Simulation-based Safety Assurance for an AVP System incorporating Learning-Enabled Components. (arXiv:2311.03362v1 [cs.SE])

There have been major developments in Automated Driving (AD) and Driving
Assist Systems (ADAS) in recent years. However, their safety assurance, thus
methodologies for testing, verification and validation AD/ADAS safety-critical
applications remain as one the main challenges. Inevitably AI also penetrates
into AD/ADAS applications, such as object detection. Despite important
benefits, adoption of such learned-enabled components and systems in
safety-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 not
handle learning-enabled ones well. The authors have participated in the
public-funded project FOCETA , and developed an Automated Valet Parking (AVP)
use case. As the nature of the baseline implementation is imperfect, it offers
a space for continuous improvement based on modelling, verification,
validation, and monitoring techniques. In this publication, we explain the
simulation-based development platform that is designed to verify and validate
safety-critical learning-enabled systems in continuous engineering loops.