| Accurate and robust perception of surrounding objects of interest, such as on- road obstacles, ground surface, curb and ditch, is an essential capability for path planning and localization in autonomous driving. Stereo cameras are of- ten used for this purpose. Comparably, 3D LiDARs directly provide accurate depth measurements of the environment without the need for association of pixels in image pairs. In this project, disparity is used to bridge the gap be- tween LiDAR and stereo cameras, therefore efficiently extracting the ground surface and obstacles from 3D point cloud in the way of 2D image processing. Given the extracted ground points, three kinds of features are designed to de- tect road structures with large geometrical variation, such as curbs, ditches and grasses. Based on the feature result, a robust regression method named least trimmed squares is used to fit the final road boundary. The proposed approach is verified with the real dataset from a 64-channel LiDAR mounted on Scania bus Klara, as well as the KITTI road benchmark, both achieving satisfying performances in some particular situations |