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

• URL: http://arxiv.org/abs/2303.04116v2
• Date: Thu, 28 Sep 2023 14:50:40 GMT
• Title: TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
• Authors: Zhejun Zhang, Alexander Liniger, Dengxin Dai, Fisher Yu, Luc Van Gool
• Abstract summary: 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. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
• Score: 149.5716746789134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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