Burton, Simon, Gauerhof, Lydia, Hawkins, Richard David orcid.org/0000-0001-7347-3413 et al. (2 more authors) (2019) Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions. In: Computer Safety, Reliability, and Security:SAFECOMP 2019 Workshops, ASSURE, DECSoS, SASSUR, STRIVE, and WAISE, Turku, Finland, September 10, 2019, Proceedings. Lecture Notes in Computer Science . Springer , 365–377.
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 insu_ciencies 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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 19 Jul 2019 15:20 |
Last Modified: | 18 Feb 2025 00:05 |
Published Version: | https://doi.org/10.1007/978-3-030-26250-1_30 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-030-26250-1_30 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:148798 |