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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.
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.
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},
}
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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).