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Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry
RISE - Research Institutes of Sweden, ICT, Viktoria.ORCID iD: 0000-0002-1043-8773
Blekinge Institute of Technology, Sweden.
RISE - Research Institutes of Sweden, ICT, Viktoria.
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2019 (English)In: Journal of Automotive Software Engineering, Vol. 1, no 1, p. 1-13Article in journal (Refereed) Published
Abstract [en]

Deep neural networks (DNNs) will emerge as a cornerstone in automotive software engineering. However, developing systems with DNNs introduces novel challenges for safety assessments. This paper reviews the state-of-the-art in verification and validation of safety-critical systems that rely on machine learning. Furthermore, we report from a workshop series on DNNs for perception with automotive experts in Sweden, confirming that ISO 26262 largely contravenes the nature of DNNs. We recommend aerospace-to-automotive knowledge transfer and systems-based safety approaches, for example, safety cage architectures and simulated system test cases.

Place, publisher, year, edition, pages
2019. Vol. 1, no 1, p. 1-13
Keywords [en]
Deep learning, Safety-critical systems, Machine learning, Verification and validation, ISO 26262
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-39335DOI: 10.2991/jase.d.190131.001OAI: oai:DiVA.org:ri-39335DiVA, id: diva2:1335546
Available from: 2019-07-05 Created: 2019-07-05 Last updated: 2019-07-05Bibliographically approved

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Borg, MarkusEnglund, Cristofer

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