Volume 42 Issue 3
Jun.  2024
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2024  >  42(3): 11-19
WANG Yiyun, YU Rongjie. An Analysis of Safety Influencing Factors for Longitudinal Interaction Between Vehicles in Human-machine Mixed Traffic Driving Conditions[J]. Journal of Transport Information and Safety, 2024, 42(3): 11-19. doi: 10.3963/j.jssn.1674-4861.2024.03.002
Citation: WANG Yiyun, YU Rongjie. An Analysis of Safety Influencing Factors for Longitudinal Interaction Between Vehicles in Human-machine Mixed Traffic Driving Conditions[J]. Journal of Transport Information and Safety, 2024, 42(3): 11-19. doi: 10.3963/j.jssn.1674-4861.2024.03.002
shu

An Analysis of Safety Influencing Factors for Longitudinal Interaction Between Vehicles in Human-machine Mixed Traffic Driving Conditions

doi: 10.3963/j.jssn.1674-4861.2024.03.002

  • Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 208104, China

  • Received Date: 2023-08-16
    Available Online: 2024-10-21
    Abstract
  • Autonomous vehicles are gradually introduced to the existing traffic environment, leading to a mixed flow of both autonomous vehicles and human-driven vehicles. Studies show that the crash rate per-kilometer for autonomous vehicles is 9.1, which is more than twice that of human-driven vehicles (4.1). The ratio of the rear-end crash pattern between autonomous vehicles and human-driven vehicles is 57.5%, which exceeds 27.9% of among human-driven vehicles. Therefore, there is an urgent need to investigate the safety mechanisms of longitudinal interactions of autonomous vehicles and human-driven vehicles. Existing studies typically employ driving simulation experiments to analyze the longitudinal interaction and safety between human-driven and autonomous vehicles in virtual environments. However, the differences between simulated environments and real-world road scenarios make it challenging to accurately capture the interaction behavior between vehicles in mixed human-autonomous traffic flows. In this study, public road-testing dataset of autonomous vehicles are utilized to extract longitudinal interacting scenarios, and the influencing factors and the impact mechanisms of longitudinal interaction behavior and safety are investigated. Specifically, scenarios of human-driven vehicles following the other human-driven vehicle, and following an autonomous vehicle are studied, Structural equation model is applied to construct a chained relationship among driving behavior of leading vehicle, type of leading vehicle (whether it is an autonomous vehicle or not), speed level of vehicles on the roadway, and the safety surrogate measure. The modelling results revealed the type of leading vehicle is identified as an influencing factor in longitudinal interaction safety. When other variables remain constant, the safety of interactions between human drivers and autonomous vehicles as leading vehicles decreased compared to interactions with other human-driven vehicles as leading vehicles.

     

    • FullText(HTML)
  • loading
    • References(27)
  • [1]
    WAYMO L L C. We're building the world's most experienced driver[R/OL]. (2019-09-05)[2023-08-16]. https://waymo.com.
    [2]
    魏文. 华为预测2030年自动驾驶新车渗透率达20%, 智能汽车时代临近?[R/OL]. (2021-09-23)[2023-08-16]. https://m.yicai.com/news/101181289.html.

    WEI W. The penetration rate of new autonomous vehicles in China is expected to reach 20% by 2030. [EB/OL]. (2021-09-23)[2023-08-16]. https://m.yicai.com/news/101181289.html.
    [3]
    TAHIR Z, ALEXANDER R. Coverage based testing for V&V and safety assurance of self-driving autonomous vehicles: a systematic literature review[C]. IEEE International Conference on Artificial Intelligence Testing(AITest), Oxford, UK: IEEE, 2020.
    [4]
    MAHDINIA I, MOHAMMADNAZAR A, ARVIN R, et al. Integration of automated vehicles in mixed traffic: evaluating changes in performance of following human-driven vehicles[J]. Accident Analysis & Prevention, 2021, 152: 106006.
    [5]
    SCHOETTLE B, SIVAK M. A preliminary analysis of real-world crashes involving self-driving vehicles[R]. Ann Arbor, USA: University of Michigan Transportation Research Institute, 2015.
    [6]
    XU C, DING Z, WANG C, et al. Statistical analysis of the patterns and characteristics of connected and autonomous vehicle involved crashes[J]. Journal of Safety Research, 2019, 71: 41-47. doi: 10.1016/j.jsr.2019.09.001
    [7]
    MA Z, ZHANG Y. Driver-automated Vehicle interaction in mixed traffic: types of interaction and drivers' driving styles[J]. Human Factors, 2024, 66(2): 544-561. doi: 10.1177/00187208221088358
    [8]
    STANGE V, KUHN M, VOLLRATH M. Safety at first sight? - manual drivers' experience and driving behavior at first contact with Level 3 vehicles in mixed traffic on the highway[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 87: 327-346. doi: 10.1016/j.trf.2022.04.004
    [9]
    REDDY N, HOOGENDOORN S P, FARAH H. How do the recognizability and driving styles of automated vehicles affect human drivers' gap acceptance at T-Intersections?[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 90: 451-465. doi: 10.1016/j.trf.2022.09.018
    [10]
    HUANG Y, YE Y, SUN J, et al. Characterizing the impact of autonomous vehicles on macroscopic fundamental diagrams[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(6): 6530-6541. doi: 10.1109/TITS.2023.3265647
    [11]
    WEN X, CUI Z, JIAN S. Characterizing car-following behaviors of human drivers when following automated vehicles using the real-world dataset[J]. Accident Analysis & Prevention, 2022, 172: 106689.
    [12]
    WANG Y, FARAH H, YU R, et al. Characterizing behavioral differences of autonomous vehicles and human-driven vehicles at signalized intersections based on Waymo open dataset[J]. Transportation Research Record, 2023, 2677(11): 324-337. doi: 10.1177/03611981231165783
    [13]
    HU X, ZHENG Z, CHEN D, et al. Autonomous vehicle's impact on traffic: empirical evidence from Waymo open dataset and implications from modelling[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24 (6) : 6711-6724. doi: 10.1109/TITS.2023.3258145
    [14]
    YU R, ZHENG Y, QIN Y, et al. Utilizing partial least-squares path modeling to analyze crash risk contributing factors for Shanghai urban expressway system[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2019, 5(4): 05019001. doi: 10.1061/AJRUA6.0001022
    [15]
    WAYMO L L C. Waymo open dataset[R/OL]. (2019)[2023-08-16]. https://waymo.com/open/.
    [16]
    HOUSTON J, ZUIDHOF G, BERGAMINI L, et al. One thousand and one hours: self-driving motion prediction dataset[C]. Conference on Robot Learning, London, UK: PMLR, 2021.
    [17]
    CAESAR H, BANKITI V, LANG A, et al. Nuscenes: a multimodal dataset for autonomous driving[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA: IEEE, 2020.
    [18]
    PUNZO V, MONTANINO M, CIUFFO B. On the assessment of vehicle trajectory data accuracy and application to the next generation simulation(NGSIM)program data[J]. Transportation Research Part C: Emerging Technologies, 2011, 19(6): 1243-1262. doi: 10.1016/j.trc.2010.12.007
    [19]
    SUN P, KRETZSCHMAR H, DOTIWALLA X, et al. Scalability in perception for autonomous driving: Waymo open dataset[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE, 2020.
    [20]
    ETTINGER S, CHENG S, CAINE B, et al. Large scale interactive motion forecasting for autonomous driving: the waymo open motion dataset[C]. IEEE/CVF International Conference on Computer Vision. Montreal, Canada: IEEE, 2021.
    [21]
    SUN Z, YAO X, QIN Z, et al. Modeling car-following heterogeneities by considering leader-follower compositions and driving style differences[J]. Transportation Research Record, 2021, 2675(11): 851-864. doi: 10.1177/03611981211020006
    [22]
    HU X, ZHENG Z, CHEN D, et al. Processing, assessing, and enhancing the Waymo autonomous vehicle open dataset for driving behavior research[J]. Transportation Research Part C: Emerging Technologies, 2022, 134: 103490. doi: 10.1016/j.trc.2021.103490
    [23]
    WANG C, XIE Y, HUANG H, et al. A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling[J]. Accident Analysis & Prevention, 2021, 157: 106157.
    [24]
    鄢云珠, 傅忠宁, 岳金田. 车辆怠速起停系统使用意愿结构方程模型及影响因素分析[J]. 交通信息与安全, 2023, 41(6): 161-170.

    YAN Y Z, FU Z N, YUE J T. An analysis of the influence factors on using intention of vehicle idle start-stop system with a structural equation model[J]. Journal of Transport Information and Safety, 2023, 41(6): 161-170. (in Chinese)
    [25]
    王永岗, 张衡, 彭志鹏, 等. 基于结构方程模型的出租车事故影响因素分析[J]. 重庆交通大学学报(自然科学版), 2021, 40(6): 36. doi: 10.3969/j.issn.1674-0696.2021.06.06

    WANG Y G, ZHANG H, PENG Z P, et al. Analysis of influencing factors of taxi accidents based on structural equation model[J]. Journal of Chongqing Jiaotong University(Natural Science), 2021, 40(6): 36. (in Chinese) doi: 10.3969/j.issn.1674-0696.2021.06.06
    [26]
    陈春. 道路交通事故的影响因素研究: 基于结构方程模型的实证研究[J]. 中国安全生产科学技术, 2014, 10(5): 110-116.

    CHEN C. Research on influencing factors of road traffic accidents: empirical study based on structural equation model[J]. Journal of Safety Science and Technology, 2014, 10 (5): 110-116. (in Chinese)
    [27]
    姚荣涵, 祁文彦, 郭伟伟. 自动驾驶环境下驾驶人接管行为结构方程模型[J]. 交通运输工程学报, 2021, 21(2): 209-221.

    YAO R H, QI W Y, GUO W W. Structural equation model of drivers' takeover behaviors in autonomous driving environment[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 209-221. (in Chinese)
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 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(4)  / Tables(6)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (175) PDF downloads(21) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return