Public articles linked to the same research event.
arXiv 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 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.