Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems
Abstrak
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.
Topik & Kata Kunci
Penulis (2)
Ana I. Pereira
Carsten Thomas
Akses Cepat
- Tahun Terbit
- 2020
- Bahasa
- en
- Total Sitasi
- 78×
- Sumber Database
- Semantic Scholar
- DOI
- 10.3390/make2040031
- Akses
- Open Access ✓