tactics2d 0.1.0

A traffic environment simulator for driving policy development.

Tactics2D: A Reinforcement Learning Environment Library for Driving Decision-making

About

tactics2d is an open-source Python library that provides diverse and challenging traffic scenarios for the development and evaluation of reinforcement learning-based decision-making models in autonomous driving. tactics2d stands out with the following key features:

• Compatibility
  • 📦 Trajectory dataset -- Enables seamless importation of various real-world trajectory datasets, including Argoverse, Dragon Lake Parking (DLP), INTERACTION, LevelX Series (highD, inD, rounD, ExiD), NuPlan, and Waymo Open Motion Dataset (WOMD), encompassing both trajectory parsing and map information.
  • 📄 Map format -- Enables parsing and conversion of commonly used open map formats like OpenDRIVE, Lanelet2-style OpenStreetMap (OSM), and SUMO roadnet.
• Customizability
  • 🚗 Traffic participants -- Supports the creation of new traffic participant classes with customizable physical attributes, physics dynamics/kinematics models, and behavior models.
  • 🚧 Road elements -- Support the definition of new road elements, with a focus on regulatory aspects.
• Diversity
  • 🛣️ Traffic scenarios -- Features an extensive range of built-in Gym-style traffic scenarios, including highway, lane-merging, unsignalized/signalized intersection, roundabout, parking, and racing.
  • 🚲 Traffic participants -- Features a variety of built-in traffic participants with realistic physics parameters, detailed further here.
  • 📷 Sensors -- Provides bird-eye-view (BEV) semantic segmentation RGB image and single-line LiDAR point cloud for model input.
• Visualization -- Offers a user-friendly visualization tool for real-time rendering of traffic scenarios and participants, along with the capability to record and replay traffic scenarios.
• Reliability -- Over 85% code is covered by unit tests and integration tests.

For further information on tactics2d, please refer to our comprehensive documentation, and a detailed comparison with other similar libraries is available here.

Community

We have a Discord Community for support. Feel free to ask questions. Posting in Github Issues and PRs are also welcome.

Installation

0. System Requirements

We have conducted testing for the execution and construction of tactics2d on the following platforms:

System	3.8	3.9	3.10	3.11
Ubuntu 18.04	:white_check_mark:	-	-	-
Ubuntu 20.04	:white_check_mark:	:white_check_mark:	:white_check_mark:	:white_check_mark:
Ubuntu 22.04	:white_check_mark:	:white_check_mark:	:white_check_mark:	:white_check_mark:
Windows	:white_check_mark:	:white_check_mark:	:white_check_mark:	:white_check_mark:
MacOS	:white_check_mark:	:white_check_mark:	:white_check_mark:	:white_check_mark:

1.1 Install from PyPI

You can simply install tactics2d from PyPI with the following command.

conda create -n tactics2d python=3.8
pip install tactics2d

1.2 Install from Github

You can also install tactics2d from from its source on GitHub. This way is recommended if you want to run the sample code or contribute to the development of tactics2d.

# clone the repository with submodules but ignore the large files (mainly the NuPlan's map data)
# please download NuPlan's map data from its official website and put it in the `tactics2d/data/map/NuPlan` directory
git clone --recurse-submodules --filter=blob:limit=100m [e-mail]:WoodOxen/tactics2d.git
cd tactics2d

conda create -n tactics2d python=3.8
[TODO] # installation command
[TODO] # test the installation

If no errors occurs, you should have installed tactics2d successfully.

2. Dataset Preparation

According to the licenses of the trajectory datasets, we cannot distribute the original datasets with tactics2d. You need to download the datasets from their official websites. Currently, tactics2d supports the following datasets:

• Argoverse 2
• Dragon Lake Parking (DLP)
• HighD
• inD
• rounD
• ExiD
• INTERACTION
• NuPlan
• Waymo Open Motion Dataset v1.2 (WOMD)

Citation

If you find tactics2d useful, please cite this in your publication.

@article{li2023tactics2d,
  title={Tactics2D: A Multi-agent Reinforcement Learning Environment for Driving Decision-making},
  author={Li, Yueyuan and Zhang, Songan and Jiang, Mingyang and Chen, Xingyuan and Yang, Ming},
  journal={arXiv preprint arXiv:2311.11058},
  year={2023}
}