Aiming at the safety and comfort problems of intelligent vehicles in highway lane-changing scenarios, a trajectory planning method based on safety potential field and polynomial lane-changing model is proposed. Firstly, the motion of the vehicle is decoupled into horizontal and vertical dimensions in the Frenet coordinate system, and the horizontal d-t trajectory cluster and the longitudinal s-t trajectory cluster of the vehicle are generated by the fifth-order polynomial and the fourth-order polynomial. Secondly, in order to improve the efficiency of the algorithm, a trajectory evaluation index including acceleration, acceleration change rate and curvature is designed according to the vehicle dynamics characteristics, and the candidate trajectory is obtained after the initial screening of the trajectory cluster. Finally, based on the safety potential field theory and the concept of minimum driving safety distance, a trajectory evaluation function including safety, comfort and efficiency is established to select the optimal trajectory for the candidate trajectory and complete the simulation verification. The algorithm is simulated and verified by building a high-speed two-lane curve model and designing different lane changing scenarios of uniform traffic flow and variable-speed traffic flow. The results show that in the process of lane changing, the collision risk value between the self-vehicle and each obstacle vehicle is always less than the critical value of collision risk, which ensures the safety of lane changing. Under different driving conditions, the acceleration, acceleration change rate and trajectory curvature of the self-driving car are all less than the threshold, indicating that the lane change trajectory planning algorithm can ensure the comfort and smoothness of the lane change trajectory of the self-driving car in a variety of obstacle traffic flows.