The path planning for autonomous vehicles is a hot topic in academic world.The goal of this problem is to design a vehi-cle with learning ability to approach the target without any collision.In this paper,we focus on data-driving reinforcement learning(RL)design for the path planning for autonomous vehicles problems.We proposed the method of sensor detection,and a self-learning strategy for a vehicle seeking the target with obstacle avoidance.Specifically,we designed a continuous reinforcement signal to improve the system s preferential decision between the target seeking and the obstacle avoidance.To verify the learning ability of our strategy,we developed an in-door environment with different experiments.The simulation results show that the RL presents an effective learning ability for the path planning for autonomous vehicles problems.