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

An ideal traffic simulator replicates the realistic long-term point-to-point trip that a self-driving system experiences during deployment.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.We 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.The code and model of InfGen will be released upon acceptance.