STPA-Based Continuous Safety Verification of Autonomous Driving Systems During Simulation

Autonomous driving systems (ADS) shall safely navigate diverse scenarios encountered in an open world. Prior to deployment, it is crucial to evaluate their ability to handle these situations. A popular approach for ADS verification is scenario-based testing, which simulates relevant critical scenarios in a virtual environment. However, evaluating these behaviors can be challenging, as traditional scenario-based testing often relies on metrics that may overlook subtle risks without immediate critical impact that still pose long-term safety concerns. System-theoretic process analysis (STPA) is a hazard analysis technique suitable for the analysis of modern complex systems. In this work, we employ STPA to derive system-level evaluation criteria for scenario-based testing frameworks designed to be universally applicable in any sim-ulation run. Their utilization provides a data-driven approach to safety assurance, helping to uncover unknown safety issues that, if unaddressed, could compromise the long-term reliability of ADS. Furthermore, the approach is demonstrated on a construction zone navigation extension for ADS, illustrating its potential to provide continuous, data-driven feedback from testing to safety assurance. The method shall contribute to the early detection of arising safety issues and ultimately lead to the development of safer systems.