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2019 | OriginalPaper | Buchkapitel

Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions

verfasst von : Simon Burton, Lydia Gauerhof, Bibhuti Bhusan Sethy, Ibrahim Habli, Richard Hawkins

Erschienen in: Computer Safety, Reliability, and Security

Verlag: Springer International Publishing

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Abstract

Due to their ability to efficiently process unstructured and highly dimensional input data, machine learning algorithms are being applied to perception tasks for highly automated driving functions. The consequences of failures and insufficiencies in such algorithms are severe and a convincing assurance case that the algorithms meet certain safety requirements is therefore required. However, the task of demonstrating the performance of such algorithms is non-trivial, and as yet, no consensus has formed regarding an appropriate set of verification measures. This paper provides a framework for reasoning about the contribution of performance evidence to the assurance case for machine learning in an automated driving context and applies the evaluation criteria to a pedestrian recognition case study.

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Vorheriges Kapitel Uncertainty Wrappers for Data-Driven Models

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Titel: Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions
verfasst von: Simon Burton
Lydia Gauerhof
Bibhuti Bhusan Sethy
Ibrahim Habli
Richard Hawkins
Verlag: Springer International Publishing
Buch: Computer Safety, Reliability, and Security
Print ISBN: 978-3-030-26249-5

Electronic ISBN: 978-3-030-26250-1

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-26250-1_30

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