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Human–machine cooperative scheme for car-following control of the connected and automated vehicles

Author

Listed:
  • Chen, Jin
  • Sun, Dihua
  • Li, Yang
  • Zhao, Min
  • Liu, Weining
  • Jin, Shuang

Abstract

To address the out-of-the-loop problem of the automated driving, a human–machine cooperative scheme for car-following control of the connected and automated vehicles (CAVs) is proposed. The proposed scheme can keep the drivers always in the loop and improve the car-following performance. To be specific, the driving automation system (artificial driver) is assigned to the task of velocity tracking and the human driver is responsible for headway adjustment. For the velocity tracking task, a feedforward–feedback control strategy was designed firstly by considering the advantages of the accurate perception and communication of CAVs, then an H∞ suboptimal control method was developed to optimize the controller parameters according to the desired performance index, further the controller was fine-tuned based on the idea of human-simulated intelligent control (HSIC) to improve the dynamic performance of the velocity tracking. For the operator’s headway adjusting task, the stability analysis based on the Lyapunov function proved that the simple proportional feedback control can be assumed by the driver to ensure the system stability under the cooperation of automated velocity tracking. The experiments based on the driving simulator demonstrated that human–machine cooperative scheme for car-following can reduce the tracking error of vehicle distance effectively, and the human driver can be kept in the control loop with a smaller operating load.

Suggested Citation

  • Chen, Jin & Sun, Dihua & Li, Yang & Zhao, Min & Liu, Weining & Jin, Shuang, 2021. "Human–machine cooperative scheme for car-following control of the connected and automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    • Handle: RePEc:eee:phsmap:v:573:y:2021:i:c:s0378437121002211
    DOI: 10.1016/j.physa.2021.125949
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    References listed on IDEAS

    as
    1. Dihua Sun & Hui Liu & Geng Zhang & Min Zhao, 2015. "The new car following model considering vehicle dynamics influence and numerical simulation," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(07), pages 1-16.
    2. Sun, Yuqing & Ge, Hongxia & Cheng, Rongjun, 2019. "An extended car-following model considering driver’s memory and average speed of preceding vehicles with control strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 752-761.
    3. Wang, Qingying & Ge, Hongxia, 2019. "An improved lattice hydrodynamic model accounting for the effect of “backward looking” and flow integral," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 438-446.
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    5. Yao, Zhihong & Hu, Rong & Wang, Yi & Jiang, Yangsheng & Ran, Bin & Chen, Yanru, 2019. "Stability analysis and the fundamental diagram for mixed connected automated and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    6. Chen, Dong & Sun, Dihua & Zhao, Min & Zhou, Tong & Cheng, Senlin, 2018. "Modeling and analyses for an extended car-following model accounting for drivers’ situation awareness from cyber physical perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 52-68.
    7. Zhang, Geng & Sun, Di-Hua & Zhao, Min & Liao, Xiao-Yong & Liu, Wei-Ning & Zhou, Tong, 2018. "An extended car-following model accounting for cooperation driving system with velocity uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 1008-1017.
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