Wed 6 Sep 2023 13:45 - 14:15 at f128 - Safety Chair(s): Jaelson Castro

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.

Wed 6 Sep

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

13:45 - 15:15
SafetyArtifacts / RE@Next! Papers / Journal-First at f128
Chair(s): Jaelson Castro Universidade Federal de Pernambuco
Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System
A: Markus Borg CodeScene, A: Jens Henriksson Semcon, dept. Software and Emerging Tech, Gothenburg, A: Kasper Socha RISE Research Institutes of Sweden, A: Olof Lennartsson , A: Elias Sonnsjö , A: Thanh Bui RISE Research Institutes of Sweden , A: Piotr Tomaszewski RISE Research Institutes of Sweden, A: Sankar Raman Sathyamoorthy QRTECH, A: Sebastian Brink Combitech, A: Mahshid Helali Moghadam Scania R&D
Link to publication DOI File Attached
ARCADE: a Framework for Integrated Management of Safety Assurance Information
RE@Next! Papers
A: Camilo Almendra Universidade Federal do Ceará, A: Carla Silva Universidade Federal de Pernambuco
ILLOD Replication Package: An Open-Source Framework for Abbreviation-Expansion Pair Detection and Term Consolidation in RequirementsBest Artifact
A: Hussein Hasso Fraunhofer FKIE, A: Katharina Großer University of Koblenz, A: Iliass Aymaz Fraunhofer FKIE, A: Hanna Geppert Fraunhofer FKIE, A: Jan Jürjens University of Koblenz-Landau
DOI Pre-print File Attached