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.