https://www.cyberfactory-1.org/wp-content/uploads/2020/11/RiskViz_Fotolia_147601142_L.jpg 1571 2356 Denecke https://www.cyberfactory-1.org/wp-content/uploads/2019/09/cyberfactory-logo-340x173.png Denecke2020-11-24 16:00:062020-11-24 16:00:06Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems
Machine Learning (ML) is increasingly applied for the control of safety-critical Cyber-Physical Systems (CPS) in application areas that cannot easily be mastered with traditional control approaches, such as autonomous driving. As a consequence, the safety of machine learning became a focus area for research in recent years. Despite very considerable advances in selected areas related to machine learning safety, shortcomings were identified on holistic approaches that take an end-to-end view on the risks associated to the engineering of ML-based control systems and their certification. Applying a classic technique of safety engineering, our paper provides a comprehensive and methodological analysis of the safety hazards that could be introduced along the ML lifecycle, and could compromise the safe operation of ML-based CPS. Identified hazards are illustrated and explained using a real-world application scenario—an autonomous shop-floor transportation vehicle. The comprehensive analysis presented in this paper is intended as a basis for future holistic approaches for safety engineering of ML-based CPS in safety-critical applications, and aims to support the focus on research onto safety hazards that are not yet adequately addressed.
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Ana Pereira and Carsten Thomas (Hochschule für Technik und Wirtschaft Berlin)