Mole, C orcid.org/0000-0002-1463-6419, Pekkanen, J, Sheppard, W orcid.org/0000-0002-2691-6469 et al. (5 more authors) (2020) Predicting takeover response to silent automated vehicle failures. PLoS One, 15 (11). e0242825. ISSN 1932-6203
Abstract
Current and foreseeable automated vehicles are not able to respond appropriately in all circumstances and require human monitoring. An experimental examination of steering automation failure shows that response latency, variability and corrective manoeuvring systematically depend on failure severity and the cognitive load of the driver. The results are formalised into a probabilistic predictive model of response latencies that accounts for failure severity, cognitive load and variability within and between drivers. The model predicts high rates of unsafe outcomes in plausible automation failure scenarios. These findings underline that understanding variability in failure responses is crucial for understanding outcomes in automation failures.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Mole et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Psychology (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/P017517/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Nov 2020 13:28 |
Last Modified: | 16 Apr 2021 09:35 |
Status: | Published |
Publisher: | Public Library of Science (PLoS) |
Identification Number: | 10.1371/journal.pone.0242825 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168206 |