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

2018-01-1075

04/03/2018

Event
WCX World Congress Experience
Authors Abstract
Content
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.
Meta TagsDetails
DOI
https://doi.org/10.4271/2018-01-1075
Pages
7
Citation
Salay, R., Queiroz, R., and Czarnecki, K., "An Analysis of ISO 26262: Machine Learning and Safety in Automotive Software," SAE Technical Paper 2018-01-1075, 2018, https://doi.org/10.4271/2018-01-1075.
Additional Details
Publisher
Published
Apr 3, 2018
Product Code
2018-01-1075
Content Type
Technical Paper
Language
English