• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

交通标志信息量分析对驾驶员视觉特征和驾驶安全的影响。

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

机构信息

School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China.

出版信息

Int J Environ Res Public Health. 2022 Aug 19;19(16):10349. doi: 10.3390/ijerph191610349.

DOI:10.3390/ijerph191610349
PMID:36011983
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9408178/
Abstract

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.

摘要

为了研究交通标志信息量（TSIV）对驾驶员视觉特征和驾驶安全的影响，建立了不同 TSIV 水平的模拟场景，并招募了 30 名参与者进行模拟驾驶测试。使用眼动追踪器在三种速度条件（60km/h、80km/h 和 100km/h）和不同的 TSIV 水平（0bits/km、10bits/km、20bits/km、30bits/km、40bits/km 和 50bits/km）下收集眼动数据。主成分分析（PCA）用于选择对 TSIV 对驾驶员视觉行为影响敏感的指标，并分析不同速度条件下 TSIV 对驾驶员视觉特征和视觉工作负荷强度的影响。结果表明，注视持续时间、眼跳持续时间和眼跳幅度是对 TSIV 变化最敏感的三个眼动指标。在 S3 TSIV 水平（30bits/km）下，驾驶员的视觉特征表现最佳，视觉工作负荷强度最低，表明驾驶员在这种 TSIV 条件下驾驶时心理压力最低，驾驶工作量最低。因此，建议将 TSIV 设置为 30bits/km，以确保驾驶员的安全和舒适。这项工作将为交通标志的设置和优化提供理论依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/5ccba9b0eb82/ijerph-19-10349-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/748e173ad543/ijerph-19-10349-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/b885a47dbb5e/ijerph-19-10349-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/31ab32f534db/ijerph-19-10349-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/7faac666f4a5/ijerph-19-10349-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/2ffda7c7d266/ijerph-19-10349-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/46a42653c2f4/ijerph-19-10349-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/0293578832a0/ijerph-19-10349-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/4748b5f0f8b2/ijerph-19-10349-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/4227dc0cf0df/ijerph-19-10349-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/0aa1086db6e7/ijerph-19-10349-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/5ccba9b0eb82/ijerph-19-10349-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/748e173ad543/ijerph-19-10349-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/b885a47dbb5e/ijerph-19-10349-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/31ab32f534db/ijerph-19-10349-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/7faac666f4a5/ijerph-19-10349-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/2ffda7c7d266/ijerph-19-10349-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/46a42653c2f4/ijerph-19-10349-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/0293578832a0/ijerph-19-10349-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/4748b5f0f8b2/ijerph-19-10349-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/4227dc0cf0df/ijerph-19-10349-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/0aa1086db6e7/ijerph-19-10349-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f75e/9408178/5ccba9b0eb82/ijerph-19-10349-g011.jpg

相似文献

1
Analysis of Traffic Signs Information Volume Affecting Driver's Visual Characteristics and Driving Safety.交通标志信息量分析对驾驶员视觉特征和驾驶安全的影响。
Int J Environ Res Public Health. 2022 Aug 19;19(16):10349. doi: 10.3390/ijerph191610349.
2
The effects of the traffic signs information volume on the visual characteristics and workload of novice and experienced drivers.交通标志信息量对新手和经验丰富司机的视觉特征和工作负荷的影响。
Work. 2024;79(2):733-751. doi: 10.3233/WOR-230349.
3
Evaluation of traffic signs information volume at highway tunnel entrance zone based on the visual sample entropy of novice and experienced drivers.基于新手和经验丰富驾驶员的视觉样本熵评估高速公路隧道入口区域的交通标志信息量。
Traffic Inj Prev. 2024;25(3):499-509. doi: 10.1080/15389588.2023.2300645. Epub 2024 Jan 17.
4
Driver's Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China.复杂环境下交通标志信息暴露时驾驶员的认知工作量与驾驶性能：以中国高速公路为例
Int J Environ Res Public Health. 2017 Feb 17;14(2):203. doi: 10.3390/ijerph14020203.
5
The evaluation of eye-catching effect in freeway tunnel entrance zone based on fixation behavior characteristics.基于注视行为特征的高速公路隧道入口区域视认性评价。
Traffic Inj Prev. 2024;25(2):122-132. doi: 10.1080/15389588.2023.2280950. Epub 2024 Jan 2.
6
Research on drivers' hazard perception in plateau environment based on visual characteristics.基于视觉特征的高原环境下驾驶员危险感知研究。
Accid Anal Prev. 2022 Mar;166:106540. doi: 10.1016/j.aap.2021.106540. Epub 2021 Dec 24.
7
Eye movement as a function to explore the effects of improved signs design and audio warning on drivers' behavior at STOP-sign-controlled grade crossings.眼动作为一种功能，用于探索改进的标志设计和音频警告对驾驶员在有停车标志控制的平交道口的行为的影响。
Accid Anal Prev. 2022 Jul;172:106693. doi: 10.1016/j.aap.2022.106693. Epub 2022 May 9.
8
Effect of highway directional signs on driver mental workload and behavior using eye movement and brain wave.基于眼动和脑电的高速公路导向标志对驾驶员心理负荷和行为的影响
Accid Anal Prev. 2020 Oct;146:105705. doi: 10.1016/j.aap.2020.105705. Epub 2020 Aug 17.
9
Can variations in visual behavior measures be good predictors of driver sleepiness? A real driving test study.视觉行为测量的变化能否成为驾驶员困倦的良好预测指标？一项实际驾驶测试研究。
Traffic Inj Prev. 2017 Feb 17;18(2):132-138. doi: 10.1080/15389588.2016.1203425. Epub 2016 Oct 20.
10
The Relationship of the Information Quantity of Urban Roadside Traffic Signs and Drivers' Visibility Based on Information Transmission.基于信息传递的城市道路路侧交通标志信息量与驾驶员可视性的关系。
Int J Environ Res Public Health. 2021 Oct 19;18(20):10976. doi: 10.3390/ijerph182010976.

引用本文的文献

1
Information density threshold of urban road traffic signs based on visual comfort.基于视觉舒适度的城市道路交通标志信息密度阈值
Heliyon. 2024 Aug 29;10(17):e37080. doi: 10.1016/j.heliyon.2024.e37080. eCollection 2024 Sep 15.

本文引用的文献

1
Research on drivers' hazard perception in plateau environment based on visual characteristics.基于视觉特征的高原环境下驾驶员危险感知研究。
Accid Anal Prev. 2022 Mar;166:106540. doi: 10.1016/j.aap.2021.106540. Epub 2021 Dec 24.
2
The Relationship of the Information Quantity of Urban Roadside Traffic Signs and Drivers' Visibility Based on Information Transmission.基于信息传递的城市道路路侧交通标志信息量与驾驶员可视性的关系。
Int J Environ Res Public Health. 2021 Oct 19;18(20):10976. doi: 10.3390/ijerph182010976.
3
Driving style recognition and comparisons among driving tasks based on driver behavior in the online car-hailing industry.
基于网约车行业驾驶员行为的驾驶风格识别及驾驶任务比较
Accid Anal Prev. 2021 May;154:106096. doi: 10.1016/j.aap.2021.106096. Epub 2021 Mar 23.
4
Evaluation of tunnel retro-reflective arch in an extra-long tunnel based on the matter-element extension method.基于物元可拓法的特长隧道内反光环隧道评价。
Accid Anal Prev. 2021 Feb;150:105913. doi: 10.1016/j.aap.2020.105913. Epub 2020 Dec 8.
5
Feature extraction of EEG signals based on functional data analysis and its application to recognition of driver fatigue state.基于功能数据分析的脑电信号特征提取及其在驾驶员疲劳状态识别中的应用。
Physiol Meas. 2021 Jan 1;41(12):125004. doi: 10.1088/1361-6579/abc66e.
6
Drivers' Visual Attention Characteristics under Different Cognitive Workloads: An On-Road Driving Behavior Study.不同认知负荷下驾驶员的视觉注意特征：一项道路驾驶行为研究。
Int J Environ Res Public Health. 2020 Jul 25;17(15):5366. doi: 10.3390/ijerph17155366.
7
Comparative analysis of data reduction techniques for questionnaire validation using self-reported driver behaviors.使用自我报告的驾驶行为对问卷进行验证的数据缩减技术的比较分析。
J Safety Res. 2020 Jun;73:133-142. doi: 10.1016/j.jsr.2020.02.004. Epub 2020 Mar 20.
8
Optimal design alternatives of advance guide signs of closely spaced exit ramps on urban expressways.城市快速路近间距出口匝道预告标志的优化设计方案。
Accid Anal Prev. 2020 Apr;138:105465. doi: 10.1016/j.aap.2020.105465. Epub 2020 Feb 9.
9
An approach for evaluating the effectiveness of traffic guide signs at intersections.交叉口交通标志有效性评估方法。
Accid Anal Prev. 2019 Aug;129:7-20. doi: 10.1016/j.aap.2019.05.003. Epub 2019 May 14.
10
Driver's visual attention to different categories of roadside advertising signs.驾驶员对不同类别道路侧方广告标志的视觉关注度。
Appl Ergon. 2019 Jul;78:127-136. doi: 10.1016/j.apergo.2019.03.001. Epub 2019 Mar 11.