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.
翻译:正在制订新的安全标准和技术准则,以支持基于最低限值的系统的安全,例如汽车领域的ISO 21448 SOTIF和自动系统框架内使用的机器学习保证。SOTIF和AMLAS提供了高级别指导,但细节必须针对每个具体案例加以说明。我们启动了一个研究项目,目的是证明在开放汽车系统中一个最低限值部件的完整安全案例。本文报告是工业-学术界合作在SMIRK的安全保障方面的结果,一个基于最大限值的行人自动自动制动模拟器在工业级模拟器中运行。我们展示了在SMLISK上应用AMLAS用于最低限值操作设计领域的情况,即我们对其基于最低限值的集成部件有一个完整的安全案例。最后,我们报告所吸取的经验教训,并向SMIRK提供SMIRK和根据开放源许可证向研究界提供用于再利用的安全案例。