高速公路隧道路段对驾驶员视觉及心率影响规律研究
摘要
随着交通事业的快速发展，公路隧道的数量有了突飞猛进的增长。公路隧道一方面实现了截弯、降坡、提速、提高运营安全和运营收益，另一方面又由于事故多发、易发，事故发生后危害大、处理困难等问题成为了公路交通的瓶颈。
在由人-车-路构成的交通系统中，人是感知的主体，是第一重要因素，车辆和道路环境的优劣都要通过人的感知得到体现。人也是该系统中最活跃的因素，易受其他因素的影响。所以通过研究驾驶员的视觉及心率变化，对于提高驾驶员的可靠性、提高车辆和道路环境的质量和安全性具有重要的意义。
本文首先分析了隧道的环境特征与交通事故特征，通过对外界刺激与人体反应之间的关系分析，得出了隧道环境与驾驶员视觉及生理变化之间的关系，为研究奠定了理论基础。选择驾驶员的瞳孔直径大小、平均注视时间、平均注视次数、注视时间百分比、平均眨眼次数、平均眨眼时间和眨眼时间百分比等作为评价指标,研究不同长度隧道间的视觉负荷；选择驾驶员注视次数、注视时间百分比、眨眼次数和眨眼时间百分比等评价指标,研究隧道内部不同路段间的视觉负荷；选择心率作为生理负荷的评价指标，研究不同长度隧道驾驶员的生理负荷。
其次，应用眼动仪、生理记录仪等仪器搭建道路试验平台。基于采集到的驾驶员视觉数据，研究了不同长度隧道驾驶员的瞳孔直径变化规律及瞳孔直径均值之间的差异，为隧道路段的照明提出了一些优化建议；其次研究了不同长度隧道驾驶员的平均注视次数、平均注视时间以及注视时间百分比之间的差异，以及隧道内不同路段间注视次数、平均注视时间的差异，为隧道限速及内部照明提出了一些安全设计建议；然后，研究了不同长度隧道驾驶员的平均眨眼时间、平均眨眼次数和眨眼时间百分比之间的差异，以及隧道内不同路段驾驶员的眨眼次数、眨眼时间百分比之间的差异，为隧道安全行车提供了一些建议。应用生理记录仪采集到的驾驶员的心电数据，研究了不同长度隧道驾驶员心率的变化规律，基于心率的变化规律研究了隧道内驾驶员心率最小值出现位置与隧道长度、平均行驶车速之间的关系，并应用线性回归方法建立了三者之间的数学模型，通过模型分析为隧道设计和安全管理提供了一些理论参考。
With the rapid development of traffic facilities, expressway tunnel has a greatincrease in the amount. Expressway tunnels, on one hand realize cut-off bends, speed,improve operation safety and operation profit, on the other hand, with thedisadvantage of prone to accidents, accident harm and handling difficulty, they havebecome the bottleneck of expressway traffic.
Traffic system is composed of driver, vehicle and road. Of them, driver is thetheme to perceive, so he’s the first important factor. Only by means of driver’sperception, can the performance of vehicles and road environment be reflected.Meanwhile, driver is also the most active factor in the system, for he is susceptible tothe other factors. So the study of the driver's vision, physiological and psychologicalcharacteristics, has an important significance to improve the reliability of drivers andthe quality of vehicles and road environment.
This paper firstly analyzes the tunnel environmental and its accidentcharacteristics. Through researching the relationship between human response andenvironment change, the relationship between tunnel environment and driver’s visualand physiological variation is established, which is the theoretical foundation for theresearch. Based on this theory, this paper chose driver's pupil diameter size, averagefixation time, average fixation frequency, fixation times, the percentage of fixationtime, average blink time, average blink frequency and the percentage of blink time forthe vision load evaluation index between different length tunnel, while differentsections of the tunnel chose average fixation time, fixation frequency, average blinktime, average blink frequency for the vision load evaluation index. The heart rate ischosen as the physiological load evaluation indexes.
Secondly, Using the Smart Eye tracking system, Biopac MP100Physiologicaldata recorder and other instruments, a road test platform is set up. Based on driver'seye movement data collected, driver’s pupil diameter variation and pupil diametersize differences among different length tunnels are analyzed, and some suggestionsfor optimizing tunnel road lighting are also put forward; The average fixation duration,average fixation times, and the percentage of fixation duration accounting for thetravelling time among different length tunnels, as well as the average fixation durationand the fixation times of different sections in tunnels, are also studied. Some speedlimit and tunnel interior lighting suggestions are put forward; In addition, the averageblink duration, average blink times, and the percentage of blink duration accountingfor the travelling time among different length tunnels, as well as the average blink duration and the blink times of different sections in tunnel are also studied, then somesuggestions for tunnel safe driving are offered. Applying the physiological datacollected in tunnels, heart rate variation with the length in different length tunnels isanalysed. Based on the change rule of the heart rate in the tunnel, driver’s minimumheart rate position is found and the relationships among the minimum heart rateposition, average vehicle velocity and the tunnel length are studied. Then a linearregression model of the three is established. After analyzing the model, somesuggestions for tunnel design and management is offered.
在由人-车-路构成的交通系统中，人是感知的主体，是第一重要因素，车辆和道路环境的优劣都要通过人的感知得到体现。人也是该系统中最活跃的因素，易受其他因素的影响。所以通过研究驾驶员的视觉及心率变化，对于提高驾驶员的可靠性、提高车辆和道路环境的质量和安全性具有重要的意义。
本文首先分析了隧道的环境特征与交通事故特征，通过对外界刺激与人体反应之间的关系分析，得出了隧道环境与驾驶员视觉及生理变化之间的关系，为研究奠定了理论基础。选择驾驶员的瞳孔直径大小、平均注视时间、平均注视次数、注视时间百分比、平均眨眼次数、平均眨眼时间和眨眼时间百分比等作为评价指标,研究不同长度隧道间的视觉负荷；选择驾驶员注视次数、注视时间百分比、眨眼次数和眨眼时间百分比等评价指标,研究隧道内部不同路段间的视觉负荷；选择心率作为生理负荷的评价指标，研究不同长度隧道驾驶员的生理负荷。
其次，应用眼动仪、生理记录仪等仪器搭建道路试验平台。基于采集到的驾驶员视觉数据，研究了不同长度隧道驾驶员的瞳孔直径变化规律及瞳孔直径均值之间的差异，为隧道路段的照明提出了一些优化建议；其次研究了不同长度隧道驾驶员的平均注视次数、平均注视时间以及注视时间百分比之间的差异，以及隧道内不同路段间注视次数、平均注视时间的差异，为隧道限速及内部照明提出了一些安全设计建议；然后，研究了不同长度隧道驾驶员的平均眨眼时间、平均眨眼次数和眨眼时间百分比之间的差异，以及隧道内不同路段驾驶员的眨眼次数、眨眼时间百分比之间的差异，为隧道安全行车提供了一些建议。应用生理记录仪采集到的驾驶员的心电数据，研究了不同长度隧道驾驶员心率的变化规律，基于心率的变化规律研究了隧道内驾驶员心率最小值出现位置与隧道长度、平均行驶车速之间的关系，并应用线性回归方法建立了三者之间的数学模型，通过模型分析为隧道设计和安全管理提供了一些理论参考。
With the rapid development of traffic facilities, expressway tunnel has a greatincrease in the amount. Expressway tunnels, on one hand realize cut-off bends, speed,improve operation safety and operation profit, on the other hand, with thedisadvantage of prone to accidents, accident harm and handling difficulty, they havebecome the bottleneck of expressway traffic.
Traffic system is composed of driver, vehicle and road. Of them, driver is thetheme to perceive, so he’s the first important factor. Only by means of driver’sperception, can the performance of vehicles and road environment be reflected.Meanwhile, driver is also the most active factor in the system, for he is susceptible tothe other factors. So the study of the driver's vision, physiological and psychologicalcharacteristics, has an important significance to improve the reliability of drivers andthe quality of vehicles and road environment.
This paper firstly analyzes the tunnel environmental and its accidentcharacteristics. Through researching the relationship between human response andenvironment change, the relationship between tunnel environment and driver’s visualand physiological variation is established, which is the theoretical foundation for theresearch. Based on this theory, this paper chose driver's pupil diameter size, averagefixation time, average fixation frequency, fixation times, the percentage of fixationtime, average blink time, average blink frequency and the percentage of blink time forthe vision load evaluation index between different length tunnel, while differentsections of the tunnel chose average fixation time, fixation frequency, average blinktime, average blink frequency for the vision load evaluation index. The heart rate ischosen as the physiological load evaluation indexes.
Secondly, Using the Smart Eye tracking system, Biopac MP100Physiologicaldata recorder and other instruments, a road test platform is set up. Based on driver'seye movement data collected, driver’s pupil diameter variation and pupil diametersize differences among different length tunnels are analyzed, and some suggestionsfor optimizing tunnel road lighting are also put forward; The average fixation duration,average fixation times, and the percentage of fixation duration accounting for thetravelling time among different length tunnels, as well as the average fixation durationand the fixation times of different sections in tunnels, are also studied. Some speedlimit and tunnel interior lighting suggestions are put forward; In addition, the averageblink duration, average blink times, and the percentage of blink duration accountingfor the travelling time among different length tunnels, as well as the average blink duration and the blink times of different sections in tunnel are also studied, then somesuggestions for tunnel safe driving are offered. Applying the physiological datacollected in tunnels, heart rate variation with the length in different length tunnels isanalysed. Based on the change rule of the heart rate in the tunnel, driver’s minimumheart rate position is found and the relationships among the minimum heart rateposition, average vehicle velocity and the tunnel length are studied. Then a linearregression model of the three is established. After analyzing the model, somesuggestions for tunnel design and management is offered.
引文
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[3]许文斌.隧道缘何成为交通事故多发地.北国网.http://news.lnd.com.cn/htm/2011-07/20/content_1873180.htm
[4]点击渝武高速北碚隧道交通事故.北碚报数字版. http://beibei.gov.cn/bbb/Qnews.asp?ID=9027
[5]隧道缘何成为交通事故多发.大众网.http://www.dzwww.com/rollnews/news/201104/t20110421_7134544.htm
[6]周然.图:重庆渝武高速重庆段两车追尾致2死9伤.中国频道. http://news.eastday.com/c/20090608/u1a4423346.html
[7]兰临高速公路隧道发生油罐车爆炸现场发现4具遗体.黑龙江网络广播电视台.2011-04-09.http://www.hljtv.com/2011/0409/15063_5. shtml
[8]杜坤.长大隧道驾驶员心理研究[D].西安：长安大学，2006
[9]唐登科.驾驶员驾车生理、心理反应与道路线形关系的研究[D].东南大学，2006
[10]Narisada.K, Yoshikawa.K. TUNNEL ENTRANCE LIGHTING-EFFECTOF FIXATION POINT AND OTHER FACTORS ON THEDETERMINATION OF REQUIREMENTS[J]. Lighting Research andTechnology,1974,6(1):9-19
[11]JoséManuel Serrano, Friedhelm Blennemann Motorist behaviour study forthe Gibraltar road tunnel[J]. Tunnelling and Underground SpaceTechnology,1992,7(1):9-18
[12]Alison Smiley, Thomas Smahel, Moshe Eizenman. Impact of VideoAdvertising on Driver Fixation Patterns[J]. Transportation Research Record:TRB, National Research Council, Washington. D.C.,2004,(1899):76-83.
[13]David L. Smith, James Chang, Richard Glassco, James Foley, and DanielCohen. Methodology for Capturing Driver Eye Glance Behavior DuringIn-Vehicle Secondary Tasks. Transportation Research Record,2005,1937:61–65.
[14]Yulan Liang, John D. Lee, Michelle L. Reyes. Non-intrusive Detection ofDriver Cognitive Distraction in Real-time Using Bayesian Networks. TRB2007Annual Meeting
[15]M.P.Manser，P.A.Hancock. The influence of perceptual speed regulation onspeed perception, choice, and control: Tunnel wall characteristics andinfluences [J]. Accident Analysis and Prevention,39:69-78
[16]Bryan Reimer. Impact of Cognitive Task Complexity on Drivers’ VisualTunnelin[J]. Transportation Research Record,2009,2138:13-19.
[17]Katja Kircher，Christer Ahlstrom. The impact of tunnel design and lightingon the performance of attentive and visually distracted drivers[J]. AccidentAnalysis and Prevention,2012,47:153-161
[18]J.A. Groeger. J. A. Rothengatter. Traffic psychology and behavior.Transportation Research Part F,1998,1:1-9
[19]Jennifer Healey, Rosalind Picard. SmartCar: Detecting Driver Stress.Proceedings15Th International Conference On PatternRecognition,2000,4:218-221
[20]Shing-Hong Liu, Chin-Teng Lin, and Wen-Hung Chao. The Short-TimeFractal Scaling of Heart Rate Variability to Estimate the Mental Stress ofDriver[J]. Proceedings of the2004IEEE International Conference onNetworking, Sensing&Control,2004,pp.829-833
[21]Chin-Teng Lin, Ruei-Cheng Wu, Sheng-Fu Liang, at al. EEG-Baseddrowsiness estimation for safety driving using independent componentanalysis[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—I:REGULAR PAPERS,2005,52(12):2726-2735
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