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Modeling lane-changing behavior of connected autonomous vehicles based on molecular force fields

Author

Listed:
  • Qu, Dayi
  • Yang, Yuxiang
  • Wang, Kedong
  • Cui, Shanning
  • Chen, Yicheng

Abstract

Connected autonomous vehicles (CAVs) play a pivotal role in intelligent transportation systems, where accurate modeling of lane-changing behavior is essential for ensuring traffic safety and efficiency. To overcome the limitations of existing approaches in capturing dynamic multi-vehicle interactions during lane-change maneuvers, this study introduces a novel lane-changing decision-making model grounded in molecular force field theory. The study begins by analyzing the dynamic characteristics of lane-changing behavior and derives the lane-change expected safety distance. Drawing inspiration from molecular interactions, vehicle behaviors are analogized as attractive and repulsive forces, and a decision-making framework is formulated using the Buckingham potential function. Simulation experiments conducted in the SUMO environment demonstrate that the proposed model reduces risky lane changes by 45 %, increases average vehicle speed by 3.38 %, and improves the average number of successful lane changes by 5.12 %. Compared to traditional models, the proposed approach significantly enhances the safety, efficiency, and comfort of lane-changing behavior. These findings offer a new theoretical foundation for cooperative lane-changing strategies among CAVs in intelligent connected traffic environments.

Suggested Citation

  • Qu, Dayi & Yang, Yuxiang & Wang, Kedong & Cui, Shanning & Chen, Yicheng, 2025. "Modeling lane-changing behavior of connected autonomous vehicles based on molecular force fields," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 675(C).
    • Handle: RePEc:eee:phsmap:v:675:y:2025:i:c:s0378437125005084
    DOI: 10.1016/j.physa.2025.130856
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    References listed on IDEAS

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    1. Kou, Yukang & Ma, Changxi, 2023. "Dual-objective intelligent vehicle lane changing trajectory planning based on polynomial optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    2. 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.
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    6. Li, Xiang & Sun, Jian-Qiao, 2017. "Studies of vehicle lane-changing dynamics and its effect on traffic efficiency, safety and environmental impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 41-58.
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