An Analysis of ISO 26262: Machine Learning and Safety in Automotive Software
Abstract
Machine learning (ML) plays an ever-increasing role in advanced automotive functionality for driver assistance and autonomous operation; however, its adequacy from the perspective of safety certification remains controversial. In this paper, we analyze the impacts that the use of ML within software has on the ISO 26262 safety lifecycle and ask what could be done to address them. We then provide a set of recommendations on how to adapt the standard to better accommodate ML.
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- Authors
- Salay, R., Queiroz, R., & Czarnecki, K.
- Affiliations
- University of Waterloo (CA)
- ORCID
- Czarnecki, Krzysztof
- Venue
- SAE Technical Paper Series
- Publisher
- SAE International
- Published
- 2018-04-03
- Volume
- 1
- ISSN
- 0148-7191 (ISSN-L)
- 2688-3627
- OpenAlex
- W2796163371
- Cited by
- 109
- References
- 26
- Citations by year
- 2026: 10 · 2025: 9 · 2024: 7 · 2023: 18 · 2022: 17 · 2021: 14 · 2020: 16 · 2019: 8 · 2018: 10
- FWCI
- 29.24
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- SAE technical papers on CD-ROM/SAE technical paper series (publishedVersion)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Physical Sciences › Computer Science › Hardware and Architecture › Real-Time Systems Scheduling
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- ISO 26262, machine learning, automotive software, functional safety, safety certification, safety lifecycle, autonomous driving, driver assistance systems, standard adaptation
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