  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/.
