ForSim: Stepwise Forward Simulation for Traffic Policy Fine-Tuning

TL;DR

ForSim introduces a stepwise closed-loop traffic simulation method that enhances realism and safety in autonomous driving policy fine-tuning by modeling multimodal interactions more accurately.

Contribution

It proposes a novel stepwise forward simulation paradigm that improves traffic simulation fidelity by better modeling multimodal behaviors and interactions within a closed-loop framework.

Findings

Improves safety in traffic policy fine-tuning.

Maintains realism, efficiency, and comfort in simulations.

Enhances the fidelity and reliability of traffic simulation.

Abstract

As the foundation of closed-loop training and evaluation in autonomous driving, traffic simulation still faces two fundamental challenges: covariate shift introduced by open-loop imitation learning and limited capacity to reflect the multimodal behaviors observed in real-world traffic. Although recent frameworks such as RIFT have partially addressed these issues through group-relative optimization, their forward simulation procedures remain largely non-reactive, leading to unrealistic agent interactions within the virtual domain and ultimately limiting simulation fidelity. To address these issues, we propose ForSim, a stepwise closed-loop forward simulation paradigm. At each virtual timestep, the traffic agent propagates the virtual candidate trajectory that best spatiotemporally matches the reference trajectory through physically grounded motion dynamics, thereby preserving multimodal behavioral diversity while ensuring intra-modality consistency. Other agents are updated with stepwise predictions, yielding coherent and interaction-aware evolution. When incorporated into the RIFT traffic simulation framework, ForSim operates in conjunction with group-relative optimization to fine-tune traffic policy. Extensive experiments confirm that this integration consistently improves safety while maintaining efficiency, realism, and comfort. These results underscore the importance of modeling closed-loop multimodal interactions within forward simulation and enhance the fidelity and reliability of traffic simulation for autonomous driving. Project Page: https://currychen77.github.io/ForSim/