Path planning plays an important role in autopilot technology, and its algorithm will have a key impact on the performance of autopilot system. The most commonly used path planning algorithms, such as A^* algorithm and Dijkstra algorithm, are based on the shortest distance principle. However, in real cases, when vehicle in obstacle avoidance or over bending scenes, ride comfort needs to be considered. To solve this problem, a path planning method based on large amount human driving trajectories is proposed in this paper. It enables autopilot system to learn human driving behavior and independently plan a more suitable method according to human driving habits. First step, we use map data and the artificial driving trajectories as the system inputs, and that provides an artificial driving trajectory database for path planning. Large amount of Artificial driving trajectories and high precision map insure that one or more paths can be plan out based on the human driving habits. In the second step, the road is divided into segments, and the path set is constructed in the third step. By setting a weight for each driving habit, the optimal path can be selected out at the end. The roundabout road test supports its effectiveness during the real road testing process. According to this method, the planning path will lead to the outside of the roundabout road in advance, which ensures that the centrifugal force is smaller and that is safer and smoother when passing the roundabout. That is more consistent with the habit of artificial driving through the roundabout road.