Volume 1, Issue 1, 2020, Pages 1 - 19
Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry
Markus Borg1, *, Cristofer Englund1, Krzysztof Wnuk2, Boris Duran1, Christoffer Levandowski3, Shenjian Gao2, Yanwen Tan2, Henrik Kaijser4, Henrik Lönn4, Jonas Törnqvist3
RISE Research Institutes of Sweden AB, Scheelevägen 17, SE-223 70 Lund, Sweden
Blekinge Institute of Technology, Valhallavägen 1, SE-371 41 Karlskrona, Sweden
QRTECH AB, Flöjelbergsgatan 1C, SE-431 35 Mölndal, Sweden
AB Volvo, Volvo Group Trucks Technology, SE-405 08 Gothenburg, Sweden
Corresponding author. Email: firstname.lastname@example.org
Article HistoryReceived 28 May 2018
Accepted 13 December 2018
Available Online 31 January 2019
- Deep learning
Verification and validation
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.
- © 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 -