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A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory

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  • Li, Linheng
  • Gan, Jing
  • Zhou, Kun
  • Qu, Xu
  • Ran, Bin

Abstract

In order to adequately characterize the driving risks that vehicles face during the lane change process and ensure that vehicles perform safer lane change decisions, a vehicle lane change model based on the safe potential field theory is established in this paper. Firstly, the driving risk encountered during the vehicle lane-changing process is evaluated, and the spatial distribution of the safety potential field under different motion states during the vehicle driving process is given based on the potential field theory. Secondly, the critical distances between vehicles at the end of the lane-change process are summarized according to the distribution of different safety potential fields of relevant vehicles during the lane change process. Compared with the traditional critical distance calculation model, the method proposed in this paper can dynamically characterize the trend of the critical distance of the vehicle under different velocity and acceleration conditions. Based on this, according to the characteristics that various types of vehicle movement status can be perceived in real-time under the CAVs environment, the safety-critical time required for lane change under various motion states of the vehicle is summarized, and the minimum safety distance lane change model based on the safety potential field theory is finally established. Numerical simulation analysis of the model shows that the model can characterize the effects of various motion parameters on the lane change results. The research results can provide some theoretical support for related researches such as vehicle lane changing, vehicle autonomous driving, and vehicle group optimization control in the intelligent networked environment in the future.

Suggested Citation

  • Li, Linheng & Gan, Jing & Zhou, Kun & Qu, Xu & Ran, Bin, 2020. "A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    • Handle: RePEc:eee:phsmap:v:559:y:2020:i:c:s0378437120305410
    DOI: 10.1016/j.physa.2020.125039
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    References listed on IDEAS

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    1. Ma, Yanli & Zhang, Peng & Hu, Baoyu, 2019. "Active lane-changing model of vehicle in B-type weaving region based on potential energy field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
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    3. Gipps, P. G., 1986. "A model for the structure of lane-changing decisions," Transportation Research Part B: Methodological, Elsevier, vol. 20(5), pages 403-414, October.
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    Citations

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    Cited by:

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    4. Sun, Baofeng & Ma, Guodong & Song, Jia & Cheng, Zeyang & Wang, Wei, 2023. "Driving safety field modeling focused on heterogeneous traffic flows and cooperative control strategy in highway merging zone," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    5. Guo, Yingshi & Zhang, Hongjia & Wang, Chang & Sun, Qinyu & Li, Wanmin, 2021. "Driver lane change intention recognition in the connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
    6. Li, Linheng & An, Bocheng & Wang, Zhiyu & Gan, Jing & Qu, Xu & Ran, Bin, 2024. "Stability analysis and numerical simulation of a car-following model considering safety potential field and V2X communication: A focus on influence weight of multiple vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    7. Ai, Yi & Li, Yueyang & Han, Xun & Yao, Zhihong & Li, Zongping, 2024. "Real-time risk assessment method for multi-aircraft interaction based on potential field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    8. He, Yongming & Feng, Jia & Wei, Kun & Cao, Jian & Chen, Shisheng & Wan, Yanan, 2023. "Modeling and simulation of lane-changing and collision avoiding autonomous vehicles on superhighways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    9. Guo, Wenfeng & Song, Xiaolin & Cao, Haotian & Zhao, Song & Yi, Binlin & Wang, Jianqiang, 2023. "Human-centered driving authority allocation for driver-automation shared control: A two-layer game-theoretic approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    10. Yao, Zhihong & Gu, Qiufan & Jiang, Yangsheng & Ran, Bin, 2022. "Fundamental diagram and stability of mixed traffic flow considering platoon size and intensity of connected automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    11. Wang, Baojie & Li, Wei & Wen, Haosong & Hu, Xiaojian, 2021. "Modeling impacts of driving automation system on mixed traffic flow at off-ramp freeway facilities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    12. Xin Chang & Xingjian Zhang & Haichao Li & Chang Wang & Zhe Liu, 2022. "A Survey on Mixed Traffic Flow Characteristics in Connected Vehicle Environments," Sustainability, MDPI, vol. 14(13), pages 1-22, June.
    13. 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).
    14. 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).
    15. Kekun Zhang & Dayi Qu & Hui Song & Tao Wang & Shouchen Dai, 2022. "Analysis of Lane-Changing Decision-Making Behavior and Molecular Interaction Potential Modeling for Connected and Automated Vehicles," Sustainability, MDPI, vol. 14(17), pages 1-20, September.

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