Decision making and motion planning algorithms play a crucial role in determining the safety and handling stability of autonomous vehicles, particularly on highway curves. Addressing safety and driving efficiency concerns in the decision-making process for highway lane changes, this study proposes a driving dissatisfaction decision algorithm based on the relative driving dissatisfaction of the ego-vehicle compared to the preceding vehicle. To improve the real-time performance of the planning algorithm, a path-speed decoupling framework is adopted for lane change trajectory planning. The path planning utilizes the quintic polynomial curve, incorporating four path evaluation indicators that considered safety, comfort and efficiency to achieve optimal path planning. Speed planning involves obtaining a smooth speed curve through a combination of dynamic programming and quadratic programming optimization. Simulation results show that the lane change decision model based on driving dissatisfaction can choose a more efficient and safer driving mode. In typical lane changing scenarios, both the maximum centroid sideslip angle and maximum yaw rate of the ego-vehicle are small, indicating that the lane change trajectory planning algorithm can ensure the safety and handling stability of the ego-vehicle during the lane change process.