Obstacle detection is an essential technique for intelligent vehicles. Environmental sensing especially plays a vital role to achieve accurate obstacle detection. Unlike classical 2D scan, emerging 3D Light Detection and Ranging (LiDAR) sensors can scan dense point cloud at one time, which represents detailed information of urban environments. The downside of obstacle detection using 3D LiDAR, on the other hand, is its computational cost posed by a large amount of 3D data. The virtual scan (VScan), first introduced by Petrovskaya et al. [1] for efficient vehicle detection and tracking, is a 2D compression of 3D point cloud to represent free space, obstacles and unknown areas. To overcome the computational problem of obstacle detection using 3D LiDAR, therefore, VScan is suitable. In addition, it can bridge across new-born 3D LiDAR sensors and many matured applications based on 2D scan, including occupancy grid map, SLAM, planning, detection, and tracking, due to its 2D representation of 3D point cloud.