Public articles linked to the same research event.
arXiv 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.
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.