Dynamic Lane-changing Trajectory Planning Model for Intelligent Vehicle Based on Quadratic Programming

To realize automated lane-changing maneuvers for intelligent vehicles and meet safety, comfort, and real-time requirements, a lane-changing trajectory planning model for dynamic environments was proposed. The model consisted of a road plane curve characterization module, a path generation module, and a velocity profile generation module. First, in the road plane curve characterization module, the Chebyshev polynomial interpolation method was used to fit the continuous and derivable surrounding road curve, which enabled the universality of the proposed model in various road plane alignments. Then, in the path generation module, according to the initial traffic states of the intelligent vehicle, a series of polynomial equations were established to generate a lane-changing path connecting the initial position and the candidate target positions, in which the equation parameters were solved using the Newton iteration method. Finally, in the velocity profile generation module, considering the collision avoidance, car-following acceleration, and vehicle motion state constraints, a quadratic programming problem was constructed to generate velocity profiles along the lane-changing path. Moreover, considering the surrounding dynamic traffic environments, the proposed model could be applied repeatedly in each time step to update the lane-changing trajectory until the intelligent vehicle reached the target position. The simulation results show that by employing the proposed lane-changing trajectory planning model, the intelligent vehicle can effectively avoid collisions with surrounding moving vehicles and successfully complete the lane-changing maneuver. Simultaneously, based on the quadratic programming framework, the model optimization time is significantly shortened, which meets the requirements for real-time planning and the effectiveness of trajectory planning. © 2021, Editorial Department of China Journal of Highway and Transport. All right reserved.