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Integration of Machine Learning (ML) components in critical applicationsintroduces novel challenges for software certification and verification. Newsafety standards and technical guidelines are under development to support thesafety of ML-based systems, e.g., ISO 21448 SOTIF for the automotive domain andthe Assurance of Machine Learning for use in Autonomous Systems (AMLAS)framework. SOTIF and AMLAS provide high-level guidance but the details must bechiseled out for each specific case. We initiated a research project with thegoal to demonstrate a complete safety case for an ML component in an openautomotive system. This paper reports results from an industry-academiacollaboration on safety assurance of SMIRK, an ML-based pedestrian automaticemergency braking demonstrator running in an industry-grade simulator. Wedemonstrate an application of AMLAS on SMIRK for a minimalistic operationaldesign domain, i.e., we share a complete safety case for its integratedML-based component. Finally, we report lessons learned and provide both SMIRKand the safety case under an open-source licence for the research community toreuse.

Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System