{"id":13440760,"url":"https://github.com/SysCV/TrafficBots","last_synced_at":"2025-03-20T10:32:21.363Z","repository":{"id":165530305,"uuid":"605460107","full_name":"SysCV/TrafficBots","owner":"SysCV","description":"TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction. ICRA 2023. 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Please check back later!\n\n\u003cp align=\"center\"\u003e\n  \u003cimg width=\"80%\" src=\"docs/trafficbots_demo.png\"\u003e\n\u003c/p\u003e\n\nThis is the official code release of the paper \u003cbr\u003e \n**[TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction](https://arxiv.org/abs/2303.04116)** \u003cbr\u003e\n*by [Zhejun Zhang](https://zhejz.github.io/), [Alexander Liniger](https://alexliniger.github.io/), [Dengxin Dai](https://www.trace.ethz.ch/team/members/dengxin.html), [Fisher Yu](https://www.yf.io/) and [Luc van Gool](https://www.trace.ethz.ch/team/members/luc.html)*, accepted at [ICRA 2023](https://www.icra2023.org/). \u003cbr\u003e\n\n## Abstract\nData-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.\n\n## Citation\nPlease cite our work if you found it useful:\n```\n@inproceedings{zhang2023trafficbots,\n  title = {{TrafficBots}: Towards World Models for Autonomous Driving Simulation and Motion Prediction},\n  booktitle = {International Conference on Robotics and Automation (ICRA)},\n  author = {Zhang, Zhejun and Liniger, Alexander and Dai, Dengxin and Yu, Fisher and Van Gool, Luc},\n  year = {2023},\n}\n```\n\n## License\nThis software is released under a CC-BY-NC 4.0 license, which allows personal and research use only. For a commercial \nlicense, please contact the authors. You can view a license summary here.\n\nPortions of source code taken from external sources are annotated with links to original files and their corresponding \nlicenses.\n\n## Acknowledgements\nThis work was supported by Toyota Motor Europe and was carried out at the TRACE Lab at ETH Zurich (Toyota Research on \nAutomated Cars in Europe - Zurich).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSysCV%2FTrafficBots","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FSysCV%2FTrafficBots","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSysCV%2FTrafficBots/lists"}