TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
Open access
Date
2023Type
- Conference Paper
ETH Bibliography
yes
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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 nav-igational 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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000643397Publication status
publishedExternal links
Book title
2023 IEEE International Conference on Robotics and Automation (ICRA)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
03514 - Van Gool, Luc / Van Gool, Luc
09688 - Yu, Fisher / Yu, Fisher
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ETH Bibliography
yes
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