Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System. (arXiv:2204.07874v2 [cs.SE] UPDATED)

Integration of Machine Learning (ML) components in critical applications
introduces novel challenges for software certification and verification. New
safety standards and technical guidelines are under development to support the
safety of ML-based systems, e.g., ISO 21448 SOTIF for the automotive domain and
the Assurance of Machine Learning for use in Autonomous Systems (AMLAS)
framework. SOTIF and AMLAS provide high-level guidance but the details must be
chiseled out for each specific case. We initiated a research project with the
goal to …

arxiv case emergency machine machine learning safety