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An Analysis of ISO 26262: Machine Learning andSafety in Automotive Software University of Waterloo

SAE Technical Papers (1906-current) Available online

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Format:
Conference/Event
Author/Creator:
Salay, Salay, author.
Contributor:
Czarnecki, Krzysztof
Queiroz, Rodrigo
Conference Name:
WCX World Congress Experience (2018-04-10 : Detroit, Michigan, United States)
Language:
English
Physical Description:
1 online resource
Place of Publication:
Warrendale, PA SAE International 2018
Summary:
AbstractMachine 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
Notes:
Vendor supplied data
Publisher Number:
2018-01-1075
Access Restriction:
Restricted for use by site license

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