TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
Zhejun Zhang,Alexander Liniger,Dengxin Dai,Fisher Yu,Luc Van Gool,Zhejun Zhang,Alexander Liniger,Dengxin Dai,Fisher Yu,Luc Van Gool
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 buil...
