Lane change obstacle avoidance is a common driving scenario for autonomous vehicles. However, existing methods for lane change obstacle avoidance in vehicles decouple path and velocity planning, neglecting the coupling relationship between the path and velocity. Additionally, these methods often do not sufficiently consider the lane change behaviors characteristic of human drivers. In response to these challenges, this paper innovatively applies the Dynamic Movement Primitives (DMPs) algorithm to vehicle trajectory planning and proposes a real-time trajectory planning method that integrates DMPs and Artificial Potential Fields (APFs) algorithm (DMP-Fs) for lane change obstacle avoidance, enabling rapid coordinated planning of both path and velocity. The DMPs algorithm is based on the lane change trajectories of human drivers. Therefore, this paper first collected lane change trajectory samples from on-road vehicle experiments. Second, the DMPs parameters are learned from the lane change trajectories of human drivers and the human-like lane change trajectories are planned. Meanwhile, the artificial potential field, which considers driver characteristics, is utilized to adjust the human-like lane change trajectory, ensuring that the vehicle can dynamically avoid obstacles in real-time during the lane change process. Finally, simulations and vehicle experiments were conducted in challenging scenarios with static and dynamic obstacles. The results indicate that the proposed DMP-Fs method exhibits high computational efficiency, strong generalization capabilities, and trackability of the planned trajectories. Furthermore, the DMP-Fs can actively and dynamically avoid obstacles in real-time built upon generating human-like lane change trajectories. The minimum distance between the vehicle and obstacles has been increased from 0.725 to 1.205 m, ensuring the vehicle's driving safety.