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

TL;DR

This paper presents a comprehensive safety case for an ML-based pedestrian emergency braking system, demonstrating how safety standards like AMLAS can be applied in automotive safety assurance.

Contribution

It provides a detailed safety case for an ML component in automotive systems, applying AMLAS and sharing open-source safety artifacts for community reuse.

Findings

Successful application of AMLAS to SMIRK system

Open-source safety case available for research reuse

Lessons learned for ML safety assurance in automotive

Abstract

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.