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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.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0001-7879-4371
RISE - Research Institutes of Sweden, ICT, Viktoria.ORCID iD: 0000-0002-1043-8773
2018 (English)Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
2018. p. 47-49
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:ri:diva-34195DOI: 10.1145/3194085.3194090Scopus ID: 2-s2.0-85051137851ISBN: 978-1-4503-5739-5 (print)OAI: oai:DiVA.org:ri-34195DiVA, id: diva2:1232993
Conference
1st Software Engineering for AI in Autonomous Systems
Available from: 2018-07-13 Created: 2018-07-13 Last updated: 2019-01-07Bibliographically approved

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Borg, MarkusEnglund, Cristofer

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Total: 138 hits
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