Automotive safety and machine learning initial results from a study on how to adapt the ISO 26262 safety standard

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 whi...

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Veröffentlicht in:2018 IEEE ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) S. 47 - 49
Hauptverfasser: Henriksson, Jens, Borg, Markus, Englund, Cristofer
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 28.05.2018
Schriftenreihe:ACM Conferences
Schlagworte:
Automotive engineering
automotive software
Computing methodologies
Computing methodologies > Machine learning
Hazards
interview study
ISO Standards
Machine learning
safety
Software and its engineering
Software and its engineering > Software organization and properties > Extra-functional properties > Software safety
Software safety
Training
automotive software
interview study
machine learning
safety
ISBN:1450357393, 9781450357395, 9781538662618, 1538662612
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
ISBN:1450357393
9781450357395
9781538662618
1538662612
DOI:10.1145/3194085.3194090