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There have been major developments in Automated Driving (AD) and DrivingAssist Systems (ADAS) in recent years. However, their safety assurance, thusmethodologies for testing, verification and validation AD/ADAS safety-criticalapplications remain as one the main challenges. Inevitably AI also penetratesinto AD/ADAS applications, such as object detection. Despite importantbenefits, adoption of such learned-enabled components and systems insafety-critical scenarios causes that conventional testing approaches (e.g.,distance-based testing in automotive) quickly become infeasible. Similarly,safety engineering approaches usually assume model-based components and do nothandle learning-enabled ones well. The authors have participated in thepublic-funded project FOCETA , and developed an Automated Valet Parking (AVP)use case. As the nature of the baseline implementation is imperfect, it offersa space for continuous improvement based on modelling, verification,validation, and monitoring techniques. In this publication, we explain thesimulation-based development platform that is designed to verify and validatesafety-critical learning-enabled systems in continuous engineering loops.

Simulation-based Safety Assurance for an AVP System incorporating Learning-Enabled Components