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Car following trajectory planning of CAVs: An improved APF model with considering the stochasticity of HDVs

Author

Listed:
  • Zhao, Jiandong
  • Yu, Zhixin
  • Jiang, Rui
  • Wu, Di
  • Zheng, Shiteng

Abstract

This paper considered the stochasticity of Human-Driven Vehicles (HDVs) and proposed an improved Artificial Potential Field (APF) method for car-following trajectory planning of Connected and automated vehicles (CAVs) based on real mixed traffic flow experiment. Firstly, the heterogeneity between HDVs and CAVs was considered to determine the type of attractive field. Then to adapt the APF model to dynamic traffic environments, the field functions were improved by incorporating the speed and position differences. In addition, taking into account both the vehicle itself and its impact on traffic flow, Grey Wolf Optimization-Chaos (GWO-C) was proposed to calibrate parameters, which helps avoid local optima. Furthermore, the proposed model was compared with experimental data and original APF method. The results show that the proposed APF improves the speed, jerk, and speed standard deviation of the platoon. Finally, the impact of different CAVs’ market penetration rates (MPR) on the stability and fundamental diagram were explored. It was found that traffic stability and capacity can be enhanced by CAVs, with a more significant impact at higher MPR.

Suggested Citation

  • Zhao, Jiandong & Yu, Zhixin & Jiang, Rui & Wu, Di & Zheng, Shiteng, 2025. "Car following trajectory planning of CAVs: An improved APF model with considering the stochasticity of HDVs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 658(C).
    • Handle: RePEc:eee:phsmap:v:658:y:2025:i:c:s037843712400774x
    DOI: 10.1016/j.physa.2024.130265
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    References listed on IDEAS

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    1. Zhao, Jiandong & Shen, Jin & Yu, Zhixin & Gao, Yuhang & Jiang, Rui, 2024. "Exploration on relation between vehicle oscillation type and platoon oscillation evolution based on multi-scenario field experiment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    2. Qinglei Zhang & Weixin Hu & Jianguo Duan & Jiyun Qin, 2021. "Cooperative Scheduling of AGV and ASC in Automation Container Terminal Relay Operation Mode," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-18, January.
    3. Yu, Chunhui & Feng, Yiheng & Liu, Henry X. & Ma, Wanjing & Yang, Xiaoguang, 2018. "Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 89-112.
    4. Pengwei Wang & Song Gao & Liang Li & Binbin Sun & Shuo Cheng, 2019. "Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm," Energies, MDPI, vol. 12(12), pages 1-14, June.
    5. Zhou, Yang & Ahn, Soyoung & Wang, Meng & Hoogendoorn, Serge, 2020. "Stabilizing mixed vehicular platoons with connected automated vehicles: An H-infinity approach," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 152-170.
    6. Vranken, Tim & Sliwa, Benjamin & Wietfeld, Christian & Schreckenberg, Michael, 2021. "Adapting a cellular automata model to describe heterogeneous traffic with human-driven, automated, and communicating automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
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    1. Zhang, Zhishun & Xu, Ting & Easa, Said M. & Hui, Shouwei & Lai, Xinhe, 2026. "Impact analysis of driving stochasticity on heterogeneous traffic flow stability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 681(C).

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