Cooperative Lane‐Changing Control for CAVs at Freeway On‐Ramps considering Vehicle Dynamics

• Published in: Journal of advanced transportation Vol. 2024; no. 1
• Main Authors: Wang, Zhengwu, Xiang, Jian, Wang, Jie, Gao, Zhibo, Chen, Tao, Li, Hao, Mao, Rui
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 16.08.2024; Wiley
• Subjects: Acceleration; Analysis; Control algorithms; Controllers; Cooperative control; Decision making; Decomposition; Efficiency; Energy consumption; Errors; Headways; Highway access ramps; Lane changing; Linear quadratic regulator; Optimization; Polynomials; Predictive control; Roads & highways; Safety regulations; Speed control; Traffic accidents & safety; Traffic control; Traffic flow; Traffic speed; Trajectory control; Trajectory optimization; Vehicles; Velocity
• DOI: 10.1155/2024/1221717
• ISSN: 0197-6729

This study proposes a cooperative lane‐changing control framework for multiple vehicles in freeway ramp merging areas, aiming to achieve safe and efficient merging. Specifically, multiple connected and automated vehicles (CAVs) form triplets to participate in cooperative lane‐changing. The framework consists of two stages: Longitudinal Headway Adjustment (LHA) and Lane‐Changing Execution (LCE). In the LHA stage, a centralized longitudinal controller is developed based on the vehicle’s longitudinal dynamics model to optimize the longitudinal velocity of the cooperative vehicles and create suitable gaps for merging vehicles. In the LCE stage, an optimal lane‐changing reference trajectory is generated using a quintic polynomial and a lateral controller is designed based on the vehicle’s lateral dynamics model. Model Predictive Control (MPC) is utilized for trajectory tracking. The simulation results obtained using MATLAB/Simulink, GPOPS, and CarSim demonstrate that the proposed control strategy can be applied to different vehicle speed control scenarios. Taking a specific velocity combination as an example, the cumulative control errors in the longitudinal and lateral directions for PV (Preceding Vehicle), SV (Subject Vehicle), and FV (Following Vehicle) are 1.4014 m, 0.5631 m, and −0.7601 m, respectively, satisfying the safety distance requirements. Compared to the Linear Quadratic Regulator (LQR) control, the proposed strategy improves control efficiency by 145.03%, 69.64%, 43.18%, and 67.61% in terms of comprehensive spacing errors, synthesized acceleration, front wheel angle, and speed fluctuation, respectively. These research findings highlight the superior performance of the proposed control strategy in terms of traffic efficiency, comfort, safety, and vehicle stability.