# Autonomous Vehicle Safety Evaluation

The study defines a generated scenario's adversity as its percentile in the conditional distribution of future risk given the observed history, learns a reference risk distribution mapping each requested percentile to a physical risk target, and uses a percentile-conditioned joint diffusion model with sampling-time risk guidance to generate multi-agent futures; on highD with minimum ego-surrounding-vehicle post-encroachment time (PET) as the surrogate, the method realizes 1,422 of 1,440 requests (98.75%) within a 0.05 percentile tolerance, with mean percentile error 0.00673 and PET-target error 0.00991 seconds.

The study defines a generated scenario's adversity as its percentile in the conditional distribution of future risk given the observed history, learns a reference risk distribution mapping each requested percentile to a physical risk target, and uses a percentile-conditioned joint diffusion model with sampling-time risk guidance to generate multi-agent futures; on highD with minimum ego-surrounding-vehicle post-encroachment time (PET) as the surrogate, the method realizes 1,422 of 1,440 requests (98.75%) within a 0.05 percentile tolerance, with mean percentile error 0.00673 and PET-target error 0.00991 seconds.

The authors present CrashSim, a human behavior-informed crash scenario generation framework that uses a vision-language model to interpret naturalistic driving scenes, hierarchically retrieves real-world crash priors, and converts their relative-motion patterns and behavior-stage timings into anchor guidance for a diffusion generator pretrained on nuScenes; results show CrashSim reproduces real-world pre-impact behavior, collision dynamics, collision geometry and crash-type distributions more closely than Strive and LD-scene, and it is used to build nuCrash, a dataset of over 4,000 crash and near-crash scenarios on which five planners show larger closed-loop performance differences than on nuScenes, with an LLM evaluation agent providing capability-level diagnoses and improvement guidance.

The authors present CrashSim, a human behavior-informed crash scenario generation framework that uses a vision-language model to interpret naturalistic driving scenes, hierarchically retrieves real-world crash priors, and converts their relative-motion patterns and behavior-stage timings into anchor guidance for a diffusion generator pretrained on nuScenes; results show CrashSim reproduces real-world pre-impact behavior, collision dynamics, collision geometry and crash-type distributions more closely than Strive and LD-scene, and it is used to build nuCrash, a dataset of over 4,000 crash and near-crash scenarios on which five planners show larger closed-loop performance differences than on nuScenes, with an LLM evaluation agent providing capability-level diagnoses and improvement guidance.