@article{PereiraThomas, author = {Pereira, Ana and Thomas, Carsten}, title = {Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems}, series = {Machine Learning and Knowledge Extraction}, volume = {2}, journal = {Machine Learning and Knowledge Extraction}, number = {4}, publisher = {MDPI}, issn = {2504-4990}, doi = {10.3390/make2040031}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:523-14570}, abstract = {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.}, language = {en} }