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Molecular Dynamics-Based Car-Following Safety Characteristics and Modeling for Connected Autonomous Vehicles

Author

Listed:
  • Kedong Wang

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
    Intelligent Manufacturing Institute, Qingdao Huanghai University, Qingdao 266427, China)

  • Dayi Qu

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Yiming Meng

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Tao Wang

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
    School of Artificial Intelligence and Big Data, Zibo Vocational Institute, Zibo 255314, China)

  • Ziyi Yang

    (School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China)

Abstract

To characterize the dynamic interaction properties of heterogeneous traffic flow in the complex human–vehicle–road environment and to enhance the safety and efficiency of connected autonomous vehicles (CAVs), this study analyzes the self-driven particle characteristics and safety interaction behavior of CAVs based on molecular interaction potential. The molecular dynamics of potential interaction functions are employed to establish a dynamic quantization model for car-following (CF) safety potential, referred to as the molecular force field quantization model. To calibrate the model parameters, the Artificial Bee Colony Algorithm and the highD dataset are utilized, subsequently validating the reasonableness and effectiveness of the molecular dynamics model for vehicle tracking. The simulation results demonstrate that the proposed model can more accurately fit actual CF data, significantly improving vehicle travel safety and efficiency. Moreover, the profile of vehicle acceleration shows a lower mean absolute error and root mean square error compared to actual data, indicating that the model provides superior anti-interference fluctuation resistance and stability in CF scenarios. Overall, the proposed model effectively captures the microscopic CF behavior and vehicle–vehicle safety interactions, offering a theoretical foundation for further research into vehicle-following dynamics.

Suggested Citation

  • Kedong Wang & Dayi Qu & Yiming Meng & Tao Wang & Ziyi Yang, 2024. "Molecular Dynamics-Based Car-Following Safety Characteristics and Modeling for Connected Autonomous Vehicles," Sustainability, MDPI, vol. 16(12), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:4903-:d:1410859
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    References listed on IDEAS

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    1. Tian, Junfang & Zhang, H.M. & Treiber, Martin & Jiang, Rui & Gao, Zi-You & Jia, Bin, 2019. "On the role of speed adaptation and spacing indifference in traffic instability: Evidence from car-following experiments and its stochastic model," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 334-350.
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