Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving

ArXi:2603.20232v1 Announce Type: cross Autonomous driving testing increasingly relies on mining safety critical scenarios from large scale naturalistic driving data, yet existing screening pipelines still depend on manual risk annotation and expensive frame by frame risk evaluation, resulting in low efficiency and weakly grounded risk quantification. To address this issue, we propose a driver risk fusion based hazardous scenario screening method for autonomous driving. During