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2020 | OriginalPaper | Buchkapitel

Structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications

verfasst von : Gesina Schwalbe, Bernhard Knie, Timo Sämann, Timo Dobberphul, Lydia Gauerhof, Shervin Raafatnia, Vittorio Rocco

Erschienen in: Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops

Verlag: Springer International Publishing

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Abstract

Deep neural networks (DNNs) are widely considered as a key technology for perception in high and full driving automation. However, their safety assessment remains challenging, as they exhibit specific insufficiencies: black-box nature, simple performance issues, incorrect internal logic, and instability. These are not sufficiently considered in existing standards on safety argumentation. In this paper, we systematically establish and break down safety requirements to argue the sufficient absence of risk arising from such insufficiencies. We furthermore argue why diverse evidence is highly relevant for a safety argument involving DNNs, and classify available sources of evidence. Together, this yields a generic approach and template to thoroughly respect DNN specifics within a safety argumentation structure. Its applicability is shown by providing examples of methods and measures following an example use case based on pedestrian detection.

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Vorheriges Kapitel A Safety Case Pattern for Systems with Machine Learning Components

Nächstes Kapitel An Assurance Case Pattern for the Interpretability of Machine Learning in Safety-Critical Systems

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Titel: Structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications
verfasst von: Gesina Schwalbe
Bernhard Knie
Timo Sämann
Timo Dobberphul
Lydia Gauerhof
Shervin Raafatnia
Vittorio Rocco
Verlag: Springer International Publishing
Buch: Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops
Print ISBN: 978-3-030-55582-5

Electronic ISBN: 978-3-030-55583-2

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-55583-2_29

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