\n\n",
+    "BDDX_Processed/video/1d092676-d1d3ad6b_18329.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.5, 3.95, 4.35, 4.31, 3.48, 2.21, 1.11]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.15, 0.41, 0.19, -0.48, -1.1, -1.29, -1.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The pulls into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: in order to turn left.\n\n",
+    "BDDX_Processed/video/22b4f5e6-0e50b841_21128.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [11.31, 11.2, 10.69, 10.47, 11.2, 11.8, 12.18]\n Curvature: [0.0, -0.0, 0.02, 0.01, 0.02, -0.01, -0.01]\n Acceleration: [-0.1, -0.17, -0.42, 0.26, 0.44, 0.4, 0.13]\n Course: [0.0, 0.39, 0.12, -0.16, -0.26, -0.46, 0.0]\nWhat is the action of ego car?\nAssistant: The car drives slowly down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because it is dark and rainy.\n\n",
+    "BDDX_Processed/video/09ae119b-ba122e1a_08440.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [14.93, 13.98, 13.15, 12.19, 11.31, 10.94, 10.62]\n Curvature: [0.01, 0.01, 0.01, 0.01, 0.01, 0.0, -0.0]\n Acceleration: [-2.59, -2.56, -2.53, -2.5, -2.42, -1.97, -1.57]\n Course: [0.0, -0.07, -0.04, -0.07, -0.04, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is braking\n\nHuman: Why does the ego car doing this?\nAssistant: since it was cut off by a truck merging right from the left lane\n\n",
+    "BDDX_Processed/video/161c6340-b4595890_14938.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.5, 1.79, 4.77, 6.45, 6.35, 6.67, 8.23]\n Curvature: [0.11, 0.02, -0.03, -0.0, -0.0, 0.01, 0.0]\n Acceleration: [0.16, 1.42, 1.76, 0.42, -0.09, 0.7, 0.64]\n Course: [0.0, 0.0, 0.04, 0.0, 0.0, 0.0, -0.27]\nWhat is the action of ego car?\nAssistant: The car slowly moves forward\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front is moving forward.\n\n",
+    "BDDX_Processed/video/14e111e7-722385a8_14121.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.46, 10.51, 10.49, 10.05, 9.6, 9.37, 7.95]\n Curvature: [0.0, -0.0, -0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.04, 0.02, -0.17, -0.42, -0.28, -0.7, -1.65]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car keeps rolling but slows\n\nHuman: Why does the ego car doing this?\nAssistant: because there is an intersection.\n\n",
+    "BDDX_Processed/video/0bfc0ff9-1fdb0036_09657.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [26.08, 26.17, 27.24, 27.9, 29.04, 29.26, 29.34]\n Curvature: [0.0, 0.02, -0.01, -0.0, 0.0, -0.01, -0.0]\n Acceleration: [0.09, 0.41, 0.29, 0.57, 0.35, -0.0, 0.12]\n Course: [0.0, -0.7, -0.49, -0.34, 0.0, 0.18, 0.0]\nWhat is the action of ego car?\nAssistant: The car is driving down a highway\n\nHuman: Why does the ego car doing this?\nAssistant: as the road ahead is clear.\n\n",
+    "BDDX_Processed/video/198d8f5e-2b4d439d_16752.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.82, 2.19, 3.3, 4.88, 6.17, 7.06, 7.65]\n Curvature: [7.33, 0.72, 0.01, 0.0, -0.01, -0.01, -0.02]\n Acceleration: [1.17, 1.34, 1.51, 1.55, 1.21, 0.82, 0.47]\n Course: [0.0, -0.48, 0.35, -0.24, -0.48, 0.66, 0.61]\nWhat is the action of ego car?\nAssistant: The car accelerates through the intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turns green.\n\n",
+    "BDDX_Processed/video/20833cc9-28773ed8_20132.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.71, 9.81, 10.51, 11.81, 12.65, 13.66, 14.06]\n Curvature: [0.01, 0.0, 0.01, 0.01, -0.0, -0.0, -0.0]\n Acceleration: [0.39, 0.18, 0.57, 0.5, 0.48, 0.5, -0.27]\n Course: [0.0, 0.66, 0.34, -0.35, -0.33, 0.51, -0.18]\nWhat is the action of ego car?\nAssistant: The car speeds up a little\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front has moved over.\n\n",
+    "BDDX_Processed/video/2238edb7-0ece1727_20919.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [6.53, 4.98, 3.56, 2.56, 1.66, 0.93, 0.31]\n Curvature: [-0.01, -0.01, -0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.64, -2.51, -2.37, -1.97, -1.59, -1.2, -0.8]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car quickly slows\n\nHuman: Why does the ego car doing this?\nAssistant: because the light at the intersection is red.\n\n",
+    "BDDX_Processed/video/0c6c9c66-5e86e200_09879.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.65, 1.82, 1.93, 2.67, 1.29, 0.22, 0.01]\n Curvature: [-0.0, 0.03, 1.17, 1.83, -0.58, -2.18, -2.61]\n Acceleration: [-0.68, -0.33, 0.36, -0.15, -1.29, -0.39, -0.11]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped in the right lane\n\nHuman: Why does the ego car doing this?\nAssistant: because the cars in front of it have stopped.\n\n",
+    "BDDX_Processed/video/1e1d4587-f9a27aea_18929.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.21, 4.69, 3.97, 2.62, 1.4, 0.63, 0.62]\n Curvature: [-0.0, -0.0, 0.0, -0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.75, -0.38, -0.78, -0.96, -0.71, -0.24, -0.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopping\n\nHuman: Why does the ego car doing this?\nAssistant: because it is approaching a red light.\n\n",
+    "BDDX_Processed/video/1d315fc6-365968a8_18429.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 1.04, 4.06, 4.67, 5.19, 5.4, 5.2]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.25, 1.36, 1.48, 0.21, 0.33, -0.08, -0.17]\n Course: [0.0, 0.0, 0.0, 0.0, -0.01, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowly moving\n\nHuman: Why does the ego car doing this?\nAssistant: while following another car down the road\n\n",
+    "BDDX_Processed/video/1bb2df55-52b38d56_17766.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.69, 12.69, 11.16, 9.05, 6.8, 5.39, 2.47]\n Curvature: [-0.03, 0.02, 0.01, 0.01, 0.03, -0.24, 0.11]\n Acceleration: [-0.59, -1.52, -2.28, -2.62, -2.53, -2.96, -2.44]\n Course: [0.0, -0.21, 0.0, 0.07, 0.04, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car brakes and comes to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the traffic light ahead turns red.\n\n",
+    "BDDX_Processed/video/2021c742-87a39f03_19969.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [-0.02, -0.02, -0.01, -0.01, -0.01, -0.01, -0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.16, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car remains stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is still red.\n\n",
+    "BDDX_Processed/video/14098efb-f2a5c999_13778.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped at a curb near an intersection\n\nHuman: Why does the ego car doing this?\nAssistant: because the car is pulled over.\n\n",
+    "BDDX_Processed/video/0fa741ce-689ea4b8_11489.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.8, 3.02, 3.43, 4.18, 4.37, 5.61, 7.08]\n Curvature: [0.0, -0.0, 0.0, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.21, 0.78, 0.57, 0.44, 0.68, 1.34, 0.68]\n Course: [0.0, 0.07, -0.07, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car maintains a slow speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving slowly.\n\n",
+    "BDDX_Processed/video/1127aff5-4ca99a83_12295.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.56, 5.37, 5.18, 4.99, 4.81, 4.52, 4.23]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.59, -0.67, -0.76, -0.85, -0.93, -0.98, -1.04]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/131d44d3-ede5a792_13271.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.03, 9.37, 8.71, 8.05, 7.36, 6.47, 5.58]\n Curvature: [-0.01, -0.01, -0.01, -0.0, 0.0, 0.0, 0.0]\n Acceleration: [-2.4, -2.55, -2.71, -2.87, -3.03, -3.19, -3.35]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stop sign.\n\n",
+    "BDDX_Processed/video/1f2adee2-d0541020_19402.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.7, 2.06, 2.28, 1.87, 1.71, 1.94, 2.24]\n Curvature: [0.0, 0.0, -0.0, 0.0, 0.04, 0.64, 0.69]\n Acceleration: [0.4, 0.32, -0.11, -0.27, 0.02, 0.28, 0.44]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -8.5, -10.95]\nWhat is the action of ego car?\nAssistant: The car makes a left turn slowly\n\nHuman: Why does the ego car doing this?\nAssistant: because it is dark and the narrow road is lined with parked cars on both sides.\n\n",
+    "BDDX_Processed/video/07a192de-e995e685_07243.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.23, 0.08, 0.0, 0.0, 0.0, 0.0, 0.0]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.9, -0.4, -0.08, -0.01, 0.0, 0.04, 0.16]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: because the light is red.\n\n",
+    "BDDX_Processed/video/176682b0-ec1a1b4b_15751.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.81, 5.59, 5.39, 5.18, 5.0, 4.81, 4.48]\n Curvature: [0.01, -0.02, -0.04, -0.04, -0.02, 0.0, -0.0]\n Acceleration: [-0.3, -0.39, -0.46, -0.54, -0.61, -0.68, -0.83]\n Course: [0.0, 0.04, 0.04, 0.28, 0.19, 0.28, 0.46]\nWhat is the action of ego car?\nAssistant: The car moves into the left lane\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane is clear.\n\n",
+    "BDDX_Processed/video/04629c39-4a119705_03100.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.49, 1.96, 2.42, 2.67, 2.84, 2.53, 2.22]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [0.9, 0.79, 0.65, 0.27, -0.09, -0.42, -0.61]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: The car is approaching an intersection with a stop sign.\n\n",
+    "BDDX_Processed/video/02f101f6-803bdb72_03413.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [9.26, 8.28, 7.41, 6.43, 5.57, 5.22, 4.87]\n Curvature: [0.27, 0.13, 0.01, -0.13, -0.25, -1.12, -1.99]\n Acceleration: [-1.85, -2.02, -2.16, -2.33, -2.48, -2.25, -2.03]\n Course: [0.0, -0.19, -0.19, -0.19, -0.09, -0.12, -0.12]\nWhat is the action of ego car?\nAssistant: The car is stopped\n\nHuman: Why does the ego car doing this?\nAssistant: The vehicle ahead of the car is stopped.\n\n",
+    "BDDX_Processed/video/088d61b7-a4ec6aaf_07742.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.89, 3.89, 3.88, 3.95, 4.12, 4.28, 4.53]\n Curvature: [-0.0, -0.06, -0.12, -0.23, -0.39, -0.56, -0.4]\n Acceleration: [-0.01, 0.07, 0.16, 0.27, 0.41, 0.55, 0.69]\n Course: [0.0, 3.37, 2.25, 5.49, 5.49, 5.49, -0.2]\nWhat is the action of ego car?\nAssistant: The car is turning right\n\nHuman: Why does the ego car doing this?\nAssistant: as the road is clear.\n\n",
+    "BDDX_Processed/video/04076a65-55eb2157_02028.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.68, 1.01, 1.23, 1.76, 2.04, 2.08, 2.14]\n Curvature: [-0.22, -0.34, -0.22, -0.18, -0.17, -0.11, -0.06]\n Acceleration: [0.32, 0.3, 0.42, 0.43, 0.2, 0.07, 0.2]\n Course: [0.0, 1.56, 2.3, 3.49, 3.95, 4.37, 3.02]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: to turn to the right.\n\n",
+    "BDDX_Processed/video/208caf98-7316a867_20154.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.2, 5.29, 8.12, 9.07, 9.85, 11.46, 14.42]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [1.43, 1.58, 1.28, 0.42, 0.39, 1.65, 1.07]\n Course: [0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.07]\nWhat is the action of ego car?\nAssistant: The car is speeding up\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has cleared and it is able to move now.\n\n",
+    "BDDX_Processed/video/206be0fc-480f4b0f_20098.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.13, 11.24, 10.41, 9.63, 8.9, 8.3, 7.76]\n Curvature: [-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-1.59, -1.6, -1.59, -1.45, -1.31, -1.02, -0.71]\n Course: [0.0, 0.0, 0.0, 0.0, -0.01, -0.06, -0.22]\nWhat is the action of ego car?\nAssistant: The car slows to make a wide left turn\n\nHuman: Why does the ego car doing this?\nAssistant: because there is no traffic and it's able to make the turn.\n\n",
+    "BDDX_Processed/video/021c4a16-f3a34ff5_04999.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.29, 7.02, 5.75, 4.98, 4.74, 4.48, 4.47]\n Curvature: [-0.0, 0.01, 0.02, 0.02, 0.02, 0.02, 0.01]\n Acceleration: [-2.11, -2.04, -1.96, -1.61, -1.03, -0.4, -0.14]\n Course: [0.0, 0.21, 0.21, -0.03, -0.02, -0.03, -0.1]\nWhat is the action of ego car?\nAssistant: The car is slowing down\n\nHuman: Why does the ego car doing this?\nAssistant: Because it is approaching a bump in the road.\n\n",
+    "BDDX_Processed/video/144bd77c-0a461478_13869.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.71, 12.8, 13.03, 13.18, 13.07, 12.65, 12.81]\n Curvature: [0.0, 0.02, 0.02, 0.01, -0.0, -0.0, 0.0]\n Acceleration: [0.24, 0.25, 0.2, 0.0, -0.29, -0.12, -0.19]\n Course: [0.0, 0.0, -0.94, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is heading down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic is moving at a steady speed.\n\n",
+    "BDDX_Processed/video/061b081d-b5e37863_06479.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.59, 7.62, 7.65, 7.27, 6.38, 5.42, 4.12]\n Curvature: [0.03, 0.04, 0.04, 0.05, 0.06, 0.08, 0.05]\n Acceleration: [1.75, 0.58, -0.48, -1.46, -2.1, -2.8, -2.82]\n Course: [0.0, -0.14, -0.14, -0.14, -0.09, -0.14, -0.04]\nWhat is the action of ego car?\nAssistant: The car slows to a stop\n\nHuman: Why does the ego car doing this?\nAssistant: since the light turned red and the car ahead has stopped.\n\n",
+    "BDDX_Processed/video/1496a55a-06a6b599_14003.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.03, 4.63, 7.56, 10.33, 12.21, 12.59, 11.78]\n Curvature: [0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [-0.07, 1.59, 1.58, 1.34, 0.62, -0.21, -0.33]\n Course: [0.0, 0.0, 0.0, 0.03, -0.13, 0.0, -0.17]\nWhat is the action of ego car?\nAssistant: The car is accelerating again\n\nHuman: Why does the ego car doing this?\nAssistant: Because it has stopped at the stop sign and is clear to move forward.\n\n",
+    "BDDX_Processed/video/17ffc2bb-0536c94a_16016.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.21, 7.66, 8.08, 8.27, 8.43, 8.27, 8.08]\n Curvature: [-0.02, -0.01, 0.0, -0.0, -0.01, -0.0, -0.0]\n Acceleration: [1.08, 0.84, 0.61, 0.3, 0.01, -0.23, -0.47]\n Course: [0.0, -0.02, -0.02, 0.0, 0.03, 0.1, 0.03]\nWhat is the action of ego car?\nAssistant: the car returns to the left lane and accelerates forward\n\nHuman: Why does the ego car doing this?\nAssistant: as the lane ahead is clear.\n\n",
+    "BDDX_Processed/video/076399f2-3cc0e470_07128.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.73, 0.84, 0.94, 1.04, 1.15, 1.23, 1.29]\n Curvature: [0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.34, 0.35, 0.35, 0.35, 0.33, 0.21, 0.11]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: because the car in front of them is moving and the light is green.\n\n",
+    "BDDX_Processed/video/132f36a5-3e7891ae_13296.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.69, 11.15, 10.82, 11.29, 12.27, 13.56, 13.95]\n Curvature: [-0.0, 0.0, -0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.72, 0.01, 0.04, 0.64, 0.97, 0.75, -0.96]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, -0.16, 0.0]\nWhat is the action of ego car?\nAssistant: The car is going down the road\n\nHuman: Why does the ego car doing this?\nAssistant: because the road is empty.\n\n",
+    "BDDX_Processed/video/07058848-6862bfe4_00797.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [13.28, 12.0, 11.81, 11.86, 11.52, 11.59, 10.77]\n Curvature: [-0.05, 0.02, 0.01, 0.0, 0.04, -0.02, -0.03]\n Acceleration: [-0.65, -0.32, -0.05, -0.12, -0.14, -0.14, -0.67]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.07]\nWhat is the action of ego car?\nAssistant: The car maintains the slower speed\n\nHuman: Why does the ego car doing this?\nAssistant: because traffic has slowed.\n\n",
+    "BDDX_Processed/video/1aa6dd36-da527525_17233.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.05, 5.15, 3.0, 1.27, 0.82, 0.58, 0.74]\n Curvature: [-0.0, 0.0, -0.49, -0.84, -0.81, -0.81, -0.41]\n Acceleration: [-1.07, -1.67, -1.84, -0.54, -0.33, 0.02, 0.09]\n Course: [0.0, 0.0, 0.0, 14.81, -55.31, 3.05, 2.58]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because it almost missed it's turn.\n\n",
+    "BDDX_Processed/video/0b6dd7d8-ff3ac76b_09361.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.92, 2.06, 3.47, 4.51, 5.49, 6.44]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0]\n Acceleration: [1.42, 1.67, 1.82, 1.87, 1.66, 1.47, 1.37]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating\n\nHuman: Why does the ego car doing this?\nAssistant: as cars in front of it accelerate at a green light.\n\n",
+    "BDDX_Processed/video/0d1fc580-2bbf8587_10340.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [12.09, 11.14, 10.31, 9.36, 8.5, 8.28, 8.09]\n Curvature: [0.02, 0.02, 0.01, 0.01, -0.0, -0.03, -0.06]\n Acceleration: [-2.79, -2.63, -2.49, -2.33, -2.13, -1.65, -1.22]\n Course: [0.0, 0.02, 0.01, 0.02, 0.08, 0.14, 0.07]\nWhat is the action of ego car?\nAssistant: The car slows and moves into the middle lane\n\nHuman: Why does the ego car doing this?\nAssistant: because the far left lane is full of stopped traffic.\n\n",
+    "BDDX_Processed/video/05a93853-0d9f37e7_01250.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.53, 4.39, 4.27, 4.13, 3.98, 3.76, 3.56]\n Curvature: [0.03, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01]\n Acceleration: [0.02, -0.17, -0.33, -0.52, -0.68, -0.6, -0.53]\n Course: [0.0, -0.29, -0.15, -0.29, -0.23, -0.17, -0.09]\nWhat is the action of ego car?\nAssistant: The car slows, still veering left\n\nHuman: Why does the ego car doing this?\nAssistant: to merge behind a slow moving vehicle.\n\n",
+    "BDDX_Processed/video/053da4e3-48ec49ba_00179.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.77, 5.24, 5.71, 6.18, 6.59, 6.96, 7.34]\n Curvature: [0.0, 0.0, 0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [2.2, 2.04, 1.89, 1.74, 1.61, 1.35, 1.09]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car drifts left and accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: so that it can get around the other vehicle.\n\n",
+    "BDDX_Processed/video/067ebca1-36bf70d8_06624.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.03, 4.08, 4.13, 4.17, 4.22, 4.26, 4.29]\n Curvature: [-0.0, -0.01, -0.01, -0.02, -0.02, -0.0, 0.02]\n Acceleration: [0.16, 0.16, 0.16, 0.16, 0.15, 0.1, 0.03]\n Course: [0.0, -0.01, -0.02, -0.01, 0.01, 0.02, 0.05]\nWhat is the action of ego car?\nAssistant: The car is turning left\n\nHuman: Why does the ego car doing this?\nAssistant: to enter another road.\n\n",
+    "BDDX_Processed/video/06ff7d61-1fa52d1c_06854.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [5.36, 5.56, 5.76, 6.09, 6.54, 7.04, 7.62]\n Curvature: [-0.52, -0.38, -0.25, -0.14, -0.08, -0.01, -0.0]\n Acceleration: [0.03, 0.36, 0.69, 0.97, 1.15, 1.36, 1.36]\n Course: [0.0, 4.45, 4.45, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car slows and turns right\n\nHuman: Why does the ego car doing this?\nAssistant: to enter another road.\n\n",
+    "BDDX_Processed/video/1accee17-9a1b4b0e_17415.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.37, 2.98, 3.08, 3.75, 4.83, 6.18, 8.1]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.01]\n Acceleration: [-1.15, -0.28, 0.57, 1.3, 1.92, 2.31, 2.38]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car accelerates down the road\n\nHuman: Why does the ego car doing this?\nAssistant: since the road is clear of traffic.\n\n",
+    "BDDX_Processed/video/1f3f018d-a25650ad_19431.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [0.0, 0.91, 1.87, 2.98, 4.13, 5.27, 6.31]\n Curvature: [-0.01, -0.06, -0.05, -0.02, -0.0, -0.0, 0.0]\n Acceleration: [0.59, 1.09, 1.33, 1.44, 1.48, 1.39, 1.12]\n Course: [0.0, 0.07, -0.07, 0.0, -0.03, -0.07, 0.07]\nWhat is the action of ego car?\nAssistant: The car accelerates\n\nHuman: Why does the ego car doing this?\nAssistant: because the light turns green and traffic begins to move forward.\n\n",
+    "BDDX_Processed/video/21d61f0e-6ff313be_20690.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [7.82, 7.02, 6.29, 5.84, 5.45, 5.38, 5.27]\n Curvature: [-0.02, -0.01, 0.0, 0.01, 0.02, -0.0, -0.02]\n Acceleration: [-1.08, -1.13, -1.16, -0.8, -0.49, -0.4, -0.34]\n Course: [0.0, -0.05, -0.02, 0.05, 0.0, -0.05, 0.05]\nWhat is the action of ego car?\nAssistant: The car slows down\n\nHuman: Why does the ego car doing this?\nAssistant: because a pedestrian is crossing the road.\n\n",
+    "BDDX_Processed/video/0252f2b5-2e69185a_01431.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [8.1, 7.93, 7.78, 7.84, 8.21, 8.6, 9.02]\n Curvature: [-0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0]\n Acceleration: [-0.31, -0.07, 0.15, 0.41, 0.68, 0.98, 0.75]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is travelling down the road.\n\nHuman: Why does the ego car doing this?\nAssistant: There are no obstructions ahead of the car.\n\n",
+    "BDDX_Processed/video/21d61f0e-6ff313be_20691.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [4.79, 4.75, 4.71, 4.67, 4.63, 4.59, 4.55]\n Curvature: [-0.03, -0.02, -0.02, -0.02, -0.01, -0.01, -0.01]\n Acceleration: [-0.42, -0.41, -0.39, -0.38, -0.36, -0.35, -0.33]\n Course: [0.0, 0.11, 0.21, 0.11, 0.11, 0.11, 0.21]\nWhat is the action of ego car?\nAssistant: The car slows down further\n\nHuman: Why does the ego car doing this?\nAssistant: as it is preparing to make a turn and there are pedestrians crossing.\n\n",
+    "BDDX_Processed/video/18f3b2d1-4163de2e_16465.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [10.76, 9.92, 9.08, 8.15, 7.25, 6.09, 4.85]\n Curvature: [0.02, 0.02, 0.02, 0.01, 0.01, 0.0, -0.01]\n Acceleration: [-1.46, -1.58, -1.69, -1.86, -2.04, -2.39, -2.63]\n Course: [0.0, -0.29, -0.23, -0.33, -0.21, -0.1, -0.04]\nWhat is the action of ego car?\nAssistant: The car slows to a complete stop\n\nHuman: Why does the ego car doing this?\nAssistant: because the light ahead is red.\n\n",
+    "BDDX_Processed/video/10f1b8b5-cdd37d24_12122.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [3.74, 3.85, 3.96, 4.07, 4.11, 3.6, 3.1]\n Curvature: [0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n Acceleration: [0.05, -0.16, -0.37, -0.58, -0.79, -0.95, -1.12]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is slowing down.\n\nHuman: Why does the ego car doing this?\nAssistant: because there is a stopped car in front.\n\n",
+    "BDDX_Processed/video/065992a2-5bef4d51_02676.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [2.12, 1.7, 1.33, 0.91, 0.54, 0.56, 0.58]\n Curvature: [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]\n Acceleration: [-2.06, -1.71, -1.4, -1.05, -0.68, -0.14, 0.33]\n Course: [0.0, -0.33, -0.16, -0.33, -0.25, -0.16, -0.08]\nWhat is the action of ego car?\nAssistant: The car slows\n\nHuman: Why does the ego car doing this?\nAssistant: to allow cross traffic to pass.\n\n",
+    "BDDX_Processed/video/1698c1ee-12445160_15264.mp4": "Human: The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: [1.2, 1.7, 2.2, 2.7, 3.24, 4.05, 4.86]\n Curvature: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n Acceleration: [1.57, 1.82, 2.07, 2.32, 2.55, 2.61, 2.67]\n Course: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\nWhat is the action of ego car?\nAssistant: The car is accelerating and steering to the left\n\nHuman: Why does
\ No newline at end of file
diff --git a/retrieval/check_sim.py b/retrieval/check_sim.py
new file mode 100644
index 0000000..88f7f60
--- /dev/null
+++ b/retrieval/check_sim.py
@@ -0,0 +1,49 @@
+import json
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from tqdm import tqdm
+import re
+
+# match_path = f"./retrieval/BDDX_RAG_hybird_vpmatch.json"
+# match_path = f"./retrieval/BDDX_RAG_visual_vpmatch.json"
+# match_path = f"./retrieval/BDDX_RAG_visual_vpmatch.json"
+match_path = f"./retrieval/BDDX_RAG_tuned_vpmatch_t13.json"
+# match_path = f"./retrieval/BDDX_RAG_tuned_vpmatch_t9.json"
+id_info_path = "./retrieval/bddx_vpath_info_match.json"
+
+def get_text(text):
+    # Regex pattern to find the action and justification
+    action_pattern = r"What is the action of ego car\?\nAssistant: (.+?)\n"
+    justification_pattern = r"Why does the ego car doing this\?\nAssistant: (.+?)\n"
+
+    # Extracting action and justification
+    action_match = re.search(action_pattern, text)
+    justification_match = re.search(justification_pattern, text)
+
+    action = action_match.group(1) if action_match else "No action found"
+    justification = justification_match.group(1) if justification_match else "No justification found"
+
+    return [action, justification]
+
+with open(match_path,"r") as fm:
+    match_dict = json.load(fm)
+with open(id_info_path,"r") as fi:
+    id_info_match = json.load(fi)
+    
+id_info_match = {vp:" ".join(get_text(cont)) for vp, cont in id_info_match.items()}
+    
+vectorizer = TfidfVectorizer()
+sim_f = []
+for vp, rag in tqdm(match_dict.items()):
+    cq_info = id_info_match[vp]
+    rag_infos = [id_info_match[rvp] for rvp in rag]
+    # print(rag_infos)
+    # break
+    info_lis = [cq_info] + rag_infos
+    tfidf_matrix = vectorizer.fit_transform(info_lis)
+    sim = cosine_similarity(tfidf_matrix)
+    mean_sim = sum(sim[0][1:])
+    sim_f.append(mean_sim)
+
+print(sum(sim_f))
+print(sum(sim_f)/len(sim_f))
\ No newline at end of file
diff --git a/retrieval/embeddings_project.npz b/retrieval/embeddings_project.npz
new file mode 100644
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diff --git a/retrieval/final_match.py b/retrieval/final_match.py
new file mode 100644
index 0000000..60c7491
--- /dev/null
+++ b/retrieval/final_match.py
@@ -0,0 +1,238 @@
+import numpy as np
+import re
+import json
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.metrics.pairwise import pairwise_distances
+from sklearn.feature_extraction.text import TfidfVectorizer
+from scipy.spatial.distance import pdist, squareform
+from tqdm import tqdm
+
+
+def normalize_data(data, method='z-score'):
+    """
+    Normalize the data.
+
+    Parameters:
+    data (numpy.ndarray): The data to normalize.
+    method (str, optional): The normalization method ('min-max' or 'z-score').
+
+    Returns:
+    numpy.ndarray: Normalized data.
+    """
+    if method == 'min-max':
+        min_val = np.min(data)
+        max_val = np.max(data)
+        normalized = (data - min_val) / (max_val - min_val)
+    elif method == 'z-score':
+        mean = np.mean(data)
+        std = np.std(data)
+        normalized = (data - mean) / std
+    else:
+        raise ValueError("Unknown normalization method.")
+
+    return normalized
+
+def compute_signal_similarity(data_sets, signal_index):
+    """
+    Compute the similarity matrix for a specific type of signal.
+
+    Parameters:
+    data_sets (list of tuples or lists): A list of data sets, where each data set contains multiple signals.
+    signal_index (int): The index of the signal to compute similarity for.
+
+    Returns:
+    numpy.ndarray: A similarity matrix for the specified signal.
+    """
+
+    # Extract the specific signal from each data set
+    # specific_signals = [data_set[signal_index] for data_set in data_sets]
+    expected_length = 7
+    pad_value = 0
+    # Extract and pad the specific signal from each data set
+    specific_signals = []
+    for data_set in tqdm(data_sets):
+        signal = data_set[signal_index]
+        if (expected_length - len(signal)) != 0:
+            print(expected_length - len(signal))
+        padded_signal = list(signal) + [pad_value] * (expected_length - len(signal))
+        specific_signals.append(padded_signal[:expected_length])
+        
+    # Normalize the signals
+    flattened_signals = np.array(specific_signals)
+    # for i in range(flattened_signals.shape[1]):
+    #     flattened_signals[:, i] = normalize_data(flattened_signals[:, i], "min-max")
+
+    # Flatten each signal and compute pairwise Euclidean distance
+    
+    distances = pdist(flattened_signals, 'euclidean')
+    distance_matrix = squareform(distances)
+
+    # Convert distances to similarities
+    similarity_matrix = 1 / (1 + distance_matrix)
+    
+    print(np.mean(similarity_matrix))
+    
+    return similarity_matrix
+
+def aggregate_similarities(data_sets, num_signals):
+    """
+    Aggregate the similarities of each signal type into a combined similarity measure.
+
+    Parameters:
+    data_sets (list of tuples or lists): A list of data sets, where each data set contains multiple signals.
+    num_signals (int): The number of different signals in each data set.
+
+    Returns:
+    numpy.ndarray: A combined similarity matrix.
+    """
+    combined_similarity = np.zeros((len(data_sets), len(data_sets)))
+    weight = [0.70702137, 0.0485446,  0.17451003, 0.069924]
+    # manual = []
+    # weight = [w[j]*manual[j] for j in range(4)]
+    # weight = [(j/sum(weight)) for j in weight]
+    print(weight)
+    # normv = [0.25,0.25,0.25,0.25] # S Cur Acc Cour
+    name = ['speed','curvature','acceleration','course']
+    for i in [0,1,2,3]:
+        signal_similarity = compute_signal_similarity(data_sets, i)
+        # np.save(f'{name[i]}.npy', signal_similarity)
+        combined_similarity += weight[i] * signal_similarity
+
+    # Average the combined similarities
+    # combined_similarity /= num_signals
+
+    return combined_similarity
+
+
+def extract_vehicle_signals(input_string):
+    # Patterns for matching speed, curvature, acceleration, and course data
+    speed_pattern = r"Speed: \[([0-9., -]+)\]"
+    curvature_pattern = r"Curvature: \[([0-9., -]+)\]"
+    acceleration_pattern = r"Acceleration: \[([0-9., -]+)\]"
+    course_pattern = r"Course: \[([0-9., -]+)\]"
+
+    # Function to extract and convert data to a list of floats
+    def extract_data(pattern):
+        match = re.search(pattern, input_string)
+        return [float(x) for x in match.group(1).split(',')] if match else []
+
+    # Extract data
+    speed = extract_data(speed_pattern)
+    curvature = extract_data(curvature_pattern)
+    acceleration = extract_data(acceleration_pattern)
+    course = extract_data(course_pattern)
+
+    return speed, curvature, acceleration, course
+
+
+
+def get_normal_embedding(dataset, strategy):
+
+    if dataset == "BDDX":
+        train_conv_path = "./video_process/final_conv_base/conversation_bddx_train.json"
+        test_conv_path = "./video_process/final_conv_base/conversation_bddx_eval.json"
+            
+        with open(train_conv_path,"r") as ftr:
+            train_conv = json.load(ftr)
+        with open(test_conv_path,"r") as fte:
+            test_conv = json.load(fte)
+        conv = train_conv + test_conv
+        # Load embeddings
+        vp_emb_match = {}
+
+        npz_file = np.load("./retrieval/embeddings_project.npz")
+        for key in npz_file:
+            vp_emb_match[key] = npz_file[key]
+        npz_file.close()
+        vp_conversation_match = {ele["video"]: ' '.join([ele["conversations"][1]['value'],ele["conversations"][3]['value']]) for ele in conv}
+        # Info Match
+        with open("./retrieval/bddx_vpath_info_match.json",'r') as im:
+            vp_info = json.load(im)
+
+        vp_signal_match = {vp['video']: vp_info[vp['video']] for vp in conv}
+        
+        
+    elif dataset == 'SAX':
+        train_conv_path = "./video_process/final_conv_base/conversation_bddx_train.json"
+        test_conv_path = "./video_process/final_sax_conv_base/conversation_sax_eval.json"
+        with open(train_conv_path,"r") as ftr:
+            train_conv = json.load(ftr)
+        with open(test_conv_path,"r") as fte:
+            test_conv = json.load(fte)
+        conv = train_conv + test_conv
+                # Load embeddings
+        vp_emb_match = {}
+        npz_file1 = np.load("./video_process/SAX/new_emb/embeddings.npz")
+        npz_file2 = np.load("./video_process/BDDX_Processed/new_emb/embeddings.npz")
+        for key in npz_file1:
+            vp_emb_match[key] = npz_file1[key]
+        npz_file1.close()
+        for key in npz_file2:
+            vp_emb_match[key] = npz_file2[key]
+        npz_file2.close()
+        vp_conversation_match = {ele["video"][0]: ' '.join([ele["conversations"][1]['value'],ele["conversations"][3]['value']]) for ele in conv}
+        vp_signal_match = None
+    vpath_lis = [vp for vp in vp_emb_match]
+    sorted_video_ids = [i for i in range(len(vpath_lis))]
+    vpath_id_match = {i:vpath_lis[i] for i in range(len(vpath_lis))}
+    
+    # Process embeddings
+    
+    
+    embeddings = np.array([vp_emb_match[vp] for vp in vp_emb_match])
+    embeddings = embeddings.reshape(embeddings.shape[0], embeddings.shape[2])
+    print("EMB SHAPE",embeddings.shape)
+
+    norm_embeddings = embeddings
+    norm_embeddings = embeddings / np.linalg.norm(embeddings, axis=1, keepdims=True)
+    
+    return norm_embeddings, vp_conversation_match, sorted_video_ids, vpath_id_match, vp_signal_match
+
+def get_similarity(norm_embeddings):
+    
+    similarities = cosine_similarity(norm_embeddings)
+        
+    return similarities
+
+
+if __name__ == "__main__":
+    k = 2
+    dataset = 'SAX'
+    
+    retr_strategy = "hybrid"
+    
+    norm_embeddings, vp_conversation_match, sorted_video_ids, vpath_id_match, vp_signal_match = get_normal_embedding(dataset, retr_strategy)
+
+    combined_similarity = get_similarity(norm_embeddings)
+
+    # Determine top k similar videos (assuming k is defined)
+    top_k_indices = np.argsort(-combined_similarity, axis=1)[:, 1:100]
+
+    # Update the Similarity Dictionary with combined similarities
+    cosine_similarity_dict = {sorted_video_ids[i]: [sorted_video_ids[j] for j in top_k_indices[i]] for i in range(len(sorted_video_ids))}
+    
+    # ID Match to vpath match
+    final_vpath_match = {}
+    for key, val in cosine_similarity_dict.items():
+        cur_k = vpath_id_match[key]
+        cur_v = [vpath_id_match[v] for v in val]
+        final_vpath_match.update({cur_k:cur_v})
+        
+    # Filter Test In Train
+    t_dict = {}
+    for key, val in final_vpath_match.items():
+        v = [vp for vp in val]
+        v = v[:k]
+        t_dict.update({key:v})
+        
+    final_vpath_match = t_dict
+    
+
+    # Save the cosine similarity dictionary
+    if dataset == 'BDDX':
+        with open(f"./retrieval/BDDX_RAG_{retr_strategy}_vpmatch_t13.json", "w") as f:
+            json.dump(final_vpath_match, f, indent=4)
+            
+    elif dataset == 'SAX':
+        with open(f"./retrieval/SAX_RAG_{retr_strategy}_vpmatch.json", "w") as f:
+            json.dump(final_vpath_match, f, indent=4)
\ No newline at end of file
diff --git a/retrieval/matching.py b/retrieval/matching.py
new file mode 100644
index 0000000..0adefe3
--- /dev/null
+++ b/retrieval/matching.py
@@ -0,0 +1,327 @@
+import numpy as np
+import re
+import json
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.metrics.pairwise import pairwise_distances
+from sklearn.feature_extraction.text import TfidfVectorizer
+from scipy.spatial.distance import pdist, squareform
+from tqdm import tqdm
+
+
+def normalize_data(data, method='z-score'):
+    """
+    Normalize the data.
+
+    Parameters:
+    data (numpy.ndarray): The data to normalize.
+    method (str, optional): The normalization method ('min-max' or 'z-score').
+
+    Returns:
+    numpy.ndarray: Normalized data.
+    """
+    if method == 'min-max':
+        min_val = np.min(data)
+        max_val = np.max(data)
+        normalized = (data - min_val) / (max_val - min_val)
+    elif method == 'z-score':
+        mean = np.mean(data)
+        std = np.std(data)
+        normalized = (data - mean) / std
+    else:
+        raise ValueError("Unknown normalization method.")
+
+    return normalized
+
+def compute_signal_similarity(data_sets, signal_index):
+    """
+    Compute the similarity matrix for a specific type of signal.
+
+    Parameters:
+    data_sets (list of tuples or lists): A list of data sets, where each data set contains multiple signals.
+    signal_index (int): The index of the signal to compute similarity for.
+
+    Returns:
+    numpy.ndarray: A similarity matrix for the specified signal.
+    """
+
+    # Extract the specific signal from each data set
+    # specific_signals = [data_set[signal_index] for data_set in data_sets]
+    expected_length = 7
+    pad_value = 0
+    # Extract and pad the specific signal from each data set
+    specific_signals = []
+    for data_set in tqdm(data_sets):
+        signal = data_set[signal_index]
+        if (expected_length - len(signal)) != 0:
+            print(expected_length - len(signal))
+        padded_signal = list(signal) + [pad_value] * (expected_length - len(signal))
+        specific_signals.append(padded_signal[:expected_length])
+        
+    # Normalize the signals
+    flattened_signals = np.array(specific_signals)
+    # for i in range(flattened_signals.shape[1]):
+    #     flattened_signals[:, i] = normalize_data(flattened_signals[:, i], "min-max")
+
+    # Flatten each signal and compute pairwise Euclidean distance
+    
+    distances = pdist(flattened_signals, 'euclidean')
+    distance_matrix = squareform(distances)
+
+    # Convert distances to similarities
+    similarity_matrix = 1 / (1 + distance_matrix)
+    
+    print(np.mean(similarity_matrix))
+    
+    return similarity_matrix
+
+def aggregate_similarities(data_sets, num_signals):
+    """
+    Aggregate the similarities of each signal type into a combined similarity measure.
+
+    Parameters:
+    data_sets (list of tuples or lists): A list of data sets, where each data set contains multiple signals.
+    num_signals (int): The number of different signals in each data set.
+
+    Returns:
+    numpy.ndarray: A combined similarity matrix.
+    """
+    combined_similarity = np.zeros((len(data_sets), len(data_sets)))
+    weight = [0.70702137, 0.0485446,  0.17451003, 0.069924]
+    # manual = []
+    # weight = [w[j]*manual[j] for j in range(4)]
+    # weight = [(j/sum(weight)) for j in weight]
+    print(weight)
+    # normv = [0.25,0.25,0.25,0.25] # S Cur Acc Cour
+    name = ['speed','curvature','acceleration','course']
+    for i in [0,1,2,3]:
+        signal_similarity = compute_signal_similarity(data_sets, i)
+        # np.save(f'{name[i]}.npy', signal_similarity)
+        combined_similarity += weight[i] * signal_similarity
+
+    # Average the combined similarities
+    # combined_similarity /= num_signals
+
+    return combined_similarity
+
+
+def extract_vehicle_signals(input_string):
+    # Patterns for matching speed, curvature, acceleration, and course data
+    speed_pattern = r"Speed: \[([0-9., -]+)\]"
+    curvature_pattern = r"Curvature: \[([0-9., -]+)\]"
+    acceleration_pattern = r"Acceleration: \[([0-9., -]+)\]"
+    course_pattern = r"Course: \[([0-9., -]+)\]"
+
+    # Function to extract and convert data to a list of floats
+    def extract_data(pattern):
+        match = re.search(pattern, input_string)
+        return [float(x) for x in match.group(1).split(',')] if match else []
+
+    # Extract data
+    speed = extract_data(speed_pattern)
+    curvature = extract_data(curvature_pattern)
+    acceleration = extract_data(acceleration_pattern)
+    course = extract_data(course_pattern)
+
+    return speed, curvature, acceleration, course
+
+
+
+def get_normal_embedding(dataset, strategy):
+
+    if dataset == "BDDX":
+        train_conv_path = "./video_process/final_conv_base/conversation_bddx_train.json"
+        test_conv_path = "./video_process/final_conv_base/conversation_bddx_eval.json"
+            
+        with open(train_conv_path,"r") as ftr:
+            train_conv = json.load(ftr)
+        with open(test_conv_path,"r") as fte:
+            test_conv = json.load(fte)
+        conv = train_conv + test_conv
+        # Load embeddings
+        vp_emb_match = {}
+        if strategy != 'tuned':
+            npz_file = np.load("./video_process/BDDX_Processed/new_emb/embeddings.npz")
+        else:
+            npz_file = np.load("./retrieval/embeddings_project_2.npz")
+        for key in npz_file:
+            vp_emb_match[key] = npz_file[key]
+        npz_file.close()
+        vp_conversation_match = {ele["video"]: ' '.join([ele["conversations"][1]['value'],ele["conversations"][3]['value']]) for ele in conv}
+        # Info Match
+        with open("/zhaobai46g/Project/Video-LLaVA/retrieval/bddx_vpath_info_match.json",'r') as im:
+            vp_info = json.load(im)
+
+        vp_signal_match = {vp['video']: vp_info[vp['video']] for vp in conv}
+        
+        
+    elif dataset == 'SAX':
+        train_conv_path = "./video_process/final_sax_conv_base/conversation_sax_train.json"
+        test_conv_path = "./video_process/final_sax_conv_base/conversation_sax_eval.json"
+            
+        with open(train_conv_path,"r") as ftr:
+            train_conv = json.load(ftr)
+        with open(test_conv_path,"r") as fte:
+            test_conv = json.load(fte)
+        conv = train_conv + test_conv
+        # Load embeddings
+        vp_emb_match = {}
+        npz_file = np.load("./video_process/SAX/new_emb/embeddings.npz")
+        
+        for key in npz_file:
+            vp_emb_match[key] = npz_file[key]
+        npz_file.close()
+        vp_conversation_match = {ele["video"][0]: ' '.join([ele["conversations"][1]['value'],ele["conversations"][3]['value']]) for ele in conv}
+        
+        
+    elif dataset == 'SAX_ZS':
+        train_conv_path = "./video_process/final_conv_base/conversation_bddx_train.json"
+        test_conv_path = "./video_process/final_sax_conv_base/conversation_sax_eval.json"
+        with open(train_conv_path,"r") as ftr:
+            train_conv = json.load(ftr)
+        with open(test_conv_path,"r") as fte:
+            test_conv = json.load(fte)
+        conv = train_conv + test_conv
+                # Load embeddings
+        vp_emb_match = {}
+        npz_file1 = np.load("./video_process/SAX/new_emb/embeddings.npz")
+        npz_file2 = np.load("./video_process/BDDX_Processed/new_emb/embeddings.npz")
+        for key in npz_file1:
+            vp_emb_match[key] = npz_file1[key]
+        npz_file1.close()
+        for key in npz_file2:
+            vp_emb_match[key] = npz_file2[key]
+        npz_file2.close()
+        vp_conversation_match = {ele["video"][0]: ' '.join([ele["conversations"][1]['value'],ele["conversations"][3]['value']]) for ele in conv}
+        # vp_signal_match = {ele["video"][0]: ' '.join([ele["conversations"][1]['value'],ele["conversations"][3]['value']]) for ele in conv}
+    
+    # print(len(vp_conversation_match))
+    # import pdb; pdb.set_trace()
+    vpath_lis = [vp for vp in vp_emb_match]
+    sorted_video_ids = [i for i in range(len(vpath_lis))]
+    vpath_id_match = {i:vpath_lis[i] for i in range(len(vpath_lis))}
+    
+    # Process embeddings
+    
+    embeddings = np.array([vp_emb_match[vp] for vp in vp_emb_match])
+    embeddings = embeddings.reshape(embeddings.shape[0], embeddings.shape[2])
+    print("EMB SHAPE",embeddings.shape)
+
+    # Normalize the embeddings
+    
+    # norm_embeddings = embeddings / np.linalg.norm(embeddings, axis=1, keepdims=True)
+    norm_embeddings = embeddings
+    
+    return norm_embeddings, vp_conversation_match, sorted_video_ids, vpath_id_match, vp_signal_match
+
+def get_similarity(norm_embeddings, vp_conversation_match, vp_signal_match, strategy='hybrid'):
+    # Compute Cosine Similarities for embeddings
+    
+    cosine_similarities = cosine_similarity(norm_embeddings)
+    
+    # OR
+    
+    # euclidean_distances = pairwise_distances(norm_embeddings, metric='euclidean')
+    # cosine_similarities = 1 / (1 + euclidean_distances)
+
+    if strategy=='hybrid':
+        # vp_pure_signal = {vp:extract_vehicle_signals(val) for vp, val in vp_signal_match.items()}
+        # pure_signal = [v for v in vp_pure_signal.values()]
+        # textual_similarity = aggregate_similarities(pure_signal,4)
+        # print(textual_similarity.shape)
+        # Compute TF-IDF based textual similarity
+        vectorizer = TfidfVectorizer()
+        tfidf_matrix = vectorizer.fit_transform(vp_conversation_match.values())
+        textual_similarity = cosine_similarity(tfidf_matrix)
+        # print(textual_similarity.shape)
+        # import pdb; pdb.set_trace()
+        # Combine embedding and textual similarities (example: equal weight)
+        # get hybrid similarity:
+        random_similarity = np.random.rand(cosine_similarities.shape[0], cosine_similarities.shape[1])
+
+        print("Cos",np.mean(cosine_similarities),cosine_similarities.shape,"Text",np.mean(textual_similarity),textual_similarity.shape)
+        # Normalizing weights
+        embedding_weight = 0.0
+        text_weight = 1.0
+        random_weight = 0.0
+        total_weight = embedding_weight + text_weight + random_weight
+        normalized_embedding_weight = embedding_weight / total_weight
+        normalized_text_weight = text_weight / total_weight
+        normalized_random_weight = random_weight / total_weight
+
+        # Combine the similarities
+        combined_similarity = (normalized_embedding_weight * cosine_similarities) + \
+                            (normalized_text_weight * textual_similarity) + \
+                            (normalized_random_weight * random_similarity)
+
+        # Ensure self-similarity is still 0
+        np.fill_diagonal(combined_similarity, 0)
+        
+    elif strategy=='visual':
+        combined_similarity = cosine_similarities
+        
+    elif strategy=='tuned':
+        
+        combined_similarity = cosine_similarities
+        # Set the model to evaluation mode
+        
+    
+    return combined_similarity
+
+
+if __name__ == "__main__":
+    k = 3
+    dataset = 'BDDX'
+    
+    retr_strategy = "hybrid"
+    
+    norm_embeddings, vp_conversation_match, sorted_video_ids, vpath_id_match, vp_signal_match = get_normal_embedding(dataset, retr_strategy)
+
+    combined_similarity = get_similarity(norm_embeddings, vp_conversation_match, vp_signal_match, strategy=retr_strategy)
+
+    # Determine top k similar videos (assuming k is defined)
+    # top_k_indices = np.argsort(-combined_similarity, axis=1)[:, 1:k+1]
+    top_k_indices = np.argsort(-combined_similarity, axis=1)[:, 1:k+1]
+    dowm_k_indices = np.argsort(combined_similarity, axis=1)[:, 200:3000]
+    # np.save("optimal.npy",top_k_indices)
+    
+    # Update the Similarity Dictionary with combined similarities
+    cosine_similarity_dict = {sorted_video_ids[i]: [sorted_video_ids[j] for j in top_k_indices[i]] for i in range(len(sorted_video_ids))}
+    neg_cosine_similarity_dict = {sorted_video_ids[i]: [sorted_video_ids[j] for j in dowm_k_indices[i]] for i in range(len(sorted_video_ids))}
+    
+    # ID Match to vpath match
+    final_vpath_match = {}
+    for key, val in cosine_similarity_dict.items():
+        cur_k = vpath_id_match[key]
+        cur_v = [vpath_id_match[v] for v in val]
+        final_vpath_match.update({cur_k:cur_v})
+    # Filter Test In Train
+    t_dict = {}
+    for key, val in final_vpath_match.items():
+        # v = [vp for vp in val if not ("Test" in vp and "Processed" in key)]
+        # v = [vp for vp in val if not ("Test" in vp)]
+        v = [vp for vp in val]
+        v = v[:k]
+        t_dict.update({key:v})
+        
+    final_vpath_match = t_dict
+    
+    # Neg match
+    nfinal_vpath_match = {}
+    for key, val in neg_cosine_similarity_dict.items():
+        cur_k = vpath_id_match[key]
+        cur_v = [vpath_id_match[v] for v in val]
+        nfinal_vpath_match.update({cur_k:cur_v})
+    t_dict = {}
+    for key, val in nfinal_vpath_match.items():
+        v = [vp for vp in val]
+        v = v[:1000]
+        t_dict.update({key:v})
+        
+    nfinal_vpath_match = t_dict
+    
+    with open(f"./retrieval/BDDX_RAG_pos_3.json", "w") as f:
+        json.dump(final_vpath_match, f, indent=4)
+            
+    with open(f"./retrieval/BDDX_RAG_neg_3.json", "w") as f:
+        json.dump(nfinal_vpath_match, f, indent=4)
+    
\ No newline at end of file
diff --git a/retrieval/project.py b/retrieval/project.py
new file mode 100644
index 0000000..ce58a93
--- /dev/null
+++ b/retrieval/project.py
@@ -0,0 +1,105 @@
+from torch.utils.data import Dataset, DataLoader
+import torch
+import torch.nn as nn
+import json 
+import os
+import numpy as np
+import random
+from tqdm import tqdm
+import re
+
+def extract_vehicle_signals(input_string):
+    # Patterns for matching speed, curvature, acceleration, and course data
+    speed_pattern = r"Speed: \[([0-9., -]+)\]"
+    curvature_pattern = r"Curvature: \[([0-9., -]+)\]"
+    acceleration_pattern = r"Acceleration: \[([0-9., -]+)\]"
+    course_pattern = r"Course: \[([0-9., -]+)\]"
+
+    # Function to extract and convert data to a list of floats
+    def extract_data(pattern):
+        match = re.search(pattern, input_string)
+        return [float(x) for x in match.group(1).split(',')] if match else []
+
+    # Extract data
+    speed = extract_data(speed_pattern)
+    curvature = extract_data(curvature_pattern)
+    acceleration = extract_data(acceleration_pattern)
+    course = extract_data(course_pattern)
+
+    return speed, curvature, acceleration, course
+
+
+
+class MLP(nn.Module):
+    def __init__(self):
+        super(MLP, self).__init__()
+        self.layer_norm = nn.LayerNorm(1052)  # Normalize the entire concatenated input
+
+        self.layers = nn.Sequential(
+            nn.Linear(1052, 2048),
+            nn.GELU(),
+            nn.Linear(2048, 4096),
+            nn.GELU(),
+            nn.Linear(4096, 2048),
+            nn.GELU(),
+            nn.Linear(2048, 4096),
+            nn.GELU(),
+            nn.Linear(4096, 8192)  
+        )
+
+    def forward(self, x):
+        x = self.layer_norm(x)
+        return self.layers(x)
+
+
+
+
+def get_emb():
+    train_conv_path = "./video_process/final_conv_base/conversation_bddx_train.json"
+    test_conv_path = "./video_process/final_conv_base/conversation_bddx_eval.json"
+        
+    with open(train_conv_path,"r") as ftr:
+        train_conv = json.load(ftr)
+    with open(test_conv_path,"r") as fte:
+        test_conv = json.load(fte)
+    conv = train_conv + test_conv
+    # Load embeddings
+    vp_emb_match = {}
+    npz_file = np.load("./video_process/BDDX_Processed/new_emb/embeddings.npz")
+    for key in npz_file:
+        vp_emb_match[key] = npz_file[key]
+    npz_file.close()
+    
+    # Info
+    with open("./retrieval/bddx_vpath_info_match.json",'r') as im:
+        vp_info = json.load(im)
+    vp_signal_match = {vp['video']: vp_info[vp['video']] for vp in conv}
+    vp_pure_signal = {vp:extract_vehicle_signals(val) for vp, val in vp_signal_match.items()}
+    
+    # Concat
+    vp_f_emb = {}
+    for key in vp_emb_match.keys():
+        sig = np.concatenate([np.array(lst) for lst in vp_pure_signal[key]]).reshape(1,28)
+        # print(sig.shape, vp_emb_match[key].shape)
+        val = np.concatenate((vp_emb_match[key], sig), axis=1).reshape(1052).astype(np.float32)
+        # print(val.shape)
+        vp_f_emb.update({key:val})
+    
+    return vp_f_emb
+
+vp_f_emb = get_emb()
+model = MLP()
+checkpoint = torch.load('./retrieval/projector/best_model.pth')
+model.load_state_dict(checkpoint)
+print("Loaded")
+model.eval()
+psim = {}
+with torch.no_grad():
+    for vp, emb in tqdm(vp_f_emb.items()):
+        predictions = model(torch.tensor(emb))
+        p = predictions.unsqueeze(0).numpy()
+        # print(p.shape)
+        psim.update({vp:p})
+        # break
+    
+np.savez(f'./retrieval/embeddings_project.npz', **psim)
\ No newline at end of file
diff --git a/retrieval/projector/best_model.pth b/retrieval/projector/best_model.pth
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diff --git a/retrieval/rag.py b/retrieval/rag.py
new file mode 100644
index 0000000..043dacd
--- /dev/null
+++ b/retrieval/rag.py
@@ -0,0 +1,85 @@
+import numpy as np
+import json
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.feature_extraction.text import TfidfVectorizer
+import random
+import math
+from tqdm import tqdm
+
+def random_subset(sample_list):
+    """
+    Randomly selects a varying number of elements (1 to 5) from the provided list.
+
+    Parameters:
+    sample_list (list): The list from which to select elements.
+
+    Returns:
+    list: A list containing a random subset of the original list.
+    """
+    # Randomly choose how many elements to select (between 1 and 5)
+    # num_elements = random.randint(1, min(5, len(sample_list)))
+    num_elements = 2
+
+    # Randomly select the elements
+    return random.sample(sample_list, num_elements)
+
+if __name__ == "__main__":
+    dataset = 'BDDX'
+    split = 'train'
+    
+    if_hybird = True
+    retr_strategy = "hybird"
+
+    if dataset == "BDDX":
+        if split == 'train':
+            conv_path = f"./video_process/final_conv_base/conversation_bddx_{split}.json"
+        elif split == 'test':
+            conv_path = f"./video_process/final_conv_base/conversation_bddx_eval.json"
+        with open(conv_path, "r") as fj:
+            conv = json.load(fj)
+
+        # Load embeddings
+        id_emb_match = {}
+        if split == 'train':
+            npz_file = np.load("./video_process/BDDX_Processed/embeddings_train.npz")
+        elif split == 'test':
+            npz_file = np.load("./video_process/BDDX_Test/embeddings_test.npz")
+            
+        for key in npz_file:
+            id_emb_match[key] = npz_file[key]
+        npz_file.close()
+
+    id_vpath_match = {ele["id"]: ele["video"] for ele in conv if ele["id"] in id_emb_match}
+    vpath_id_match = {val:key for key,val in id_vpath_match.items()}
+    id_conversation_match = {ele["id"]: ' '.join([ele["conversations"][1]['value'],ele["conversations"][3]['value']]) for ele in conv if ele["id"] in id_emb_match}
+
+    # match json
+
+    match_path = f"./retrieval/match_{retr_strategy}_similarity_{split}.json"
+    with open(match_path,"r") as fmm:
+        mm = json.load(fmm)
+
+    new_mm = {}
+    # Fixed Random sample
+    exclude = []
+    for key,val in mm.items():
+        new_val = [id_vpath_match[ele] for ele in id_vpath_match if ele not in exclude]
+        new_val = random_subset(new_val)
+        new_mm.update({id_vpath_match[key]:new_val})
+
+    id_match = {}
+    for key,val in tqdm(new_mm.items()):
+        new_val = [vpath_id_match[ele] for ele in val]
+        id_match.update({vpath_id_match[key]:new_val})
+
+
+
+    if dataset == 'BDDX':
+        with open(f"./retrieval/BDDX_RAG_{retr_strategy}_vpmatch_{split}.json", "w") as fv:
+            json.dump(new_mm, fv, indent=4)
+        with open(f"./retrieval/BDDX_RAG_{retr_strategy}_vidmatch_{split}.json", "w") as fvid:
+            json.dump(id_match, fvid, indent=4)
+
+
+
+        
\ No newline at end of file
diff --git a/retrieval/train.py b/retrieval/train.py
new file mode 100644
index 0000000..395861e
--- /dev/null
+++ b/retrieval/train.py
@@ -0,0 +1,195 @@
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+import torch.optim as optim
+import json 
+import os
+import numpy as np
+import random
+import argparse
+from tqdm import tqdm
+import re
+
+def extract_vehicle_signals(input_string):
+    # Patterns for matching speed, curvature, acceleration, and course data
+    speed_pattern = r"Speed: \[([0-9., -]+)\]"
+    curvature_pattern = r"Curvature: \[([0-9., -]+)\]"
+    acceleration_pattern = r"Acceleration: \[([0-9., -]+)\]"
+    course_pattern = r"Course: \[([0-9., -]+)\]"
+
+    # Function to extract and convert data to a list of floats
+    def extract_data(pattern):
+        match = re.search(pattern, input_string)
+        return [float(x) for x in match.group(1).split(',')] if match else []
+
+    # Extract data
+    speed = extract_data(speed_pattern)
+    curvature = extract_data(curvature_pattern)
+    acceleration = extract_data(acceleration_pattern)
+    course = extract_data(course_pattern)
+
+    return speed, curvature, acceleration, course
+
+def get_emb():
+    train_conv_path = "./video_process/final_conv_base/conversation_bddx_train.json"
+    test_conv_path = "./video_process/final_conv_base/conversation_bddx_eval.json"
+        
+    with open(train_conv_path,"r") as ftr:
+        train_conv = json.load(ftr)
+    with open(test_conv_path,"r") as fte:
+        test_conv = json.load(fte)
+    conv = train_conv + test_conv
+    # Load embeddings
+    vp_emb_match = {}
+    npz_file = np.load("./video_process/BDDX_Processed/new_emb/embeddings.npz")
+    for key in npz_file:
+        vp_emb_match[key] = npz_file[key]
+    npz_file.close()
+    
+    # Info
+    with open("./retrieval/bddx_vpath_info_match.json",'r') as im:
+        vp_info = json.load(im)
+    vp_signal_match = {vp['video']: vp_info[vp['video']] for vp in conv}
+    vp_pure_signal = {vp:extract_vehicle_signals(val) for vp, val in vp_signal_match.items()}
+    
+    # Concat
+    vp_f_emb = {}
+    for key in vp_emb_match.keys():
+        sig = np.concatenate([np.array(lst) for lst in vp_pure_signal[key]]).reshape(1,28)
+        # rep_sig = sig.repeat(1024, 1)
+        # # print(sig.shape, vp_emb_match[key].shape)
+        # val = np.concatenate((vp_emb_match[key], rep_sig), axis=1).reshape(1052,2048).astype(np.float32)
+        # # print(val.shape)
+        vp_f_emb.update({key:sig})
+    
+    return vp_f_emb
+
+class EmbeddingDataset(Dataset):
+    def __init__(self):
+        match_path = f"./retrieval/BDDX_RAG_pos_3.json"
+        with open(match_path,"r") as fi:
+            matches = json.load(fi)
+        
+        nmatch = f"./retrieval/BDDX_RAG_neg_3.json"      
+        with open(nmatch,"r") as fi:
+            neg_matches = json.load(fi)
+    
+            
+        self.sig_embeddings = get_emb()
+        self.pos_matches = matches  
+        self.neg_matches = neg_matches  
+        
+        self.vp_id = {key: index for index, key in enumerate(matches.keys())}
+        self.id_vp = {val: key for key,val in self.vp_id.items()}
+        
+        # print(self.id_vp[15466])
+
+    def __len__(self):
+        return len(self.sig_embeddings)
+    
+    def get_emb_from_vp(self,vp):
+        
+        ep = vp.split("/")[-1].replace('.mp4','.npy')
+        ep = os.path.join("./video_process/BDDX_Processed/new_emb2",ep)
+        emb = np.load(ep)
+        sig = self.sig_embeddings[vp]
+        rep_sig = sig.repeat(1024, 1).reshape(1,28,1024)
+        # print(emb.shape, rep_sig.shape)
+        val = np.concatenate((emb, rep_sig), axis=1).reshape(1024,2076).astype(np.float32)
+        
+        return val
+        
+        
+
+    def __getitem__(self, idx):
+        vp = self.id_vp[idx] #  Current query vp
+        
+        pos_lis = self.pos_matches[vp]
+        neg_lis = self.neg_matches[vp]
+        pos_vp = random.choice(pos_lis)
+        neg_vp = random.choice(neg_lis)
+
+        anchor, pos, neg = [self.get_emb_from_vp(v) for v in [vp, pos_vp, neg_vp]]
+
+        return (anchor, pos, neg)
+
+
+class MLP(nn.Module):
+    def __init__(self):
+        super(MLP, self).__init__()
+        self.layer_norm = nn.LayerNorm(1052)  # Normalize the entire concatenated input
+
+        self.layers = nn.Sequential(
+            nn.Linear(1052, 2048),
+            nn.GELU(),
+            nn.Linear(2048, 4096),
+            nn.GELU(),
+            nn.Linear(4096, 2048),
+            nn.GELU(),
+            nn.Linear(2048, 4096),
+            nn.GELU(),
+            nn.Linear(4096, 8192)  
+        )
+
+    def forward(self, x):
+        x = self.layer_norm(x)
+        return self.layers(x)
+
+def main(args):
+    # Create dataset and data loader
+    dataset = EmbeddingDataset()
+    data_loader = DataLoader(dataset, batch_size=128, shuffle=True)
+
+    # Initialize your model and loss function
+    model = MLP()
+    loss_fn = nn.TripletMarginLoss(margin=1.0, p=2)
+
+    # Initialize your optimizer
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+    # optimizer = optim.AdamW(model.parameters(), lr=0.001, weight_decay=1e-5)
+    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    loss_fn.to(device)
+
+
+    # Training loop
+    num_epochs = 2000
+    save_interval = 200
+    best_loss = float("inf")  # Initialize with a high value
+    for epoch in tqdm(range(num_epochs)):
+        total_loss = 0.0
+        num_batches = 0
+        for anchor, positive, negative in data_loader:
+            optimizer.zero_grad()  # Clear gradients
+            anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device)
+            anchor_embedding = model(anchor)
+            positive_embedding = model(positive)
+            negative_embedding = model(negative)
+            
+            
+
+            loss = loss_fn(anchor_embedding, positive_embedding, negative_embedding)
+
+            loss.backward()  # Compute gradients
+            optimizer.step()  # Update weights
+
+            total_loss += loss.item()
+            num_batches += 1
+
+        avg_loss = total_loss / num_batches
+        print(f"Epoch [{epoch+1}/{num_epochs}], Average Loss: {avg_loss:.4f}")
+        
+        # Save the model checkpoint if it has the lowest loss so far
+        if avg_loss < best_loss and epoch > 1000:
+            best_loss = avg_loss
+            torch.save(model.state_dict(), './retrieval/projector/best_model.pth')
+        
+        # Save the model checkpoint (remove this part if you don't need to save)
+        if (epoch + 1) % save_interval == 0:
+            torch.save(model.state_dict(), f'./retrieval/projector/model_epoch{epoch+1}.pth')
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Training script without DeepSpeed.")
+    args = parser.parse_args()
+    main(args)
diff --git a/retrieval/train_projector.sh b/retrieval/train_projector.sh
new file mode 100644
index 0000000..40b5e02
--- /dev/null
+++ b/retrieval/train_projector.sh
@@ -0,0 +1,5 @@
+python ./retrieval/train.py
+
+python ./retrieval/project.py
+
+python ./retrieval/final_match.py
diff --git a/retrieval/vpath_info_match.py b/retrieval/vpath_info_match.py
new file mode 100644
index 0000000..d3177f7
--- /dev/null
+++ b/retrieval/vpath_info_match.py
@@ -0,0 +1,55 @@
+import json 
+
+def format_conversation_as_qa(conversations):
+    """
+    Formats a list of conversation entries into a QA format.
+    
+    Parameters:
+    conversations (list): A list of conversation dictionaries with 'from' and 'value' keys.
+
+    Returns:
+    str: A formatted string representing the conversation in QA format.
+    """
+    formatted_conversation = ""
+    # print(conversations)
+    for i in range(0, len(conversations), 2):
+        # Check if the pair of conversation exists and matches the expected pattern
+        if i+1 < len(conversations) and conversations[i]["from"] == "human" and conversations[i+1]["from"] == "gpt":
+            question = conversations[i]["value"]
+            answer = conversations[i+1]["value"]
+            if "Predict" in question:
+                continue
+            formatted_conversation += f"Human: {question}\nAssistant: {answer}\n\n"
+
+    return formatted_conversation
+
+if __name__ == "__main__":
+    dataset = 'BDDX'
+
+    if dataset == 'BDDX':
+        train_conv_path = "./video_process/final_conv_base/conversation_bddx_train.json"
+        test_conv_path = "./video_process/final_conv_base/conversation_bddx_eval.json"
+    elif dataset == 'SAX':
+        train_conv_path = "./video_process/final_sax_conv_base/conversation_sax_train.json"
+        test_conv_path = "./video_process/final_sax_conv_base/conversation_sax_eval.json"
+        
+    with open(train_conv_path,"r") as ftr:
+        train_conv = json.load(ftr)
+    with open(test_conv_path,"r") as fte:
+        test_conv = json.load(fte)
+        
+    all_conv = train_conv + test_conv
+
+    vpath_info_match = {}
+    for item in all_conv:
+        vpath,conv = item['video'][0], item['conversations']
+        rconv = format_conversation_as_qa(conv)
+        vpath_info_match.update({vpath:rconv})
+        # break
+        
+    if dataset == 'BDDX':
+        with open("./retrieval/bddx_vpath_info_match.json","w") as fic:
+            json.dump(vpath_info_match,fic,indent=4)        
+    elif dataset == 'SAX':
+        with open("./retrieval/sax_vpath_info_match.json","w") as fic:
+            json.dump(vpath_info_match,fic,indent=4)
diff --git a/scripts/finetune.sh b/scripts/finetune.sh
new file mode 100644
index 0000000..b72f5a7
--- /dev/null
+++ b/scripts/finetune.sh
@@ -0,0 +1,46 @@
+# !/bin/
+#####################
+DATA_ROOT="video_process"
+HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=0 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 deepspeed llava/train/train_mem.py \
+    --deepspeed ./scripts/zero3_offload.json \
+    --output_dir ./checkpoints/Video-LLaVA-7B_RAGDRIVER \
+    --model_name_or_path ./checkpoints/Video-LLaVA-7B \
+    --version v1 \
+    --train_data_path video_process/final/conversation_bddx_train.json \
+    --eval_data_path video_process/final/conversation_bddx_eval.json \
+    --video_folder ${DATA_ROOT} \
+    --image_folder ${DATA_ROOT} \
+    --X "Video" "Image" \
+    --video_tower ./cache_dir/LanguageBind_Video_merge \
+    --image_tower ./cache_dir/LanguageBind_Image \
+    --pretrain_mm_mlp_adapter checkpoints/Video-LLaVA-Pretrain-7B/mm_projector.bin \
+    --mm_projector_type mlp2x_gelu \
+    --mm_vision_select_layer -2 \
+    --mm_use_x_start_end False \
+    --mm_use_x_patch_token False \
+    --image_aspect_ratio pad \
+    --group_by_modality_length False \
+    --bf16 True \
+    --num_train_epochs 2 \
+    --per_device_train_batch_size 4 \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 4 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 130 \
+    --save_total_limit 5 \
+    --learning_rate 2e-5 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 2048 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 4 \
+    --lazy_preprocess True \
+    --report_to tensorboard \
+    --cache_dir "./cache_dir"
+
+echo "Finished 1"
+
diff --git a/scripts/pretrain.sh b/scripts/pretrain.sh
new file mode 100644
index 0000000..b407b91
--- /dev/null
+++ b/scripts/pretrain.sh
@@ -0,0 +1,43 @@
+DATA_ROOT="video_process"
+CUDA_VISIBLE_DEVICES=0 deepspeed llava/train/train_mem.py \
+    --deepspeed ./scripts/zero3.json \
+    --model_name_or_path ./cache_dir/vicuna-7b-v1.5 \
+    --version v1 \
+    --train_data_path video_process/conversation_train.json \
+    --eval_data_path video_process/conversation_eval.json \
+    --video_folder ${DATA_ROOT} \
+    --image_folder ${DATA_ROOT} \
+    --X "Video" "Image" \
+    --video_tower ./cache_dir/LanguageBind_Video_merge \
+    --image_tower ./cache_dir/LanguageBind_Image \
+    --mm_projector_type mlp2x_gelu \
+    --tune_mm_mlp_adapter True \
+    --mm_vision_select_layer -2 \
+    --mm_use_x_start_end False \
+    --mm_use_x_patch_token False \
+    --group_by_modality_length True \
+    --bf16 True \
+    --output_dir ./checkpoints/Video-LLaVA-Pretrain-7B_drive \
+    --num_train_epochs 10 \
+    --per_device_train_batch_size 8 \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 2 \
+    --evaluation_strategy "steps" \
+    --save_strategy "steps" \
+    --save_steps 24000 \
+    --save_total_limit 1 \
+    --learning_rate 2e-5 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 2048 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 4 \
+    --lazy_preprocess True \
+    --report_to tensorboard \
+    --cache_dir "./cache_dir"
+
+
+        
\ No newline at end of file
diff --git a/scripts/zero2.json b/scripts/zero2.json
new file mode 100644
index 0000000..c95ebef
--- /dev/null
+++ b/scripts/zero2.json
@@ -0,0 +1,23 @@
+{
+    "fp16": {
+        "enabled": "auto",
+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 16,
+        "hysteresis": 2,
+        "min_loss_scale": 1
+    },
+    "bf16": {
+        "enabled": "auto"
+    },
+    "train_micro_batch_size_per_gpu": "auto",
+    "train_batch_size": "auto",
+    "gradient_accumulation_steps": "auto",
+    "zero_optimization": {
+        "stage": 2,
+        "overlap_comm": true,
+        "contiguous_gradients": true,
+        "sub_group_size": 1e9,
+        "reduce_bucket_size": "auto"
+    }
+}
\ No newline at end of file
diff --git a/scripts/zero3.json b/scripts/zero3.json
new file mode 100644
index 0000000..6917317
--- /dev/null
+++ b/scripts/zero3.json
@@ -0,0 +1,28 @@
+{
+    "fp16": {
+        "enabled": "auto",
+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 16,
+        "hysteresis": 2,
+        "min_loss_scale": 1
+    },
+    "bf16": {
+        "enabled": "auto"
+    },
+    "train_micro_batch_size_per_gpu": "auto",
+    "train_batch_size": "auto",
+    "gradient_accumulation_steps": "auto",
+    "zero_optimization": {
+        "stage": 3,
+        "overlap_comm": true,
+        "contiguous_gradients": true,
+        "sub_group_size": 1e9,
+        "reduce_bucket_size": "auto",
+        "stage3_prefetch_bucket_size": "auto",
+        "stage3_param_persistence_threshold": "auto",
+        "stage3_max_live_parameters": 1e9,
+        "stage3_max_reuse_distance": 1e9,
+        "stage3_gather_16bit_weights_on_model_save": true
+    }
+}
\ No newline at end of file
diff --git a/scripts/zero3_offload.json b/scripts/zero3_offload.json
new file mode 100644
index 0000000..e0a54c2
--- /dev/null
+++ b/scripts/zero3_offload.json
@@ -0,0 +1,56 @@
+{
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "initial_scale_power": 16,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "betas": "auto",
+      "eps": "auto",
+      "weight_decay": "auto"
+    }
+  },
+  "scheduler": {
+    "type": "WarmupLR",
+    "params": {
+      "warmup_min_lr": "auto",
+      "warmup_max_lr": "auto",
+      "warmup_num_steps": "auto"
+    }
+  },
+  "zero_optimization": {
+    "stage": 3,
+    "offload_optimizer": {
+      "device": "cpu",
+      "pin_memory": true
+    },
+    "offload_param": {
+      "device": "cpu",
+      "pin_memory": true
+    },
+    "overlap_comm": true,
+    "contiguous_gradients": true,
+    "sub_group_size": 1e9,
+    "reduce_bucket_size": "auto",
+    "stage3_prefetch_bucket_size": "auto",
+    "stage3_param_persistence_threshold": "auto",
+    "stage3_max_live_parameters": 1e9,
+    "stage3_max_reuse_distance": 1e9,
+    "gather_16bit_weights_on_model_save": true
+  },
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "steps_per_print": 1e5,
+  "wall_clock_breakdown": false
+}
\ No newline at end of file
diff --git a/video_process/create_bddx_json.py b/video_process/create_bddx_json.py
new file mode 100644
index 0000000..dfd5eab
--- /dev/null
+++ b/video_process/create_bddx_json.py
@@ -0,0 +1,170 @@
+import os
+import json
+import numpy as np
+from tqdm import tqdm 
+import random
+import math
+
+def radian_to_degree(radian):
+    return radian * (180 / math.pi)
+
+def uniform_sample(lst, num_samples):
+    sample = lst[::4]
+    sample = [round(s,2) for s in sample]
+    return sample
+
+def random_subset(sample_list):
+    """
+    Randomly selects a varying number of elements (1 to 5) from the provided list.
+
+    Parameters:
+    sample_list (list): The list from which to select elements.
+
+    Returns:
+    list: A list containing a random subset of the original list.
+    """
+    # Randomly choose how many elements to select (between 1 and 5)
+    # num_elements = random.randint(1, min(5, len(sample_list)))
+    num_elements = 2
+    # Randomly select the elements
+    return random.sample(sample_list, num_elements)
+
+def get_car_info(car_info, predict_cs=False):
+    speed = uniform_sample(car_info['speed'],8)
+    curvature = uniform_sample(car_info['curvature'],8)
+    # curvature = [radian_to_degree(ele) for ele in curvature]
+    # print(curvature)
+    acceleration = uniform_sample(car_info['acceleration'],8)
+    course = uniform_sample(car_info['course'],8)
+    
+    # Context
+    if predict_cs:
+        final_cs = f'Speed: {speed[-1]} Course: {course[-1]}'
+        speed, curvature, acceleration, course = speed[:-1], curvature[:-1], acceleration[:-1], course[:-1]    
+        # final_cs = [speed[-1], curvature[-1], acceleration[-1], course[-1]]
+    else:
+        final_cs = None
+        
+    context = f"The current video records driving scenario: <video>\n Control Signal until current Frame Sequence is: Speed: {speed}\n Curvature: {curvature}\n Acceleration: {acceleration}\n Course: {course}"
+    return context, final_cs
+    
+def main(split):
+    if split == "train":
+        info_root = "./BDDX_Processed/info"
+    elif split == "test":
+        info_root = "./BDDX_Test/info"
+        
+    question_1 = '''What is the action of ego car?'''
+    question_2 = '''Why does the ego car doing this?'''
+    question_3 = '''Predict the control signal for next frame.'''
+    control_signal = True
+    # RAG ICL
+    rag = True
+    strategy = 'hybird'
+    if split == 'train':
+        match_path = f"../retrieval/BDDX_RAG_{strategy}_vidmatch_train.json"
+        id_info_path = "../retrieval/bddx_id_info_match.json"
+    elif split == 'test':
+        match_path = f"../retrieval/BDDX_RAG_{strategy}_vidmatch_test.json"
+        id_info_path = "../retrieval/bddx_id_info_match.json"
+        
+    if rag:
+        with open(match_path,"r") as fm:
+            match_dict = json.load(fm)
+        with open(id_info_path,"r") as fi:
+            id_info_match = json.load(fi)
+    # Add eval
+    add_eval = False
+    if add_eval and split == "train":
+        info_root = "./BDDX_ALL/info"
+        
+    predict_cs = True
+    
+    new_info_lis = []
+    for root, dirs, files in os.walk(info_root):
+        for f in tqdm(files):
+            info_path = os.path.join(root,f)
+            with open(info_path,"r") as inf:
+                info = json.load(inf)
+                idx, video_path, conversation, car_info = info['id'], info['video'], info['comment'], info['car_info']
+                # print(conversation)
+                action, justification = conversation[0]['action'], conversation[1]['justification']
+                # print(car_info)
+                if control_signal and rag:
+                    # RAG 
+                    try:
+                        matched_ids = match_dict[idx]
+                    except:
+                        matched_ids = None
+                        
+                    if matched_ids is not None:
+                        matched_samples = [id_info_match[sid] for sid in matched_ids]
+                        # matched_samples = random_subset(matched_samples)
+                        icl_context = "Here are also some historical driving experience for your reference:"
+                        for icl_id, sample in enumerate(matched_samples):
+                            # sample = sample.replace("<video>","")
+                            icl_context += f"{icl_id}:{sample}\n"
+                        icl_context += "*" * 60
+                        
+                    else:
+                        icl_context = "Here are also some historical driving experience for your reference: No Past Experience"
+                        icl_context += "*" * 60
+
+                    # Context
+                    context,final_cs = get_car_info(car_info,predict_cs)
+                    
+                    if predict_cs:
+                        new_conversation = [    
+                            # {'from':'human','value':f'{context}\n{icl_context}\n{question_1}'},
+                            {'from':'human','value':f'{icl_context}\n{context}\n{question_1}'},
+                            {'from':'gpt','value':f'{action}'},
+                            {'from':'human','value':f'{question_2}'},
+                            {'from':'gpt','value':f'{justification}'},
+                            {'from':'human','value':f'{question_3}'},
+                            {'from':'gpt','value':f'{final_cs}'},
+                        ]                        
+                    else:
+                        new_conversation = [    
+                            # {'from':'human','value':f'{context}\n{icl_context}\n{question_1}'},
+                            {'from':'human','value':f'{icl_context}\n{context}\n{question_1}'},
+                            {'from':'gpt','value':f'{action}'},
+                            {'from':'human','value':f'{question_2}'},
+                            {'from':'gpt','value':f'{justification}'}
+                        ]
+                    
+                
+                else:
+                    new_conversation = [
+                        {'from':'human','value':f'{question_1}'},
+                        {'from':'gpt','value':f'{action}'},
+                        {'from':'human','value':f'{question_2}'},
+                        {'from':'gpt','value':f'{justification}'}
+                    ]
+                video_path = video_path.split("/",5)[-1]
+                info_dict = {
+                    "id": f"{idx}",
+                    "video": f"{video_path}",
+                    "conversations": new_conversation
+                }
+                new_info_lis.append(info_dict)
+    
+    
+    sp = "train" if split == "train" else "eval"
+    out_dir ="./final_conv_hybrid"
+    os.makedirs(out_dir,exist_ok=True)
+    out_path = f'{out_dir}/conversation_bddx_{sp}.json'
+    
+    
+    with open (f'{out_dir}/conversation_bddx_eval.json','r') as ft:
+        de = json.load(ft)
+    des = random.sample(de, 256)
+    new_info_lis += des
+    
+    with open(out_path, 'w') as f:
+        json.dump(new_info_lis, f)
+        
+
+if __name__ == "__main__":
+    main("test")
+    main("train")
+    
\ No newline at end of file