Cited by 109Safety Systems Engineering in AutonomyReal-Time Systems SchedulingAutonomous Vehicle Technology and Safety

An Analysis of ISO 26262: Machine Learning and Safety in Automotive Software

Salay, Rick, Queiroz, Rodrigo, Czarnecki, Krzysztof·2018·SAE Technical Paper Series

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.

Integrity signals

Registry

Crossref · 10.4271/2018-01-1075

Cites

26 of 26 references verified0 citation errors · 0 not checkable

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Authors
Salay, R., Queiroz, R., & Czarnecki, K.
Affiliations
University of Waterloo (CA)
Venue
SAE Technical Paper Series
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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