提出分步闭环仿真方法，提升自动驾驶交通模拟的现实感与交互真实性。
ForSim: Stepwise Forward Simulation for Traffic Policy Fine-Tuning
- 分步推进仿真，基于物理动力学选择最优轨迹，保持行为多样性。
- 集成到RIFT框架后，安全性能显著提升，效率与真实感兼顾。
- 适合自动驾驶策略优化、高保真交通仿真研究者使用。
作为自动驾驶闭环训练与评估的基础，交通仿真仍面临两大挑战：开环模仿学习引入的协变量偏移，以及难以反映真实交通中多模态行为的局限性。尽管近期框架如RIFT通过群体相对优化部分解决这些问题，其前向仿真过程仍普遍缺乏反应性，导致虚拟环境中代理间互动不真实，最终限制仿真保真度。为此，我们提出ForSim，一种分步闭环前向仿真范式。在每个虚拟时间步，交通代理通过物理驱动的动力学，传播最符合参考轨迹的候选轨迹，从而在保持多模态行为多样性的同时确保模态内一致性。其他代理则通过分步预测更新，实现连贯且具备交互感知的演化。当整合至RIFT交通仿真框架时，ForSim与群体相对优化协同工作，用于精细调整交通策略。大量实验表明，该集成能持续提升安全性，同时维持高效性、真实性和舒适性。结果凸显了在前向仿真中建模闭环多模态交互的重要性，增强了自动驾驶交通仿真的保真度与可靠性。
原文摘要 · Abstract (English)
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/
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