Bridging the Gap between ISO 26262 and Machine Learning: A Survey of Techniques for Developing Confidence in Machine Learning Systems 2020-01-0738
Machine Learning (ML) based technologies are increasingly being used to fulfill safety-critical functions in autonomous and advanced driver assistance systems (ADAS). This change has been spurred by recent developments in ML and Artificial Intelligence techniques as well as rapid growth of computing power. However, demonstrating that ML-based systems achieve the necessary level of safety integrity remains a challenge. Current research and development work focused on establishing safe operation of ML-based systems presents individual techniques that might be used to gain confidence in these systems. As a result, there is minimal guidance for supporting a safety standard such as ISO 26262 - Road Vehicles - Functional Safety, to enable the development of ML-based systems. This paper presents a survey of recent ML literature to identify techniques and methods that can contribute to meeting ISO 26262 requirements. The surveyed literature is mapped onto the system development lifecycle V-model and the applicability of individual techniques and methods are discussed for each major phase of development.
Citation: Serna, J., Diemert, S., Millet, L., Debouk, R. et al., "Bridging the Gap between ISO 26262 and Machine Learning: A Survey of Techniques for Developing Confidence in Machine Learning Systems," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(3):1538-1550, 2020, https://doi.org/10.4271/2020-01-0738. Download Citation
Jose Serna, Simon Diemert, Laure Millet, Rami Debouk, Ramesh S, Jeffrey Joyce
Critical Systems Labs Inc., General Motors LLC
WCX SAE World Congress Experience
SAE International Journal of Advances and Current Practices in Mobility-V129-99EJ