Identifying Critical Scenarios in Autonomous Driving During Operation

Ensuring autonomous driving systems’ safety, reliability, and trustworthiness is paramount to preventing incorrect or unexpected system behaviors and hazardous scenarios. However, due to the complexity of such systems and the immense search space of possible scenarios, testing could be infeasible, necessitating the need to detect critical situations during operation. This paper proposes a hybrid approach that combines qualitative reasoning and object detection to prevent and discover critical driving scenarios. The proposed approach relies on identifying spatiotemporal patterns of detected objects in the driving environment that are indicative of critical scenarios, such as specific changes in movement or physical impossibilities. We evaluate the approach’s effectiveness on real-world driving data and demonstrate its ability to identify critical driving situations successfully. Moreover, we discuss the challenges associated with the approach and outline future research activities.