CarND-Capstone-Solution

Solution to Term 3, Project 3 of Udacity Nanodegree: Capstone Project

Team Silicon Forest Cruiser

Members

• Marcus Sinhalage ([email protected])
• Yury Kirpichev ([email protected])
• Duksan Ryu ([email protected])
• Steven Thomas ([email protected])
• Robert Ioffe ([email protected])

Team Lead

Robert Ioffe ([email protected])

Develop

Please see Contributing for detailed information.

This is the project repo for the final project of the Udacity Self-Driving Car Nanodegree: Programming a Real Self-Driving Car. For more information about the project, see the project introduction here.

Controllers

We use PID controller with low pass filtering over current linear velocity and stop accelearation for acceleration/breaking and a Udacity provided YawController for steering. We experimented with the PID controller with low pass filtering for steering as well, but found YawController to perform better.

Traffic Light Detection and Recognition Models

We developed several traffic light detection and recognition models. Detailed information about our models. We use three different models simultaneously to determine the color of the traffic lights (we use separate models for the simulation environment and separate models for the real environment - one of our models was trained on both real and simulated images and is uses for detection in both simulated and real environments).

Final Project Video

Please use one of the two installation options, either native or docker installation.

Native Installation

• Be sure that your workstation is running Ubuntu 16.04 Xenial Xerus or Ubuntu 14.04 Trusty Tahir. Ubuntu downloads can be found here.

• If using a Virtual Machine to install Ubuntu, use the following configuration as minimum:

  • 2 CPU
  • 2 GB system memory
  • 25 GB of free hard drive space

  The Udacity provided virtual machine has ROS and Dataspeed DBW already installed, so you can skip the next two steps if you are using this.

• Follow these instructions to install ROS

  • ROS Kinetic if you have Ubuntu 16.04.
  • ROS Indigo if you have Ubuntu 14.04.

• Dataspeed DBW

  • Use this option to install the SDK on a workstation that already has ROS installed: One Line SDK Install (binary)

• Download the Udacity Simulator.

Docker Installation

Install Docker

Build the docker container

docker build . -t capstone

Run the docker file

docker run -p 4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone

Port Forwarding

To set up port forwarding, please refer to the instructions from term 2

Usage

1. Clone the project repository

git clone https://github.com/udacity/CarND-Capstone.git

2. Install python dependencies

cd CarND-Capstone
pip install -r requirements.txt

3. Make and run styx

./model_extraction.sh
cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch

4. Run the simulator

Real world testing

1. Download training bag that was recorded on the Udacity self-driving car.
2. Unzip the file

unzip traffic_light_bag_file.zip

3. Play the bag file

rosbag play -l traffic_light_bag_file/traffic_light_training.bag

4. Launch your project in site mode

cd CarND-Capstone/ros
roslaunch launch/site.launch

5. Confirm that traffic light detection works on real life images