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Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems

School of Engineering, Hochschule für Technik und Wirtschaft Berlin, Wilhelminenhofstraße 75A, 12459 Berlin, Germany
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Mach. Learn. Knowl. Extr. 2020, 2(4), 579-602; https://doi.org/10.3390/make2040031
Received: 23 October 2020 / Revised: 17 November 2020 / Accepted: 17 November 2020 / Published: 19 November 2020
(This article belongs to the Section Thematic Reviews)
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. View Full-Text
Keywords: machine learning; safety; cyber-physical systems; hazard analysis machine learning; safety; cyber-physical systems; hazard analysis
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MDPI and ACS Style

Pereira, A.; Thomas, C. Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems. Mach. Learn. Knowl. Extr. 2020, 2, 579-602. https://doi.org/10.3390/make2040031

AMA Style

Pereira A, Thomas C. Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems. Machine Learning and Knowledge Extraction. 2020; 2(4):579-602. https://doi.org/10.3390/make2040031

Chicago/Turabian Style

Pereira, Ana, and Carsten Thomas. 2020. "Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems" Machine Learning and Knowledge Extraction 2, no. 4: 579-602. https://doi.org/10.3390/make2040031

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