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

• URL: http://arxiv.org/abs/2506.17213v1
• Date: Fri, 20 Jun 2025 17:59:21 GMT
• Title: Long-term Traffic Simulation with Interleaved Autoregressive Motion and Scenario Generation
• Authors: Xiuyu Yang, Shuhan Tan, Philipp Krähenbühl,
• Abstract summary: An ideal traffic simulator replicates the realistic long-term point-to-point trip that a self-driving system experiences during deployment.<n>We propose InfGen, a unified next-token prediction model that performs interleaved closed-loop motion simulation and scene generation.<n>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.
• Score: 26.98977914185036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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