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城市交叉口车路网联信息对青年驾驶人驾驶行为的影响分析

翟俊达 鲁光泉 陈发城 刘淼淼

翟俊达, 鲁光泉, 陈发城, 刘淼淼. 城市交叉口车路网联信息对青年驾驶人驾驶行为的影响分析[J]. 交通信息与安全, 2022, 40(1): 126-134. doi: 10.3963/j.jssn.1674-4861.2022.01.015
引用本文: 翟俊达, 鲁光泉, 陈发城, 刘淼淼. 城市交叉口车路网联信息对青年驾驶人驾驶行为的影响分析[J]. 交通信息与安全, 2022, 40(1): 126-134. doi: 10.3963/j.jssn.1674-4861.2022.01.015
ZHAI Junda, LU Guangquan, CHEN Facheng, LIU Miaomiao. Effects of Information from Connected Vehicles and Infrastructure on Driving Behavior of Young Drivers at Urban Intersections[J]. Journal of Transport Information and Safety, 2022, 40(1): 126-134. doi: 10.3963/j.jssn.1674-4861.2022.01.015
Citation: ZHAI Junda, LU Guangquan, CHEN Facheng, LIU Miaomiao. Effects of Information from Connected Vehicles and Infrastructure on Driving Behavior of Young Drivers at Urban Intersections[J]. Journal of Transport Information and Safety, 2022, 40(1): 126-134. doi: 10.3963/j.jssn.1674-4861.2022.01.015

城市交叉口车路网联信息对青年驾驶人驾驶行为的影响分析

doi: 10.3963/j.jssn.1674-4861.2022.01.015
基金项目: 

国家重点研发计划项目 2018YFB1600500

国家自然科学基金项目 52131204

详细信息
    作者简介:

    翟俊达(1995—),博士研究生.研究方向:驾驶行为.E-mail:zhaijunda@buaa.edu.cn

    通讯作者:

    鲁光泉(1974—),博士,教授. 研究方向:驾驶行为、车路协同控制.E-mail:lugq@buaa.edu.cn

  • 中图分类号: U491.6

Effects of Information from Connected Vehicles and Infrastructure on Driving Behavior of Young Drivers at Urban Intersections

  • 摘要:

    基于驾驶模拟器设计了城市道路信号和无信号交叉口场景下的模拟驾驶实验,研究网联信息的存在和内容对青年驾驶人工作负荷和操纵行为的影响。实验共包括被试26人,均为22~30岁的青年驾驶人。结果表明:对无信号交叉口(区分次干路直行车辆和主路对向左转车辆)或信号交叉口红灯时间即将结束时,网联信息可以显著降低青年驾驶人的工作负荷,有效降低心率增长值(信号交叉口:减少1.95 beats/min;无信号交叉口:分别减少2.96 beats/min和3.29 beats/min)。此外,网联信息还可以显著降低青年驾驶人的制动反应时间(信号交叉口:降低2.35 s;无信号交叉口:分别降低2.71 s和2.09 s),减少车辆速度标准差(信号交叉口:31.33%;无信号交叉口:分别减少47.40%和60.23%),提升了驾驶稳定性。在信号交叉口车辆行进方向的红灯时间即将结束时,相比于指示信息,车路网联指令信息可使制动反应时间减少3.47 s,车辆的速度标准差减少39.10%。

     

  • 图  1  驾驶模拟器

    Figure  1.  Driving simulator

    图  2  场景和协同信息示意图

    Figure  2.  Diagram of the scenarios and the connected information

    图  3  驾驶人心率增长值示意图

    Figure  3.  Diagram of drivers' heart rate increase

    图  4  车辆速度标准差示意图

    Figure  4.  Diagram of standard deviation of vehicle' s velocity

    图  5  驾驶人制动反应时间示意图

    Figure  5.  Diagram of drivers'brake reaction time

    图  6  网联信息级别对驾驶人心率增长值和车辆速度标准差的影响

    Figure  6.  The effects of the levels of connected information on driver's heart rate increase and standard deviation of vehicle's velocity

    表  1  交叉口场景描述

    Table  1.   Description of the intersection scenarios

    场景编号 是否有信号灯 冲突类别 场景描述
    1 进入交叉口 绿灯即将结束,加速,通过前方交叉口
    2
    3 红灯即将结束,减速,避免停车,通过前方交叉口
    4
    5 与次干路直行车辆冲突 优先到达冲突点,加速通过
    6 滞后到达冲突点,减速让行
    7 与主路对向左转车辆冲突 优先到达冲突点,加速通过
    8 滞后到达冲突点,减速让行
    下载: 导出CSV

    表  2  网联信息具体内容

    Table  2.   Details of the connected information

    场景编号 网联信息水平 网联信息
    1 指示信息 “距离信号交叉口150 m,距离本次绿灯结束13 s”
    2 指示信息 “请加速至60 km/h通过前方信号交叉口”
    3 指示信息 “距离信号交叉口150 m,距离本次红灯结束16 s”
    4 指示信息 “请减速至30 km/h通过信号交叉口,避免停车”
    5 指示信息 “注意右侧直行车辆,请加速通过”
    6 指示信息 “注意右侧直行车辆,请减速让行”
    7 指示信息 “注意对向左转车辆,请加速通过”
    8 指示信息 “注意对向左转车辆,请减速让行”
    下载: 导出CSV

    表  3  无信息组和信息组的驾驶人年龄、性别和驾龄变量检验

    Table  3.   Variable tests of drivers' age, gender and driving experience between information and non-information group

    组别(人数/人) 年龄(标准差)/岁 性别(男/女) 驾龄(标准差)/年
    无信息组(11) 24.72(2.38) 64%/36% 3.64(1.82)
    信息组(12) 25.08(2.72) 67%/33% 4.42(1.89)
    非参数检验 χ2= 0.097 χ2= 0.068 χ2= 0.943
    p =0.756 p =0.795 p =0.332
    下载: 导出CSV

    表  4  评价指标详细描述

    Table  4.   Description of the indicators

    评价指标 描述
    心率增长值 计算方式为从事件开始时刻到驾驶人通过交叉口这段时间内的心率平均值减去该被试的基准状态心率平均值,用于评价驾驶人工作负荷
    速度标准差 从事件触发时刻开始到通过交叉口,自车行驶速度的标准差,常被用于评价车辆纵向稳定性
    制动反应时间 仅用于需要驾驶人减速的事件,定义为从事件触发时刻到制动踏板行程超过全部行程的10%所经过的时间[34-35]
    下载: 导出CSV
  • [1] LIN P, LIU J, JIN P J, et al. Autonomous vehicle-intersection coordination method in a connected vehicle environment[J]. IEEE Intelligent Transportation Systems Magazine, 2017, 9(4): 37-47. doi: 10.1109/MITS.2017.2743167
    [2] 冉斌, 谭华春, 张健, 等. 智能网联交通技术发展现状及趋势[J]. 汽车安全与节能学报, 2018, 9(2): 119-130. doi: 10.3969/j.issn.1674-8484.2018.02.001

    RAN B, TAN H C, ZHANG J, et al. Development status and trend of connected automated vehicle highway system[J]. Journal of Automotive Safety and Energy, 2018, 9(2): 119-130. (in Chinese) doi: 10.3969/j.issn.1674-8484.2018.02.001
    [3] MAY A J, ROSS T, BAYER S H. Drivers' information requirements when navigating in an urban environment[J]. Journal of Navigation, 2003, 56(1): 89-100. doi: 10.1017/S0373463302002114
    [4] XING H, QIN H, NIU J. Driver's information needs in automated driving[C]. The International Conference on Cross-Cultural Design, Cham Springer, 2017.
    [5] AHREMS J. Implementation of collision warning algorithm based on V2V communications[C]. The 25th International Conference Radioelektronika, 2015.
    [6] SENGUPTA R, REZAEI S, SHLADOVER S E, et al. Cooperative collision warning systems: Concept definition and experimental implementation[J]. Journal of Intelligent Transportation Systems, 2007, 11(3): 143-155. doi: 10.1080/15472450701410452
    [7] HAFNER M R, CUNNINGHAM D, CAMINITI L, et al. Cooperative collision avoidance at intersections: Algorithms and experiments[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(3): 1162-1175. doi: 10.1109/TITS.2013.2252901
    [8] 冯笑凡. 基于视觉特性的高速雾天车路协同预警系统分心研究[D]. 北京: 北方工业大学, 2021.

    FENG X F. Distraction research of connected vehicle early-warning system foggy condition freeway based on driver's visual characteristics[D]. Beijing: North China University of Technology, 2021. (in Chinese)
    [9] 薛晓卿. 车联网环境下的交通安全预警方法研究[D]. 北京: 北京理工大学, 2016.

    XUE X Q. Research on traffic safety warning method based on internet of vehicle[D]. Beijing: Beijing Institute of Technology; 2016. (in Chinese)
    [10] GULER S I, MENENDEZ M, MEIER L. Using connected vehicle technology to improve the efficiency of intersections[J]. Transportation Research Part C: Emerging Technologies, 2014(46): 121-131. http://www.sciencedirect.com/science/article/pii/S0968090X14001211
    [11] AMOOZADEH M, RAGHURAMU A, CHUAH C N, et al. Security vulnerabilities of connected vehicle streams and their impact on cooperative driving[J]. IEEE Communications Magazine, 2015, 53(6): 126-132. doi: 10.1109/MCOM.2015.7120028
    [12] 姜慧夫. 网联自动驾驶环境下信号交叉口环保驾驶控制研究[D]. 哈尔滨: 哈尔滨工业大学, 2018.

    JIANG H F. Research on eco-driving control at signalized intersections under connected and automated vehicles environment[D]. Harbin: Harbin Institute of Technology, 2018. (in Chinese)
    [13] 魏涛. 车联网环境下汽车节能驾驶行为与速度优化方法研究[D]. 西安: 长安大学, 2019.

    WEI T. Research on energy-saving driving behavior and speed optimization method in vehicle networking environment[D]. Xi'an: Chang'an University, 2019. (in Chinese)
    [14] SIEBE C. Distracted driving and risk of road crashes among novice and experienced drivers[J]. The Journal of Emergency Medicine, 2014, 46(4): 600-601. http://www.sciencedirect.com/science/article/pii/S0736467914001929
    [15] HAQUE M M, OHLHAUSER A D, WASHINGTON S, et al. Examination of distracted driving and yellow light running: analysis of simulator data[C]. Transportation Research Board (TRB)92nd Annual Meeting Compendium of Papers. Washington, D. C. : Transportation Research Board(TRB), 2013.
    [16] HAQUE M M, OHLHAUSER A D, WASHINGTON S, et al. Decisions and actions of distracted drivers at the onset of yellow lights[J]. Accident Analysis & Prevention, 2016(96): 290-299. http://www.onacademic.com/detail/journal_1000037793345210_a76d.html
    [17] OMAE M, OGITSU T, HONMA N, et al. Automatic driving control for passing through intersection without stopping[J]. International Journal of Intelligent Transportation Systems Research, 2010, 8(3): 201-210. doi: 10.1007/s13177-010-0016-7
    [18] LI Z, LU C, GONG C, et al. Driver behavior modelling at the urban intersection via canonical correlation analysis[C]. The 3rd International Conference on Unmanned Systems (ICUS), Athens: IEEE, 2020.
    [19] SUN D J, WU S, SHEN S, et al. Simulation and assessment of traffic pollutant dispersion at an urban signalized intersection using multiple platforms[J]. Atmospheric Pollution Research, 2021, 12(2): 101087. http://www.sciencedirect.com/science/article/pii/S1309104221001537
    [20] 詹静静. 青年与老年驾驶人车辆操控行为特征及差异性研究[D]. 合肥: 合肥工业大学, 2018.

    ZHAN J J. The research on vehicle control behavior characteristics and differences between middle aged and older drivers[D]. Hefei: Hefei University of Technology, 2018. (in Chinese)
    [21] KÖRBER M, GOLD C, LECHNER D, et al. The influence of age on the take-over of vehicle control in highly automated driving[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2016(39): 19-32. http://www.sciencedirect.com/science/article/pii/S1369847816000462
    [22] LOEB H, BELWADI A, MAHESHWARI J, et al. Age and gender differences in emergency takeover from automated to manual driving on simulator[J]. Traffic Injury Prevention, 2019, 20(S2): 1-3. http://www.ncbi.nlm.nih.gov/pubmed/31663790
    [23] CHEN F, LU G, LIN Q, et al. Are novice drivers competent to take over control from level 3 automated vehicles? A comparative study with experienced drivers[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2021, 81(1): 65-81. http://www.sciencedirect.com/science/article/pii/S1369847821001145
    [24] ALREFAIE M T, SUMMERSKILL S, JACKON T W. In a heart beat: using driver's physiological changes to determine the quality of a takeover in highly automated vehicles[J]. Accident Analysis & Prevention, 2019(131): 180-190. http://www.sciencedirect.com/science/article/pii/S0001457518311217
    [25] CARSTEN O, FRANK C H L, BARNARD Y, et al. Control task substitution in semiautomated driving: Does it matter what aspects are automated?[J]. Human Factors, 2012, 54(5): 747. doi: 10.1177/0018720812460246
    [26] CARSTEN O, LAI F C H, BARNARD Y, t al. Control task substitution in semiautomated driving: Does it matter what aspects are automated?[J]. Human Factors, 2012, 54(5): 747-761. doi: 10.1177/0018720812460246
    [27] DU N, YANG X J, FENG Z. Psychophysiological responses to takeover requests in conditionally automated driving[J]. Accident Analysis & Prevention, 2020(148): 105804. http://doc.paperpass.com/foreign/arXiv201003047.html
    [28] HEIKOOP D D, DE WINTER J C, VAN AREM B, et al. Acclimatizing to automation: Driver workload and stress during partially automated car following in real traffic[J]. Transportation Research part F: Traffic Psychology and Behaviour, 2019(65): 503-517. http://www.researchgate.net/profile/Daniel_Heikoop/publication/325818143_Acclimatizing_to_automation_driver_workload_and_stress_during_partially_automated_car_following_in_real_traffic/links/5b276375a6fdcc69746b16c9/Acclimatizing-to-automation-driver-workload-and-stress-during-partially-automated-car-following-in-real-traffic.pdf
    [29] DENG T, FU J, SHAO Y, et al. Pedal operation characteristics and driving workload on slopes of mountainous road based on naturalistic driving tests[J]. Safety Science, 2019 (119): 40-49. http://www.onacademic.com/detail/journal_1000040892030710_a475.html
    [30] PERLMAN D, SAMOST A, DOMEL A G, et al. The relative impact of smartwatch and smartphone use while driving on workload, attention, and driving performance[J]. Applied Ergonomics, 2019(75): 8-16. http://www.onacademic.com/detail/journal_1000040843553010_384d.html
    [31] ZEEB K, BUCHNER A, SCHRAUF M. What determines the take-over time?An integrated model approach of driver take-over after automated driving[J]. Accident Analysis & Prevention, 2015(78): 212-221. http://www.researchgate.net/profile/Michael_Schrauf/publication/273833386_What_determines_the_take-over_time_An_integrated_model_approach_of_driver_take-over_after_automated_driving/links/5593b12408ae5af2b0eb99db.pdf
    [32] ZEEB K, BUCHNER A, SCHRAUF M. Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving[J]. Accident Analysis & Prevention, 2016(92): 230-239. http://www.researchgate.net/profile/Michael_Schrauf/publication/301624436_Is_take-over_time_all_that_matters_The_impact_of_visual-cognitive_load_on_driver_take-over_quality_after_conditionally_automated_driving/links/5874aaa208ae329d62201454/Is-take-over-time-all-that-matters-The-impact-of-visual-cognitive-load-on-driver-take-over-quality-after-conditionally-automated-driving.pdf
    [33] ZHANG B, DE WINTER J, VAROTTO S, et al. Determinants of take-over time from automated driving: A meta-analysis of 129 studies[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2019(64): 285-307. http://www.sciencedirect.com/science/article/pii/S1369847818303693
    [34] GOLD C, HAPPEE R, BENGLER K. Modeling take-over performance in level 3 conditionally automated vehicles[J]. Accident Analysis & Prevention, 2017(116): 3-13. http://www.onacademic.com/detail/journal_1000040129940710_734f.html
    [35] GOLD C, K RBER M, LECHNER D, et al. Taking over control from highly automated vehicles in complex traffic situations: the role of traffic density[J]. Human Factors, 2016, 58(4): 642-652. doi: 10.1177/0018720816634226
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  • 收稿日期:  2021-09-27
  • 网络出版日期:  2022-03-31

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