When multiple autonomous vehicles perform lane change and merging tasks on structured road， steering and merging actions need to be comprehensively considered to avoid potential accidents. Meanwhile， the changing road curvature and surrounding vehicle speed also increase the difficulty of cooperative control. Focusing on the above issues， this paper proposes a multi-vehicle lane change gaming motion planning and cooperative control method facing variable curvature road. Firstly， a multi-vehicle model in curvature coordinate system is developed to determine the inter-vehicle safety distance and dynamics state. Then， by systematically considering the road curvature variation and surrounding vehicle information， a game-based multi-vehicle lane change motion planning algorithm is proposed， which uses a distributed framework to quickly solve the optimal speed trajectory and lane change timing considering personalized driving. Finally， the road curvature and planning trajectory are identified effectively based on B-sample curve， and an adaptive time-varying model predictive control algorithm is constructed to achieve trajectory tracking. Specifically， the control parameters are updated in real time under the single-step prediction domain to eliminate the control deviations caused by frequently various vehicle speed and curvature. The co-simulation results show that the proposed method can reduce the tracking error by 58% compared to the Stanley method， with reduction of the merging time by 74% compared to the cooperative adaptive cruise control method. Moreover， the computational solution efficiency is only 10% of the centralized method.
