Online Safety Verification of Autonomous Driving Decision-Making Based on Dynamic Reachability Analysis

Abstract

Addressing decision safety in the unpredictable arena of complex traffic scenarios represents a significant hurdle for autonomous driving systems. Considering the inherent spatial-temporal uncertainties associated with the future actions of surrounding traffic participants, real-time safety verification of autonomous driving decisions is crucial to maintaining vehicular safety. Existing online verification methodologies, such as Responsibility Sensitive Safety (RSS) and Safety Force Field (SFF), ensure driving safety by formalizing human safe-driving rules and constraining the vehicle to maintain safe lateral and longitudinal distances in real-time. While these methods effectively prevent collisions instigated by the autonomous vehicle itself, they lack sufficient foresight and often result in less smooth driving trajectories. To address these limitations, we propose an innovative, interpretable, formal safety verification framework. This approach integrates both explicit and implicit traffic rules to anticipate all legally acceptable transitions of traffic scenarios. It builds the lawful, short-term reachable region for each vehicle, and verifies the safety of autonomous vehicle decisions by assessing whether the regions these vehicles inhabit, in accordance with the expected trajectory, overlap with the accessible zones of other vehicles. Furthermore, in scenarios presenting potential danger, a backup smooth safety trajectory is derived from the autonomous vehicle's legal reachability domain as a preventive measure to degrade safety threats. As a cornerstone of safety for autonomous vehicles, our proposed method ensures a continual safe trajectory in all traffic scenarios, provided that other participants adhere to traffic rules. Experimental outcomes, grounded in the ISO 34502 standard and real-world critical safety scenarios, demonstrate the method's efficacy in identifying potentially dangerous decisions and mitigating autonomous vehicle-induced traffic accidents.