Volume 41 Issue 4
Aug.  2023
Turn off MathJax
Article Contents
LI Zhenlong, PAN Mengniu, QU Yansong, ZHAO Xiaohua, GONG Jianguo, WANG Qiuhong. A Method for Evaluating the Safety over the Takeover Process of the Level 3 Automated Vehicles Based on IAHP-EWM-LDM[J]. Journal of Transport Information and Safety, 2023, 41(4): 14-23. doi: 10.3963/j.jssn.1674-4861.2023.04.002
Citation: LI Zhenlong, PAN Mengniu, QU Yansong, ZHAO Xiaohua, GONG Jianguo, WANG Qiuhong. A Method for Evaluating the Safety over the Takeover Process of the Level 3 Automated Vehicles Based on IAHP-EWM-LDM[J]. Journal of Transport Information and Safety, 2023, 41(4): 14-23. doi: 10.3963/j.jssn.1674-4861.2023.04.002

A Method for Evaluating the Safety over the Takeover Process of the Level 3 Automated Vehicles Based on IAHP-EWM-LDM

doi: 10.3963/j.jssn.1674-4861.2023.04.002
  • Received Date: 2022-07-21
    Available Online: 2023-11-23
  • In the Level 3 autonomous driving stage, the driver needs to respond and take over the vehicle when the system sends a takeover request. Therefore, to accurately assess the safety of the takeover process of Level 3 autonomous vehicles, the safety evaluation index system of the takeover process of autonomous driving is constructed. In this paper, a 4×2×2 takeover scenario factor is used to design a driving simulation test, and a driving simulator is used to collect various types of driving data. Based on the coefficient of variation method and Spearman correlation discriminant method, 13 security evaluation indicators are obtained from the analysis of 3 aspects, such as risk perception, risk avoidance manipulation and takeover performance. The subjective weights of the indicators are obtained using an improved hierarchical analysis that characterizes the experience of the experts, and the subjective weights of the indicators are obtained using entropy weights that reflect the characteristics of the data. To combine the advantages of the two methods, a composite weight incorporating both subjective and objective weights is obtained using the grade maximization method. The combined weights of risk perception, risk avoidance manipulation, and takeover performance are calculated to be 0.259, 0.475, and 0.271, which are used to construct the security evaluation index system of the takeover process. In this paper, the system is applied to comprehensively evaluate 655 takeover processes obtained from driving simulation tests, and they are classified into 3 categories of A, B and C takeover processes according to the evaluation results. Comparing the scores of the 3 types of takeover processes in 3 aspects: risk perception, risk avoidance manipulation and takeover performance, it is found that the A-type takeover process performs well in three aspects, the C-type takeover process performs poorly in risk avoidance manipulation and takeover performance, and the B-type takeover process performs intermediary between the A-type and C-type. Different types of takeover process have a better degree of differentiation in each indicator. The indicator system is constructed that effectively combines expert experience and indicator characteristics. The evaluation index system constructed in this paper effectively combines expert experience and index characteristics. It can provide theoretical support for a more comprehensive, reasonable and scientific evaluation of the safety in the process of automatic driving takeover.


  • loading
  • [1]
    SAE International. Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems: SAE J3016-2021[S]. Warrendale: SAE International, 2021.
    中华人民共和国工业和信息化部. 汽车驾驶自动化分级: GB/T 40429—2021[S]. 北京: 中国标准出版社, 2021.

    Ministry of Industry and Information Technology of the People's Republic of China. Taxonomy of drivingautomation for Vehicles: GB/T 40429—2021[S]. Beijing: China Standard Press, 2021. (in Chinese)
    GOOGLE. Google self-driving car testing report on disengagements of autonomous mode[R]. Google Auto LLC, 2015, 92(12): 249-255.
    鲁光泉, 陈发城, 李鹏辉, 等. 驾驶人跟车风险接受水平对其接管绩效的影响[J]. 汽车工程, 2021, 43(6): 808-814. doi: 10.19562/j.chinasae.qcgc.2021.06.003

    LU G Q, CHEN F C, LI P H, et al. Effect of drivers' acceptance level of car-following risk on the takeover performance[J]. Automotive Engineering, 2021, 43(6): 808-814. (in Chinese) doi: 10.19562/j.chinasae.qcgc.2021.06.003
    安玉, 焦朋朋, 白紫秀. 考虑多因素的驾驶行为安全评价与风险等级预测[J]. 系统仿真学报, 2021, 33(1): 118-126. https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ202101015.htm

    AN Y, JIAO P P, BAI Z X. Safety evaluation and risk level prediction of driving behavior considering multi-factors influence[J]. Journal of System Simulation, 2021, 33(1): 118-126. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ202101015.htm
    SCHÖNER H P, PRETTO P, SODNIK J, et al. A safety score for the assessment of driving style[J]. Traffic Injury Prevention, 2021, 22(5): 384-389. doi: 10.1080/15389588.2021.1904508
    咸化彩, 金立生, 李科勇, 等. 基于网络分析法的次任务驾驶安全评价模型[J]. 长安大学学报(自然科学版), 2015, 35: 124-128. https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL2015S1027.htm

    XIAN H C, JIN L S, LI K Y, et al. Driving safety evaluation model for secondary task driving by using the method of ANP[J]. Journal of Chang'an University(Nature Science Edition), 2015, 35: 124-128. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL2015S1027.htm
    刘志强, 王涛. 基于改进TOPSIS的驾驶行为实时安全性评估方法[J]. 重庆理工大学学报(自然科学), 2021, 35(11): 58-66. https://www.cnki.com.cn/Article/CJFDTOTAL-CGGL202111008.htm

    LIU Z Q, WANG T. Real-time safety assessment method of driving behavior based on improved topsis[J]. Journal of Chongqing University of Technology(Natural Science), 2021, 35(11): 58-66. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CGGL202111008.htm
    LIN Q F, LI S Q, MA X W, et al. Understanding take-over performance of high crash risk drivers during conditionally automated driving[J]. Accident Analysis & Prevention, 2020, 143: 105543.
    林庆峰, 王兆杰, 鲁光泉. L3级自动驾驶汽车的接管安全性评价模型[J]. 汽车工程, 2019, 41(11): 1258-1264. https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC201911006.htm

    LIN Q F, WANG Z J, LU G Q. Takeover safety evaluation model for level 3 automated vehicles[J]. Automotive Engineering, 2019, 41(11): 1258-1264. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC201911006.htm
    GOLD C, HAPPEE R, BENGLER K. Modeling take-over performance in level 3 conditionally automated vehicles[J]. Accident Analysis & Prevention, 2017, 116: 3-13.
    钮建伟, 张雪梅, 孙一品, 等. 险情中驾驶人接管自动驾驶车辆的驾驶行为研究[J]. 中国公路学报, 2018, 31(6): 272-280. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201806015.htm

    NIU J W, ZHANG X M, SUN Y P, et al. Analysis of the driving behavior during the takeover of automatic driving vehicles in dangerous traffic situations[J]. China Journal of Highway and Transport, 2018, 31(6): 272-280. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201806015.htm
    马小翔, 陈丰, 张霖. 预期接管场景下接管绩效及接管风险研究[J]. 中国公路学报, 2022, 35(1): 159-168. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202201014.htm

    MA X X, CHEN F, ZHANG L. Takeover-performance and takeover-risk evaluation under non-critical transition scenarios[J]. China Journal of Highway and Transport, 2022, 35 (1): 159-168. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202201014.htm
    周荣贵, 钟连德. 公路通行能力手册[M]. 北京: 人民交通出版社, 2017.

    ZHOU R G, ZHONG L D. Highway capacity manual[M]. Beijing: China Communications Press, 2017. (in Chinese)
    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.
    ITO T, TAKATA A, OOSAWA K. Time required for take-over from automated to manual driving[J]. SAE Technical Paper, 2016, 158(4): 1-6.
    林庆峰, 王兆杰, 鲁光泉. 城市道路环境下自动驾驶车辆接管绩效分析[J]. 中国公路学报, 2019, 32(6): 240-247. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906025.htm

    LIN Q F, WANG Z J, LU G Q. Analysis of take-over performance for automated vehicles in urban road environments[J]. China Journal of Highway and Transport, 2019, 32(6): 240-247. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906025.htm
    冯忠祥, 李靖宇, 张卫华, 等. 面向人机共驾车辆的驾驶人风险感知研究综述[J]. 交通信息与安全, 2022, 40(2): 1-10. doi: 10.3963/j.jssn.1674-4861.2022.02.001

    FENG Z X, LI J Y, ZHANG W H, et al. A reviewon driver's perception of risk associated with autonomous driving under human-computer shared control[J]. Journal of Transport Information and Safety, 2022, 40(2): 1-10. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.02.001
    郭应时. 交通环境及驾驶经验对驾驶员眼动和工作负荷影响的研究[D]. 西安: 长安大学, 2009.

    GUO Y S. Study on effects of traffic environment and driving experience on driver's eye movement and workload[D]. Xi'an: Chang'an University, 2009. (in Chinese)
    郭子慧, 郭伟伟, 谭墍元. 驾驶员接管自动驾驶车辆的眼动特性和行为分析[J]. 中国安全科学学报, 2022, 32(1): 65-71. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK202201009.htm

    GUO Z H, GUO W W, TAN X Y. Analysis on eye movement characteristics and behavior of drivers taking over automated vehicles[J]. China Safety Science Journal, 2022, 32 (1): 65-71. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK202201009.htm
    梅家林, 杜志刚, 郑号染, 等. 不同时段特长隧道入口区域视觉负荷研究[J]. 中国安全科学学报, 2021, 31(6): 176-181. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK202106025.htm

    MEI J L, DU Z G, ZHENG H R, et al. Research on visual load at entrance area of extra-long tunnel in different periods[J]. China Safety Science Journal, 2021, 31(6): 176-181. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK202106025.htm
    鲁光泉, 赵鹏云, 王兆杰, 等. 自动驾驶中视觉次任务对年轻驾驶人接管时间的影响[J]. 中国公路学报, 2018, 31(4): 165-171. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201804021.htm

    LU G Q, ZHAO P Y, WANG Z J, et al. Impact of visual secondary task on young drivers' take-over time in automated driving[J]. China Journal of Highway and Transport, 2018, 31(4): 165-171. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201804021.htm
    李鹏辉, 廖呈玮, 郑志晓, 等. 认知分心对车辆跟驰过程操控安全性的影响[J]. 中国公路学报, 2018, 31(5): 167-173. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201805021.htm

    LI P H, LIAO C W, ZHENG Z X, et al. Impact of cognitive distraction on vehicle control safety in car-following situation[J]. China Journal of Highway and Transport, 2018, 31 (5): 167-173. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201805021.htm
    徐超. 基于虚拟驾驶的切换型人机共驾行为评价[D]. 长春: 吉林大学, 2018.

    XU C. Evaluation of cooperative driving behavior based on the virtual driving[D]. Changchun: Jilin University, 2018. (in Chinese)
    赵微, 林健, 王树芳, 等. 变异系数法评价人类活动对地下水环境的影响[J]. 环境科学, 2013, 34(4): 1277-1283. https://www.cnki.com.cn/Article/CJFDTOTAL-HJKZ201304011.htm

    ZHAO W, LIN J, WANG S F, et al. Influence of human activities on groundwater environment based on coefficient variation method[J]. Environmental Science, 2013, 34(4): 1277-1283. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HJKZ201304011.htm
    HAO R, YANG H, ZHOU Z. Driving behavior evaluation model base on big data from internet of vehicles[J]. International Journal of Ambient Computing and Intelligence, 2019, 10(4): 78-95.
  • 加载中


    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(5)  / Tables(11)

    Article Metrics

    Article views (219) PDF downloads(30) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint