Controllable Safety-Critical Closed-loop Traffic Simulation via Guided
  Diffusion
Wei-Jer Chang Francesco Pittaluga Masayoshi Tomizuka Wei Zhan Manmohan Chandraker
Evaluating the performance of autonomous vehicle planning algorithms
necessitates simulating long-tail traffic scenarios. Traditional methods for
generating safety-critical scenarios often fall short in realism and
controllability. Furthermore, these techniques generally neglect the dynamics
of agent interactions. To mitigate these limitations, we introduce a novel
closed-loop simulation framework rooted in guided diffusion models. Our
approach yields two distinct advantages: 1) the generation of realistic
long-tail scenarios that closely emulate real-world conditions, and 2) enhanced
controllability, enabling more comprehensive and interactive evaluations. We
achieve this through novel guidance objectives that enhance road progress while
lowering collision and off-road rates. We develop a novel approach to simulate
safety-critical scenarios through an adversarial term in the denoising process,
which allows the adversarial agent to challenge a planner with plausible
maneuvers, while all agents in the scene exhibit reactive and realistic
behaviors. We validate our framework empirically using the NuScenes dataset,
demonstrating improvements in both realism and controllability. These findings
affirm that guided 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 additional resources
and demonstrations, visit our project page at https://safe-sim.github.io.