This paper presents a novel dynamic lane-changing trajectory planning (DLCTP) model for autonomous vehicle (AV) running in the mixed traffic environment. The proposed model fully considers the dynamics of surrounding human-driven vehicles and can work on both straight and curved roads. The first step of the DLCTP model is to decide when and where to make the lane change based on the car-following model and safety constraints. Upon decision-making, an optimal lane-changing trajectory that accounts for safety, comfort, and efficiency is generated at each time step until the lane-changing procedure is completed. CarSim-Simulink based simulation platform and three typical traffic scenarios are applied to validate the proposed DLCTP model. Experimental results show that the proposed DLCTP model can generate smooth, safe, and comfort trajectories even in complex traffic situations. The proposed DLCTP model can be employed directly on real AVs because it is easy to implement and can adapt to complex traffic environments.