基于提议条件的闭环交通场景生成潜扩散模型
Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation
AI总结：
提出一种基于实例场景上下文和多模态提议先验的扩散框架，通过紧凑动作潜表示和提议初始化提升采样效率，实现闭环交通场景的逼真、安全且可控生成。
摘要：
闭环交通模拟仍然具有挑战性，因为它必须生成在整个展开过程中场景一致且可控的交互式多智能体行为。先前的基于扩散的方法实现了强逼真度，但其计算成本可能阻碍其在自动驾驶车辆规划和模拟的时间受限重规划循环中的部署。我们提出了一种基于扩散的场景生成框架，该框架以实例为中心的场景上下文和多模态提议先验为条件，并可选地通过测试时引导来塑造安全关键行为。紧凑的动作潜表示和基于提议的初始化提高了采样效率，并减少了每步运行时间，而无需重新训练。在Waymo开放运动数据集上的实验表明，该方法在多样化的交互场景中实现了逼真度、安全性和可控性之间的良好平衡，同时表明测试时引导能够实现竞争目标之间的系统性权衡。
英文摘要：
Closed-loop traffic simulation remains challenging because it must generate interactive multi-agent behaviors that are scene-consistent and controllable throughout rollout. Prior diffusion-based approaches achieve strong realism, but their computational cost can hinder deployment in time-constrained replanning loops for autonomous vehicle planning and simulation. We present a diffusion-based scenario generation framework conditioned on instance-centric scene context and multimodal proposal priors, with optional test-time guidance for shaping safety-critical behaviors. A compact action-latent representation and proposal-based initialization improve sampling efficiency and reduce per-step runtime without retraining. Experiments on the Waymo Open Motion Dataset demonstrate a favorable balance among realism, safety, and controllability across diverse interactive scenarios, while showing that test-time guidance enables systematic trade-offs among competing objectives.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
