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A cooperative lane change control strategy for cooperative adaptive cruise control platoons with insufficient headway gaps

Author

Listed:
  • Yin, Yanduo
  • Gao, Zhibo
  • Long, Kejun
  • Fei, Yi

Abstract

Cooperative Adaptive Cruise Control (CACC) platoons contribute to enhancing road traffic safety and efficiency. However, creating suitable headway gaps for CACC platoons to change lanes is a challenge. This paper proposes a cooperative lane change control strategy for CACC platoons in mixed traffic environments where suitable headway gaps on the target lane are insufficient. Firstly, a cooperative longitudinal and lateral control strategy for CACC platoons is designed. Once longitudinal control ensures that the headway gap on the target lane meets the criteria, the vehicles are controlled to complete the lane change and resume cruising in their original formation. To achieve the longitudinal control objectives of gap generation by cooperative deceleration before lane change and cruising after lane change, a longitudinal cooperative control strategy that can independently create the lane change gap is proposed based on an adaptive weight model predictive control. Finally, the lane change headway gaps in the proposed strategy are generated sequentially for each vehicle, a contrast experiment is designed where the lane change headway gap is generated once and vehicles change lanes simultaneously. The effectiveness of the proposed strategy is validated through numerical simulation combining CarSim and Matlab/Simulink. The results show that the CACC platoon completed the lane change successfully under the control of the proposed strategy. Compared to the contrast simulation, in both low-speed and high-speed highway scenarios, the average speed standard deviation under the proposed strategy is reduced by 48.3 % and 39.9 % respectively, while the average comfort index decreases by 11.7 % and 5.9 %.

Suggested Citation

  • Yin, Yanduo & Gao, Zhibo & Long, Kejun & Fei, Yi, 2024. "A cooperative lane change control strategy for cooperative adaptive cruise control platoons with insufficient headway gaps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 655(C).
  • Handle: RePEc:eee:phsmap:v:655:y:2024:i:c:s0378437124006848
    DOI: 10.1016/j.physa.2024.130175
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    References listed on IDEAS

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    1. Yao, Zhihong & Wang, Yi & Liu, Bo & Zhao, Bin & Jiang, Yangsheng, 2021. "Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway," Energy, Elsevier, vol. 230(C).
    2. Zhang, Hanyu & Du, Lili & Shen, Jinglai, 2022. "Hybrid MPC System for Platoon based Cooperative Lane change Control Using Machine Learning Aided Distributed Optimization," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 104-142.
    3. Jiang, Yangsheng & Tan, Li & Xiao, Guosheng & Wu, Yunxia & Yao, Zhihong, 2024. "Platoon-aware cooperative lane-changing strategy for connected automated vehicles in mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    4. Vranken, Tim & Schreckenberg, Michael, 2022. "Modelling multi-lane heterogeneous traffic flow with human-driven, automated, and communicating automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
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    2. Huang, Yu & Liu, Changqing & Yan, Shiyi & Qin, Yanyan & Wang, Hao, 2025. "Eco-driving strategy for connected automated vehicles at signalized intersections: A behavior-cloning approach in distributed model predictive control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 676(C).
    3. Li, Haijian & Meng, Weizhe & Han, ZongLin & Zhang, Zihan & Yang, Yanfang, 2025. "Vehicle platoon in road traffic: A survey of modeling, communication, controlling and perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
    4. Gong, Wang-Han & Zhang, Geng & Song, Bo-Yu, 2025. "Human-driving like lane-changing behavior of autonomous vehicles based on asymmetric risk field and reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 679(C).

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