Automotive safety and machine learning initial results from a study on how to adapt the ISO 26262 safety standard

Machine learning (ML) applications generate a continuous stream of success stories from various domains. ML enables many novel applications, also in safety-critical contexts. However, the functional safety standards such as ISO 26262 did not evolve to cover ML. We conduct an exploratory study on whi...

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Bibliographic Details
Published in:	2018 IEEE ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) pp. 47 - 49
Main Authors:	Henriksson, Jens, Borg, Markus, Englund, Cristofer
Format:	Conference Proceeding
Language:	English
Published:	New York, NY, USA ACM 28.05.2018
Series:	ACM Conferences
Subjects:	Automotive engineering automotive software Computing methodologies Computing methodologies > Machine learning Hazards interview study ISO Standards Machine learning safety Software and its engineering Software and its engineering > Software organization and properties > Extra-functional properties > Software safety Software safety Training automotive software interview study machine learning safety
ISBN:	1450357393, 9781450357395, 9781538662618, 1538662612
Online Access:	Get full text
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