Volume 22 Issue 3
Jun.  2022
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2022  >  22(3): 263-276
ZHAO Tong, SHANGGUAN Wei, CHAI Lin-guo, GUO Peng. Scenario factor analysis and test case generation for vehicle-infrastructure cooperative mixed traffic[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 263-276. doi: 10.19818/j.cnki.1671-1637.2022.03.021
Citation: ZHAO Tong, SHANGGUAN Wei, CHAI Lin-guo, GUO Peng. Scenario factor analysis and test case generation for vehicle-infrastructure cooperative mixed traffic[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 263-276. doi: 10.19818/j.cnki.1671-1637.2022.03.021
shu

Scenario factor analysis and test case generation for vehicle-infrastructure cooperative mixed traffic

doi: 10.19818/j.cnki.1671-1637.2022.03.021
  • 1.

    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

  • 2.

    State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

  • 3.

    CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd., Tianjin 300300, China

Funds:

National Key Research and Development Program of China 2018YFB1600600

Science and Technology Research and Development Program of China Railway N2021G045

More Information
  • Author Bio:

    ZHAO Tong(1996-), male, doctoral student, Tong_Zhao@bjtu.edu.cn

    SHANGGUAN Wei(1979-), male, professor, PhD, wshg@bjtu.edu.cn

  • Received Date: 2021-12-12
  • Publish Date: 2022-06-25
    Abstract
  • Considering the test requirements of multi-vehicle efficiency and single-vehicle safety scenarios in a mixed traffic environment, a test case generation method for the vehicle-infrastructure cooperation was developed based on the scenario factor analysis of mixed traffic. For higher diversity and coverage of test cases, the interaction mechanism of mixed traffic characteristic factors was analyzed, the hierarchical model of mixed traffic scenario factors was constructed, and the consistency description index of the importance of scenario factors was proposed. On this basis, a complexity model of test cases was built. For the simulation and test of multi-vehicle efficiency scenarios, a complexity-inspired generation combination test case method was proposed, and a combination strategy with strong coupling of scenario factors was designed. For the simulation and test of single-vehicle safety scenarios, a Monte Carlo test case generation method based on the complexity clustering was put forward, and a sampling mechanism of characteristic parameters of risk scenarios was designed. Typical scenarios of vehicle-infrastructure cooperative mixed traffic were selected for simulation experiments, to verify the effectiveness of the proposed test case generation method. Research results show that for the ramp-merging scenario test of expressways in mixed traffic of multi-vehicle efficiency, compared with the traditional pairwise test method, the proposed method improves the maximum complexity of scenarios, proportion of high-complexity scenarios, and coverage of test cases by 11.93%, 60.02%, and 12.08%, respectively. For the vehicle-infrastructure cooperative lane-changing warning scenario test of single-vehicle safety, compared with the traditional Monte Carlo test method, the proposed method raises the number of dangerous scenarios by 195%, with reducing the parameter estimation error by 5.95%, and increases the number of high-risk scenarios by 119%, with reducing the parameter estimation error by 4.78%. Therefore, the proposed method can improve the diversity and coverage of test cases, contribute to carry out the functional test of the vehicle-infrastructure cooperative system under complex environments and risk conditions, and can effectively meet the test requirements of multi-vehicle efficiency and single-vehicle safety scenarios. 6 tabs, 11 figs, 34 refs.

     

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    • References(34)
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