  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 demonstrate a complete safety case for an ML component in an open
automotive system. This paper reports results from an industry-academia
collaboration on safety assurance of SMIRK, an ML-based pedestrian automatic
emergency braking demonstrator running in an industry-grade simulator. We
demonstrate an application of AMLAS on SMIRK for a minimalistic operational
design domain, i.e., we share a complete safety case for its integrated
ML-based component. Finally, we report lessons learned and provide both SMIRK
and the safety case under an open-source licence for the research community to
reuse.
