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Assessing the physiological effect of non-driving-related task performance in conditionally automated driving systems: A systematic review and meta-analysis protocol
BACKGROUND: Level 3 automated driving systems involve the continuous performance of the driving task by artificial intelligence within set environmental conditions, such as a straight highway. The driver's role in Level 3 is to resume responsibility of the driving task in response to any depart...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176551/ https://www.ncbi.nlm.nih.gov/pubmed/37188078 http://dx.doi.org/10.1177/20552076231174782 |
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author | Coyne, Rory Ryan, Leona Moustafa, Mohamed Smeaton, Alan F Corcoran, Peter Walsh, Jane C |
author_facet | Coyne, Rory Ryan, Leona Moustafa, Mohamed Smeaton, Alan F Corcoran, Peter Walsh, Jane C |
author_sort | Coyne, Rory |
collection | PubMed |
description | BACKGROUND: Level 3 automated driving systems involve the continuous performance of the driving task by artificial intelligence within set environmental conditions, such as a straight highway. The driver's role in Level 3 is to resume responsibility of the driving task in response to any departure from these conditions. As automation increases, a driver's attention may divert towards non-driving-related tasks (NDRTs), making transitions of control between the system and user more challenging. Safety features such as physiological monitoring thus become important with increasing vehicle automation. However, to date there has been no attempt to synthesise the evidence for the effect of NDRT engagement on drivers’ physiological responses in Level 3 automation. METHODS: A comprehensive search of the electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO, and IEEE Explore will be conducted. Empirical studies assessing the effect of NDRT engagement on at least one physiological parameter during Level 3 automation, in comparison with a control group or baseline condition will be included. Screening will take place in two stages, and the process will be outlined within a PRISMA flow diagram. Relevant physiological data will be extracted from studies and analysed using a series of meta-analyses by outcome. A risk of bias assessment will also be completed on the sample. CONCLUSION: This review will be the first to appraise the evidence for the physiological effect of NDRT engagement during Level 3 automation, and will have implications for future empirical research and the development of driver state monitoring systems. |
format | Online Article Text |
id | pubmed-10176551 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-101765512023-05-13 Assessing the physiological effect of non-driving-related task performance in conditionally automated driving systems: A systematic review and meta-analysis protocol Coyne, Rory Ryan, Leona Moustafa, Mohamed Smeaton, Alan F Corcoran, Peter Walsh, Jane C Digit Health Research Protocol BACKGROUND: Level 3 automated driving systems involve the continuous performance of the driving task by artificial intelligence within set environmental conditions, such as a straight highway. The driver's role in Level 3 is to resume responsibility of the driving task in response to any departure from these conditions. As automation increases, a driver's attention may divert towards non-driving-related tasks (NDRTs), making transitions of control between the system and user more challenging. Safety features such as physiological monitoring thus become important with increasing vehicle automation. However, to date there has been no attempt to synthesise the evidence for the effect of NDRT engagement on drivers’ physiological responses in Level 3 automation. METHODS: A comprehensive search of the electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO, and IEEE Explore will be conducted. Empirical studies assessing the effect of NDRT engagement on at least one physiological parameter during Level 3 automation, in comparison with a control group or baseline condition will be included. Screening will take place in two stages, and the process will be outlined within a PRISMA flow diagram. Relevant physiological data will be extracted from studies and analysed using a series of meta-analyses by outcome. A risk of bias assessment will also be completed on the sample. CONCLUSION: This review will be the first to appraise the evidence for the physiological effect of NDRT engagement during Level 3 automation, and will have implications for future empirical research and the development of driver state monitoring systems. SAGE Publications 2023-05-08 /pmc/articles/PMC10176551/ /pubmed/37188078 http://dx.doi.org/10.1177/20552076231174782 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Research Protocol Coyne, Rory Ryan, Leona Moustafa, Mohamed Smeaton, Alan F Corcoran, Peter Walsh, Jane C Assessing the physiological effect of non-driving-related task performance in conditionally automated driving systems: A systematic review and meta-analysis protocol |
title | Assessing the physiological effect of non-driving-related task
performance in conditionally automated driving systems: A systematic review and
meta-analysis protocol |
title_full | Assessing the physiological effect of non-driving-related task
performance in conditionally automated driving systems: A systematic review and
meta-analysis protocol |
title_fullStr | Assessing the physiological effect of non-driving-related task
performance in conditionally automated driving systems: A systematic review and
meta-analysis protocol |
title_full_unstemmed | Assessing the physiological effect of non-driving-related task
performance in conditionally automated driving systems: A systematic review and
meta-analysis protocol |
title_short | Assessing the physiological effect of non-driving-related task
performance in conditionally automated driving systems: A systematic review and
meta-analysis protocol |
title_sort | assessing the physiological effect of non-driving-related task
performance in conditionally automated driving systems: a systematic review and
meta-analysis protocol |
topic | Research Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176551/ https://www.ncbi.nlm.nih.gov/pubmed/37188078 http://dx.doi.org/10.1177/20552076231174782 |
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