Volume 22 Issue 4
Aug.  2022
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2022  >  22(4): 348-360
SHANGGUAN Wei, ZHA Yuan-yuan, FU Yao, ZHENG Si-fa, CHAI Lin-guo. Vehicle-infrastructure cooperative credible interaction method based on traffic business characteristics understanding[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 348-360. doi: 10.19818/j.cnki.1671-1637.2022.04.027
Citation: SHANGGUAN Wei, ZHA Yuan-yuan, FU Yao, ZHENG Si-fa, CHAI Lin-guo. Vehicle-infrastructure cooperative credible interaction method based on traffic business characteristics understanding[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 348-360. doi: 10.19818/j.cnki.1671-1637.2022.04.027
shu

Vehicle-infrastructure cooperative credible interaction method based on traffic business characteristics understanding

doi: 10.19818/j.cnki.1671-1637.2022.04.027
  • 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.

    China Mobile Research Institute, Beijing 100053, China

  • 4.

    School of Vehicle and Mobility, Tsinghua University, Beijing 100084, 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:

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

  • Received Date: 2021-12-12
    Available Online: 2022-10-08
  • Publish Date: 2022-08-25
    Abstract
  • For the credible information interaction in vehicle-infrastructure cooperative environments, the processes of vehicle-vehicle and vehicle-infrastructure cooperative information interaction and the interaction requirements of different modes were analyzed, and a vehicle-infrastructure cooperative credible interaction framework was designed. A model of vehicle behavior state deduction and one of path perturbation factor quantification were constructed. A credibility calculation method for the vehicle object and level evaluation rules were designed. The credible authentication of vehicle object behavior was thereby achieved. A quantification model for the message urgency was built by understanding the effective traffic business characteristics. The low-resolution filtering strategy was used to preliminarily filter the message, and the message content was deeply understood on the basis of the support vector machine (SVM), thereby obtaining a multi-resolution interactive content cognition method. The Veins with OMNeT++ and SUMO simulators was used to build a simulation test environment. Simulation tests were carried out in open roads and intersection scenarios with different penetration rates of connected and automated vehicles (CAVs). The proposed vehicle-infrastructure cooperative credible interaction method was tested and verified. Research results show that the credibility identification for the vehicle-infrastructure cooperative information interaction can be effectively improved by understanding the traffic business characteristics. The average cognitive accuracy for the beacon location message achieved by the proposed method is 90.91%. It is 8.68% higher than that of the credible interaction method based on the timeliness detection. In the credible interaction verification experiment on the safety efficiency message, as the proportion of malicious vehicles increases, the traditional vehicle-infrastructure cooperative credible interaction method based on the voting mechanism is gradually held invalid. In contrast, an average accuracy of 94.96% is achieved by the proposed method under the condition that the single authentication delay is less than 13 ms. It is 3.05% higher than that of the traditional method based on the back propagation (BP) neural network. Moreover, a higher accuracy rate and a lower false negative rate of the credible interaction detection results can be obtained with a higher CAV penetration rate. Therefore, the needs of vehicle-infrastructure cooperative credible interaction can be met by the proposed method.

     

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