Adaptive Mining of Failure Scenarios for Autonomous Driving Systems Based on Multi-population Genetic Algorithm

Safety of the Intended Functionality (SOTIF) testing is critical in developing autonomous vehicles, as it identifies functional limitations and performance boundaries of autonomous driving systems (ADS). Current scenario-based approaches are predominant in SOTIF testing, but lack diversity and overlook non-collision risk factors. Therefore, we propose an adaptive SOTIF testing and evaluation framework based on a multi-population genetic algorithm, capable of effectively combining risk forms to expand a variety of testing scenarios and simultaneously optimizing parameters to enhance the criticality of test cases. Moreover, apart from common collision factors, our assessment also considers the impacts of rule compliance, aiming to unearth additional potential risk scenarios. We conduct experiments by taking urban intersection passage scenarios as an example, exposing issues of Autoware, a known open-source ADS. This contribution provides insights into the extraction, testing, and evaluation of SOTIF risk scenarios in autonomous driving.