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 and neglect the dynamics of agent interactions. To mitigate these limitations, we introduce SAFE-SIM, a novel diffusion-based controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) the generation of realistic long-tail safety-critical scenarios that closely emulate real-world conditions, and 2) enhanced controllability, enabling more comprehensive and interactive evaluations. We develop a novel approach to simulate safety-critical scenarios through an adversarial term in the denoising process, 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 a user to control key aspects of the generated scenarios, such as the collision type and aggressiveness of the adversarial driver, while maintaining the realism of the behavior. We validate our framework empirically using the NuScenes dataset, 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 landscape of autonomous driving. For supplementary videos, visit our project at https://safe-sim.github.io/.

翻译：评估自动驾驶车辆规划算法的性能需要模拟长尾安全关键交通场景。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，并且忽略了智能体交互的动态性。为了缓解这些局限性，我们提出了SAFE-SIM，一种新颖的基于扩散的可控闭环安全关键仿真框架。我们的方法具有两个显著优势：1）生成能够紧密模拟真实世界条件的、真实的长尾安全关键场景；2）增强的可控性，支持更全面和交互式的评估。我们开发了一种新颖的方法，通过在去噪过程中引入一个对抗项来模拟安全关键场景，这使得一个对抗智能体能够以合理的机动策略挑战规划器，同时场景中的所有智能体表现出反应性和真实的行为。此外，我们提出了新颖的引导目标和一个部分扩散过程，使用户能够控制生成场景的关键方面，例如碰撞类型和对抗驾驶员的攻击性，同时保持行为的真实性。我们使用NuScenes数据集对我们的框架进行了实证验证，证明了其在真实性和可控性方面的改进。这些发现证实了扩散模型为安全关键、交互式的交通仿真提供了一个稳健且多功能的基础，从而扩展了其在更广泛的自动驾驶领域的应用价值。补充视频请访问我们的项目网站：https://safe-sim.github.io/。