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Automotive safety and machine learning: Initial results from a study on how to adapt the ISO 26262 safety standard
Semcon Sweden AB, Gothenburg, Sweden.
RISE SICS, Lund, Sweden.ORCID-id: 0000-0001-7879-4371
RISE Viktoria, Gothenburg, Sweden. (Cooperative Systems)ORCID-id: 0000-0002-1043-8773
2018 (engelsk)Inngår i: 2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS), New York, NY: ACM Publications, 2018, s. 47-49Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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. © 2018 ACM.

sted, utgiver, år, opplag, sider
New York, NY: ACM Publications, 2018. s. 47-49
Emneord [en]
Computing methodologies, Machine learning, Software and its engineering, Software safety
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-37754DOI: 10.1145/3194085.3194090Scopus ID: 2-s2.0-85051137851ISBN: 978-1-4503-5739-5 (digital)ISBN: 978-1-5386-6261-8 (tryckt)OAI: oai:DiVA.org:hh-37754DiVA, id: diva2:1240456
Konferanse
1st ACM/IEEE International Workshop on Software Engineering for AI in Autonomous Systems, SEFAIAS 2018 (ICSE 2018), Gothenburg, Sweden, 28 May, 2018
Prosjekter
SMILE II
Forskningsfinansiär
VINNOVA
Merknad

Funding: Vinnova/FFI and partially by the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP)

Tilgjengelig fra: 2018-08-21 Laget: 2018-08-21 Sist oppdatert: 2019-01-03bibliografisk kontrollert

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