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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9408178 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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