Road intersection is one of the most important parts of road network, and the positioning and information extraction of the road intersections constitute the basis of road network status monitoring. The traditional image-based research on this problem is so insufficient that there exist such weaknesses as the low detection rate and the need of manipulation by professional workers, which lead to low-level efficiency. In this paper, the authors proposed a method for road intersection extraction from airborne LiDAR point cloud. First, the coarse detection methods based on angular texture signature(ATS)analyzing and density-based spatial clustering with noise(DBSCAN)algorithm were performed to determine the ROI. Then, road edge points were extracted using circular profile in combination with elevation value validating, and the parallel edge lines were fitted. At last, the active Snake function with the"Ziplock Snake"way energy minimization was used to extract the road intersection contour. The experimental results show that the method presented in this paper has high detection rate and accuracy.