Important
The data model and specification are not finalized and are still under discussion. See this repository as a proof of concept.
Data Model, Format and Python Library for ground truth data containing information on dynamic objects, map and environmental factors optimized for representing urban traffic. The data model and format heavily utilze ASAM OpenDRIVE and ASAM Open-Simulation-Interface GroundTruth messages. omega-prime sets requirements on presence and quality of ASAM OSI GroundTruth messages and ASAM OpenDRIVE files and defines a file format the exchange and storage of such data.
Omega-Prime is the successor of the OMEGAFormat. It has the benefit that its definition is directly based on the established standards ASAM OSI and ASAM OpenDRIVE and carries over the data quality requirements and the data tooling from OMEGAFormat. Therefore, it should be easier to incorporate omega-prime into existing workflows and tooling.
To learn more about the example data read example_files/README.md. Example data was taken and created from esmini.
- Creation of omega-prime files from
- ASAM OSI GroundTruth trace (e.g., output of esmini)
- Table of moving object data (e.g., csv data)
- ASAM OpenDRIVE map
- Plotting
- Validation
- Interpolation
- CLI and python functions to access features
For a detailed introduction look at tutorial.ipynb.
pip install omega-prime
A detailed introduction to the features and usage can be found in tutorial.ipynb
Create an omega-prime file from an OSI GroundTruth message trace and an OpenDRIVE map:
import omega_prime
r = omega_prime.Recording.from_file('example_files/pedestrian.osi', xodr_path='example_files/fabriksgatan.xodr')
r.to_mcap('example.mcap')Read and plot an omega-prime file:
r = omega_prime.Recording.from_file('example.mcap')
ax = r.plot()Based on MCAP, ASAM OSI and ASAM OpenDRIVE the ASAM OSI GroundTruth messages and ASAM OpenDRIVE map are packaged as shown in the following figure.
