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 this https URL
Abstract (translated)
一个理想的交通模拟器能够复制自主驾驶系统在部署期间经历的真实长期点到点的旅程。之前的模型和基准测试主要集中在场景中初始代理的闭环运动仿真上，这不利于长期仿真的实施。随着主车辆进入新的区域，代理会不断进出场景，这就造成了问题。 我们提出了一种名为InfGen的统一模型，它可以执行交错进行的闭环运动模拟与场景生成任务。InfGen能够自动在闭环运动模拟和场景生成模式之间切换，从而实现稳定的长时间仿真流程。该模型在短期（9秒）交通模拟方面达到了当前最佳水平，并且在长期（30秒）模拟中显著超越了所有其他方法。 InfGen的代码和模型将在以下链接发布：[请参见原文链接以获取正确的URL]
URL
https://arxiv.org/abs/2506.17213
