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Car-following characteristics and model of connected autonomous vehicles based on safe potential field

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  • Jia, Yanfeng
  • Qu, Dayi
  • Song, Hui
  • Wang, Tao
  • Zhao, Zixu

Abstract

Aiming at the characteristics of connected and autonomous vehicle (CAV) which makes autonomous decision by perceiving the surrounding environment, a safe potential field model including lane marking potential field, road boundary potential field and vehicle potential field is established to describe the safe risk of CAV in the process of driving. In the process of building the safe potential field model, aiming at the defect that the existing vehicle potential field function has independent gravitational and repulsive expressions, a unified function of vehicle potential field based on Lennard-Jones potential is established by referring to the relationship of intermolecular interaction, and the parameter of vehicle’s acceleration is considered into the vehicle potential field model. The statistical analysis of the parameter reveals that the change of acceleration directly affects the distribution of vehicle potential field and reflect the dynamic trend of vehicle’s safe potential field under different driving states. Then, the safe potential field is applied to the car-following behavior of CAV, and the model’s parameters are calibrated by Shanghai natural driving dataset; Finally, compared with the existing classic IDM and VTH models, the simulation results show that: the model still has smoother response curves in the three car-following scenarios designed to improve the safety and efficiency, which verifies the effectiveness of the model. The research results can lay a theoretical foundation for decision making behavior of safe driving, and also provide a unique way for the research of CAVs’ safe technology.

Suggested Citation

  • Jia, Yanfeng & Qu, Dayi & Song, Hui & Wang, Tao & Zhao, Zixu, 2022. "Car-following characteristics and model of connected autonomous vehicles based on safe potential field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    • Handle: RePEc:eee:phsmap:v:586:y:2022:i:c:s0378437121007755
    DOI: 10.1016/j.physa.2021.126502
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    References listed on IDEAS

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    Cited by:

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    2. Qin, Yanyan & Liu, Mingxuan & Hao, Wei, 2024. "Energy-optimal car-following model for connected automated vehicles considering traffic flow stability," Energy, Elsevier, vol. 298(C).
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    4. Bari, Chintaman Santosh & Chandra, Satish & Dhamaniya, Ashish, 2022. "Service headway distribution analysis of FASTag lanes under mixed traffic conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    5. Hao Ma & Wenhui Pei & Qi Zhang, 2022. "Research on Path Planning Algorithm for Driverless Vehicles," Mathematics, MDPI, vol. 10(15), pages 1-14, July.
    6. Yin, Jiacheng & Li, Zongping & Cao, Peng & Li, Linheng & Ju, Yanni, 2023. "Car-following modeling based on Morse model with consideration of road slope in connected vehicles environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    7. Hu, Xu & Li, Dongshuang & Yu, Zhaoyuan & Yan, Zhenjun & Luo, Wen & Yuan, Linwang, 2022. "Quantum harmonic oscillator model for fine-grained expressway traffic volume simulation considering individual heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).

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