[2603.20232] Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving

Computer Science > Robotics arXiv:2603.20232 (cs) [Submitted on 7 Mar 2026] Title:Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving Authors:Chen Xiong, Ziwen Wang, Deqi Wang, Cheng Wang, Yiyang Chen, He Zhang, Chao Gou View a PDF of the paper titled Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving, by Chen Xiong and 6 other authors View PDF HTML (experimental) Abstract: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 training, the method combines an improved Driver Risk Field with a dynamic cost model to generate high quality risk supervision signals, while during inference it directly predicts scenario level risk scores through fast forward passes, avoiding per frame risk computation and enabling efficient large scale ranking and retrieval. The improved Driver Risk Field introduces a new risk height function and a speed adaptive look ahead mechanism, and the dynamic cost model integrates kinetic energy, oriented bounding box constraints, and Gaussian kernel diffusion smoothing for ...