Volume 41 Issue 4
Aug.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(4): 14-23
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
shu

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
  • 1.

    College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China

  • 2.

    Research Institute for Road Safety of Ministry of Public Security, Beijing 100062, China

  • Received Date: 2022-07-21
    Available Online: 2023-11-23
    Abstract
  • 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.

     

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