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2017 | Supplement | Chapter

Making the Case for Safety of Machine Learning in Highly Automated Driving

Authors : Simon Burton, Lydia Gauerhof, Christian Heinzemann

Published in: Computer Safety, Reliability, and Security

Publisher: Springer International Publishing

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Abstract

This paper describes the challenges involved in arguing the safety of highly automated driving functions which make use of machine learning techniques. An assurance case structure is used to highlight the systems engineering and validation considerations when applying machine learning methods for highly automated driving. Particular focus is placed on addressing functional insufficiencies in the perception functions based on convolutional neural networks and possible types of evidence that can be used to mitigate against such risks.

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next chapter A Thought Experiment on Evolution of Assurance Cases
Footnotes
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Metadata
Title
Making the Case for Safety of Machine Learning in Highly Automated Driving
Authors
Simon Burton
Lydia Gauerhof
Christian Heinzemann
Copyright Year
2017
Book
Computer Safety, Reliability, and Security

Print ISBN: 978-3-319-66283-1

Electronic ISBN: 978-3-319-66284-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-66284-8_1

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