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Predicting takeover response to silent automated vehicle failures

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...

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Detalles Bibliográficos
Autores principales: Mole, Callum, Pekkanen, Jami, Sheppard, William, Louw, Tyron, Romano, Richard, Merat, Natasha, Markkula, Gustav, Wilkie, Richard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703974/
https://www.ncbi.nlm.nih.gov/pubmed/33253219
http://dx.doi.org/10.1371/journal.pone.0242825
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author Mole, Callum
Pekkanen, Jami
Sheppard, William
Louw, Tyron
Romano, Richard
Merat, Natasha
Markkula, Gustav
Wilkie, Richard
author_facet Mole, Callum
Pekkanen, Jami
Sheppard, William
Louw, Tyron
Romano, Richard
Merat, Natasha
Markkula, Gustav
Wilkie, Richard
author_sort Mole, Callum
collection PubMed
description 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.
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spelling pubmed-77039742020-12-03 Predicting takeover response to silent automated vehicle failures Mole, Callum Pekkanen, Jami Sheppard, William Louw, Tyron Romano, Richard Merat, Natasha Markkula, Gustav Wilkie, Richard PLoS One Research Article 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. Public Library of Science 2020-11-30 /pmc/articles/PMC7703974/ /pubmed/33253219 http://dx.doi.org/10.1371/journal.pone.0242825 Text en © 2020 Mole et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mole, Callum
Pekkanen, Jami
Sheppard, William
Louw, Tyron
Romano, Richard
Merat, Natasha
Markkula, Gustav
Wilkie, Richard
Predicting takeover response to silent automated vehicle failures
title Predicting takeover response to silent automated vehicle failures
title_full Predicting takeover response to silent automated vehicle failures
title_fullStr Predicting takeover response to silent automated vehicle failures
title_full_unstemmed Predicting takeover response to silent automated vehicle failures
title_short Predicting takeover response to silent automated vehicle failures
title_sort predicting takeover response to silent automated vehicle failures
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703974/
https://www.ncbi.nlm.nih.gov/pubmed/33253219
http://dx.doi.org/10.1371/journal.pone.0242825
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