Journal of Automotive Software Engineering

Volume 1, Issue 1, January 2019, Pages 1 - 19

Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry

Authors
Markus Borg1, *, Cristofer Englund1, Krzysztof Wnuk2, Boris Duran1, Christoffer Levandowski3, Shenjian Gao2, Yanwen Tan2, Henrik Kaijser4, Henrik Lönn4, Jonas Törnqvist3
1 RISE Research Institutes of Sweden AB, Scheelevägen 17, SE-223 70 Lund, Sweden
2 Blekinge Institute of Technology, Valhallavägen 1, SE-371 41 Karlskrona, Sweden
3 QRTECH AB, Flöjelbergsgatan 1C, SE-431 35 Mölndal, Sweden
4 AB Volvo, Volvo Group Trucks Technology, SE-405 08 Gothenburg, Sweden
*Corresponding author. Email: markus.borg@ri.se
Corresponding Author
Markus Borg
Received 28 May 2018, Accepted 13 December 2018, Available Online 31 January 2019.
DOI
https://doi.org/10.2991/jase.d.190131.001How to use a DOI?
Keywords
Deep learning, Safety-critical systems, Machine learning, Verification and validation, ISO 26262
Abstract

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.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Automotive Software Engineering
Volume-Issue
1 - 1
Pages
1 - 19
Publication Date
2019/01/31
ISSN (Online)
2589-2258
DOI
https://doi.org/10.2991/jase.d.190131.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Markus Borg
AU  - Cristofer Englund
AU  - Krzysztof Wnuk
AU  - Boris Duran
AU  - Christoffer Levandowski
AU  - Shenjian Gao
AU  - Yanwen Tan
AU  - Henrik Kaijser
AU  - Henrik Lönn
AU  - Jonas Törnqvist
PY  - 2019
DA  - 2019/01/31
TI  - Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry
JO  - Journal of Automotive Software Engineering
SP  - 1
EP  - 19
VL  - 1
IS  - 1
SN  - 2589-2258
UR  - https://doi.org/10.2991/jase.d.190131.001
DO  - https://doi.org/10.2991/jase.d.190131.001
ID  - Borg2019
ER  -