Evaluating the performance of autonomous vehicle planning algorithms necessitates simulating long-tail safety-critical traffic scenarios. However, traditional methods for generating such scenarios often fall short in terms of controllability and realism; they also neglect the dynamics of agent interactions. To address these limitations, we introduce SAFE-SIM, a novel diffusion-based controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We develop a novel approach to simulate safety-critical scenarios through an adversarial term in the denoising process of diffusion models, which allows an adversarial agent to challenge a planner with plausible maneuvers while all agents in the scene exhibit reactive and realistic behaviors. Furthermore, we propose novel guidance objectives and a partial diffusion process that enables users to control key aspects of the scenarios, such as the collision type and aggressiveness of the adversarial agent, while maintaining the realism of the behavior. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability. These findings affirm that diffusion models provide a robust and versatile foundation for safety-critical, interactive traffic simulation, extending their utility across the broader autonomous driving landscape. Project website: https://safe-sim.github.io/.

翻译：评估自动驾驶规划算法的性能需要模拟长尾临界安全交通场景。然而，生成此类场景的传统方法通常在可控性和真实性方面存在不足，且忽略了智能体间的交互动态。为应对这些局限，我们提出了SAFE-SIM——一种基于扩散模型的新型可控闭环临界安全仿真框架。该方法具有两大优势：1）生成高度贴近现实世界条件的真实长尾临界安全场景；2）提供可控的对抗行为以实现更全面、更具交互性的评估。我们开发了一种通过在扩散模型去噪过程中引入对抗项来生成临界安全场景的新方法，使得对抗智能体能够以合理的机动策略挑战规划器，同时场景中所有智能体均表现出反应式且真实的行为。此外，我们提出了新的引导目标函数和部分扩散过程，使用户能够控制场景的关键要素（如碰撞类型和对抗智能体的攻击性），同时保持行为的真实性。我们在nuScenes和nuPlan数据集上通过多规划器实验验证了本框架，结果证明了其在真实性与可控性方面的提升。这些发现证实了扩散模型能为临界安全交互式交通仿真提供稳健且通用的基础，从而拓展其在更广泛自动驾驶领域的应用价值。项目网站：https://safe-sim.github.io/。