Automotive Innovation ›› 2025, Vol. 8 ›› Issue (1): 113-124.doi: 10.1007/s42154-024-00307-x

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Incorporating Head-to-Tail String Stability into Model Predictive Leading Cruise Control of Mixed Traffic by Control Matching

Xuan Wang1, Yilin Yang2, Yougang Bian1,3, Hongmao Qin1,3, Manjiang Hu1,3 & Rongjun Ding1,3   

  1. 1. State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, Hunan, China
    2. Beijing Megvii Co., Ltd, Beijing, 10587, China
    3. Wuxi Intelligent Control Research Institute of Hunan University, Wuxi, 214115, Jiangsu, China 
  • 出版日期:2025-02-18 发布日期:2025-04-08

Incorporating Head-to-Tail String Stability into Model Predictive Leading Cruise Control of Mixed Traffic by Control Matching

Xuan Wang1, Yilin Yang2, Yougang Bian1,3, Hongmao Qin1,3, Manjiang Hu1,3 & Rongjun Ding1,3   

  1. 1. State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, Hunan, China
    2. Beijing Megvii Co., Ltd, Beijing, 10587, China
    3. Wuxi Intelligent Control Research Institute of Hunan University, Wuxi, 214115, Jiangsu, China 
  • Online:2025-02-18 Published:2025-04-08

摘要: Leading cruise control (LCC) in mixed traffic has received wide attention as it can strengthen the capability of connected and automated vehicles in reducing traffic instability and smoothing mixed traffic. However, existing predictive LCC cannot directly address head-to-tail string stability (HSS) since velocity fluctuation of human-driven vehicles behind cannot be handled by constraint design in MPC framework. To address this challenge, this paper proposes a control matching MPC approach for LCC in mixed traffic. A head-to-tail string stable feedback controller based on the inverse optimal velocity model is designed to guarantee HSS under bilateral topologies. Then, an MPC controller is proposed and the weighting matrices in the objective function are tuned to match the MPC controller with the head-to-tail string stable feedback controller. Straightforward analysis of HSS and physical/safety constraints satisfaction are neatly combined by the proposed control scheme. The feasibility and closed-loop stability of the MPC controller are analyzed. Finally, simulations verify the effectiveness of the proposed controller.


Abstract: Leading cruise control (LCC) in mixed traffic has received wide attention as it can strengthen the capability of connected and automated vehicles in reducing traffic instability and smoothing mixed traffic. However, existing predictive LCC cannot directly address head-to-tail string stability (HSS) since velocity fluctuation of human-driven vehicles behind cannot be handled by constraint design in MPC framework. To address this challenge, this paper proposes a control matching MPC approach for LCC in mixed traffic. A head-to-tail string stable feedback controller based on the inverse optimal velocity model is designed to guarantee HSS under bilateral topologies. Then, an MPC controller is proposed and the weighting matrices in the objective function are tuned to match the MPC controller with the head-to-tail string stable feedback controller. Straightforward analysis of HSS and physical/safety constraints satisfaction are neatly combined by the proposed control scheme. The feasibility and closed-loop stability of the MPC controller are analyzed. Finally, simulations verify the effectiveness of the proposed controller.