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Age-related effects of executive function on takeover performance in automated driving
The development of highly automated vehicles can meet elderly drivers’ mobility needs; however, worse driving performance after a takeover request (TOR) is frequently found, especially regarding non-driving related tasks (NDRTs). This study aims to detect the correlation between takeover performance...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967856/ https://www.ncbi.nlm.nih.gov/pubmed/35354816 http://dx.doi.org/10.1038/s41598-022-08522-4 |
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author | Peng, Qijia Wu, Yanbin Qie, Nan Iwaki, Sunao |
author_facet | Peng, Qijia Wu, Yanbin Qie, Nan Iwaki, Sunao |
author_sort | Peng, Qijia |
collection | PubMed |
description | The development of highly automated vehicles can meet elderly drivers’ mobility needs; however, worse driving performance after a takeover request (TOR) is frequently found, especially regarding non-driving related tasks (NDRTs). This study aims to detect the correlation between takeover performance and underlying cognitive factors comprising a set of higher order cognitive processes including executive functions. Thirty-five young and 35 elderly participants were tested by computerized cognitive tasks and simulated driving tasks to evaluate their executive functions and takeover performance. Performance of n-back tasks, Simon tasks, and task switching were used to evaluate updating, inhibition, and shifting components of executive functions by principal component analysis. The performance of lane changing after TOR was measured using the standard deviation of the steering wheel angle and minimum time-to-collision (TTC). Differences between age groups and NDRT engagement were assessed by two-way mixed analysis of variance. Older participants had significantly lower executive function ability and were less stable and more conservative when engaged in NDRT. Furthermore, a significant correlation between executive function and lateral driving stability was found. These findings highlight the interaction between age-related differences in executive functions and takeover performance; thus, provide implications for designing driver screening tests or human–machine interfaces. |
format | Online Article Text |
id | pubmed-8967856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89678562022-04-01 Age-related effects of executive function on takeover performance in automated driving Peng, Qijia Wu, Yanbin Qie, Nan Iwaki, Sunao Sci Rep Article The development of highly automated vehicles can meet elderly drivers’ mobility needs; however, worse driving performance after a takeover request (TOR) is frequently found, especially regarding non-driving related tasks (NDRTs). This study aims to detect the correlation between takeover performance and underlying cognitive factors comprising a set of higher order cognitive processes including executive functions. Thirty-five young and 35 elderly participants were tested by computerized cognitive tasks and simulated driving tasks to evaluate their executive functions and takeover performance. Performance of n-back tasks, Simon tasks, and task switching were used to evaluate updating, inhibition, and shifting components of executive functions by principal component analysis. The performance of lane changing after TOR was measured using the standard deviation of the steering wheel angle and minimum time-to-collision (TTC). Differences between age groups and NDRT engagement were assessed by two-way mixed analysis of variance. Older participants had significantly lower executive function ability and were less stable and more conservative when engaged in NDRT. Furthermore, a significant correlation between executive function and lateral driving stability was found. These findings highlight the interaction between age-related differences in executive functions and takeover performance; thus, provide implications for designing driver screening tests or human–machine interfaces. Nature Publishing Group UK 2022-03-30 /pmc/articles/PMC8967856/ /pubmed/35354816 http://dx.doi.org/10.1038/s41598-022-08522-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Peng, Qijia Wu, Yanbin Qie, Nan Iwaki, Sunao Age-related effects of executive function on takeover performance in automated driving |
title | Age-related effects of executive function on takeover performance in automated driving |
title_full | Age-related effects of executive function on takeover performance in automated driving |
title_fullStr | Age-related effects of executive function on takeover performance in automated driving |
title_full_unstemmed | Age-related effects of executive function on takeover performance in automated driving |
title_short | Age-related effects of executive function on takeover performance in automated driving |
title_sort | age-related effects of executive function on takeover performance in automated driving |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967856/ https://www.ncbi.nlm.nih.gov/pubmed/35354816 http://dx.doi.org/10.1038/s41598-022-08522-4 |
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