Automotive safety and machine learning: Initial results from a study on how to adapt the ISO 26262 safety standard
Paper i proceeding, 2018

Machine learning (ML) applications generate a continuous stream of success stories from various domains. ML enables many novel applications, also in safety-critical contexts. However, the functional safety standards such as ISO 26262 did not evolve to cover ML. We conduct an exploratory study on which parts of ISO 26262 represent the most critical gaps between safety engineering and ML development. While this paper only reports the first steps toward a larger research endeavor, we report three adaptations that are critically needed to allow ISO 26262 compliant engineering, and related suggestions on how to evolve the standard.

Författare

Jens Henriksson

Cyber Physical Systems

Markus Borg

RISE Research Institutes of Sweden

Cristofer Englund

RISE Research Institutes of Sweden

2018 IEEE/ACM 40th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

Vol. 2018 IEEE/ACM 40th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

2018 ACM/IEEE 1st International Workshop on Software Engineering for AI in Autonomous Systems
Gothenburg, ,

Ämneskategorier

Datorseende och robotik (autonoma system)

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2022-03-11