Long-term Traffic Simulation with Interleaved Autoregressive Motion and Scenario Generation

Yang, Xiuyu, Tan, Shuhan, Krähenbühl, Philipp

arXiv.org Artificial Intelligence 

Prior models and benchmarks focus on closed-loop motion simulation for initial agents in a scene. This is problematic for long-term simulation. Agents enter and exit the scene as the ego vehicle enters new regions. W e propose InfGen, a unified next-token prediction model that performs interleaved closed-loop motion simulation and scene generation. InfGen automatically switches between closed-loop motion simulation and scene generation mode. It enables stable long-term rollout simulation. InfGen performs at the state-of-the-art in short-term (9s) traffic simulation, and significantly outperforms all other methods in long-term (30s) simulation.