In the process of driverless obstacle detection for new energy vehicles, it is difficult to collect data due to various obstacles on the road and complex weather such as rain, snow and haze, resulting in low accuracy of driving obstacle detection. To this end, a method for detecting obstacles in unmanned driving of new energy vehicles using onboard LiDAR is proposed. Adopting a horizontal installation method, two 32 line LiDARs are symmetrically arranged on unmanned vehicles to collect 3D point cloud data of road structure and synchronize the data with coordinate conversion. A discrete point filtering algorithm based on statistical features calculates the standard deviation and global distance average of point cloud datasets to remove discrete noise points. Using Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to achieve obstacle detection for autonomous driving of new energy vehicles. The experimental results show that the proposed method has high noise resistance, an average recognition distance of 82 m, obstacle detection accuracy of over 92%, and the highest false detection rate of 1.8%. The detection time is only 1.09 ms.