Aiming at the problem of insufficient dynamic environment perception and estimation in the process of intelligent vehicle path planning, we design a frame based on environment perception prediction、behavior decision and control sequence generation with an intelligent vehicle path planning method based on MCPDDPG (Monte Carlo prediction deep deterministic policy cradient). The framework can realize a real-time decision-making and planning for intelligent vehicle, and output continuous vehicle control sequences. Firstly, we use sequential Monte Carlo to estimate the behavioral state of other cars; Then, we design a behavioral decision method based on reinforcement Q learning to enable intelligent vehicles to predict collision risks in real time and adopt reasonable avoidance strategies; Finally, we build a deep deterministic policy gradient learning network to obtain the optimal trajectory sequence of the intelligent vehicle planning path. Experimental results show that the proposed method can alleviate the problem of insufficient prediction of environmental perception, improve the speed of intelligent vehicle behavior decision-making, ensure the active safety of path planning, and output a continuous trajectory sequence, which provides a prerequisite for intelligent vehicle navigation control.