2021/09/20 by Maximilian Kahn, Kahn, Maximilian, Atrisha Sarkar +3 · 1 citation
Engineering · Psychology · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Human-Automation Interaction and Safety #Multiagent Systems (cs.MA) #Robotics (cs.RO) #Traffic and Road Safety
paper · doi:10.48550/arxiv.2109.09807
openalex publication_date 2021/09/20 · openalex created_date 2022/07/12 · openalex updated_date 2026/07/28
A particular challenge for both autonomous and human driving is dealing with risk associated with dynamic occlusion, i.e., occlusion caused by other vehicles in traffic. Based on the theory of hypergames, we develop a novel multi-agent dynamic occlusion risk (DOR) measure for assessing situational risk in dynamic occlusion scenarios. Furthermore, we present a white-box, scenario-based, accelerated safety validation framework for assessing safety of strategic planners in AV. Based on evaluation over a large naturalistic database, our proposed validation method achieves a 4000% speedup compared to direct validation on naturalistic data, a more diverse coverage, and ability to generalize beyond the dataset and generate commonly observed dynamic occlusion crashes in traffic in an automated manner.