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Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety

To study the influence of traffic signs information volume (TSIV) on drivers’ visual characteristics and driving safety, the simulation scenarios of different levels of TSIV were established, and 30 participants were recruited for simulated driving tests. The eye tracker was used to collect eye move...

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Detalles Bibliográficos
Autores principales: Han, Lei, Du, Zhigang, Wang, Shoushuo, Chen, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9408178/
https://www.ncbi.nlm.nih.gov/pubmed/36011983
http://dx.doi.org/10.3390/ijerph191610349
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author Han, Lei
Du, Zhigang
Wang, Shoushuo
Chen, Ying
author_facet Han, Lei
Du, Zhigang
Wang, Shoushuo
Chen, Ying
author_sort Han, Lei
collection PubMed
description To study the influence of traffic signs information volume (TSIV) on drivers’ visual characteristics and driving safety, the simulation scenarios of different levels of TSIV were established, and 30 participants were recruited for simulated driving tests. The eye tracker was used to collect eye movement data under three-speed conditions (60 km/h, 80 km/h, and 100 km/h) and different levels of TSIV (0 bits/km, 10 bits/km, 20 bits/km, 30 bits/km, 40 bits/km, and 50 bits/km). Principal component analysis (PCA) was used to select indicators sensitive to the influence of TSIV on the drivers’ visual behavior and to analyze the influence of TSIV on the drivers’ visual characteristics and visual workload intensity under different speed conditions. The results show that the fixation duration, saccade duration, and saccade amplitude are the three eye movement indicators that are most responsive to changes in the TSIV. The driver’s visual characteristics perform best at the S3 TSIV level (30 bits/km), with the lowest visual workload intensity, indicating that drivers have the lowest psychological stress and lower driving workload when driving under this TSIV condition. Therefore, a density of 30 bits/km is suggested for the TSIV, in order to ensure the security and comfort of the drivers. The theoretical underpinnings for placing and optimizing traffic signs will be provided by this work.
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spelling pubmed-94081782022-08-26 Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety Han, Lei Du, Zhigang Wang, Shoushuo Chen, Ying Int J Environ Res Public Health Article To study the influence of traffic signs information volume (TSIV) on drivers’ visual characteristics and driving safety, the simulation scenarios of different levels of TSIV were established, and 30 participants were recruited for simulated driving tests. The eye tracker was used to collect eye movement data under three-speed conditions (60 km/h, 80 km/h, and 100 km/h) and different levels of TSIV (0 bits/km, 10 bits/km, 20 bits/km, 30 bits/km, 40 bits/km, and 50 bits/km). Principal component analysis (PCA) was used to select indicators sensitive to the influence of TSIV on the drivers’ visual behavior and to analyze the influence of TSIV on the drivers’ visual characteristics and visual workload intensity under different speed conditions. The results show that the fixation duration, saccade duration, and saccade amplitude are the three eye movement indicators that are most responsive to changes in the TSIV. The driver’s visual characteristics perform best at the S3 TSIV level (30 bits/km), with the lowest visual workload intensity, indicating that drivers have the lowest psychological stress and lower driving workload when driving under this TSIV condition. Therefore, a density of 30 bits/km is suggested for the TSIV, in order to ensure the security and comfort of the drivers. The theoretical underpinnings for placing and optimizing traffic signs will be provided by this work. MDPI 2022-08-19 /pmc/articles/PMC9408178/ /pubmed/36011983 http://dx.doi.org/10.3390/ijerph191610349 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Han, Lei
Du, Zhigang
Wang, Shoushuo
Chen, Ying
Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety
title Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety
title_full Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety
title_fullStr Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety
title_full_unstemmed Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety
title_short Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety
title_sort analysis of traffic signs information volume affecting driver’s visual characteristics and driving safety
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9408178/
https://www.ncbi.nlm.nih.gov/pubmed/36011983
http://dx.doi.org/10.3390/ijerph191610349
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