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TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction. ICRA 2023. Code is now available at https://github.com/zhejz/TrafficBots

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TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction

We are preparing the code release. Please check back later!

This is the official code release of the paper
TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
by Zhejun Zhang, Alexander Liniger, Dengxin Dai, Fisher Yu and Luc van Gool, accepted at ICRA 2023.

Abstract

Data-driven simulation has become a favorable way to train and test autonomous driving algorithms. The idea of replacing the actual environment with a learned simulator has also been explored in model-based reinforcement learning in the context of world models. In this work, we show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving, and based on TrafficBots we obtain a world model tailored for the planning module of autonomous vehicles. Existing data-driven traffic simulators are lacking configurability and scalability. To generate configurable behaviors, for each agent we introduce a destination as navigational information, and a time-invariant latent personality that specifies the behavioral style. To improve the scalability, we present a new scheme of positional encoding for angles, allowing all agents to share the same vectorized context and the use of an architecture based on dot-product attention. As a result, we can simulate all traffic participants seen in dense urban scenarios. Experiments on the Waymo open motion dataset show TrafficBots can simulate realistic multi-agent behaviors and achieve good performance on the motion prediction task.

Citation

Please cite our work if you found it useful:

@inproceedings{zhang2023trafficbots,
  title = {{TrafficBots}: Towards World Models for Autonomous Driving Simulation and Motion Prediction},
  booktitle = {International Conference on Robotics and Automation (ICRA)},
  author = {Zhang, Zhejun and Liniger, Alexander and Dai, Dengxin and Yu, Fisher and Van Gool, Luc},
  year = {2023},
}

License

This software is released under a CC-BY-NC 4.0 license, which allows personal and research use only. For a commercial license, please contact the authors. You can view a license summary here.

Portions of source code taken from external sources are annotated with links to original files and their corresponding licenses.

Acknowledgements

This work was supported by Toyota Motor Europe and was carried out at the TRACE Lab at ETH Zurich (Toyota Research on Automated Cars in Europe - Zurich).

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TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction. ICRA 2023. Code is now available at https://github.com/zhejz/TrafficBots

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