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 at https://orangesodahub.github.io/InfGen
@article{arxiv.2506.17213,
title = {Long-term Traffic Simulation with Interleaved Autoregressive Motion and Scenario Generation},
author = {Xiuyu Yang and Shuhan Tan and Philipp Krähenbühl},
journal= {arXiv preprint arXiv:2506.17213},
year = {2025}
}