Volume 40 Issue 1
Feb.  2022
Turn off MathJax
Article Contents
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

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

doi: 10.3963/j.jssn.1674-4861.2022.01.015
  • Received Date: 2021-09-27
    Available Online: 2022-03-31
  • This paper aims to investigate the effects ofthe existence and content of information from connected vehicles and infrastructure (CVI) ondriving workload and behavior of young drivers at signalized and non-signalized intersections. Driving simulationsfor such intersectionsin urban areas are developed, in which 26 young drivers aged between 22 and 30 are involved. The results show that: such information can significantly reduce the workload of young driversand the increase in heart rate reduced by 1.95 beats/min for signalized intersections and 2.96 beats/min or 3.29 beats/min for non-signalized intersections, respectively. In addition, such information can significantly reduce the response time for braking actions of young drivers with 2.35 s at signalized intersections and 2.71 s or 2.09 s at non-signalized intersections respectively. It is also found that it can improve the stability of vehicles in reducing the standard deviations of vehicle speed by 31.33% for signalized intersections and 47.40% or 60.23% for non-signalized intersections, respectively. In addition, when thered phase of the vehicle moving direction at signalized intersections is about to end, the command information from CVI can significantly reduce the response time of young drivers by 3.47s, and the standard deviation of vehicle speed by 39.10%, compared to the effectiveness of regular instruction information.

     

  • loading
  • [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]
    [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
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(4)

    Article Metrics

    Article views (1121) PDF downloads(78) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return