To effectively extract road information from different environments for intelligent vehicles, a road information extraction method based on hypervoxel segmentation is proposed. Road information is mainly divided into road edge and lane line information. First, the non-ground point cloud is filtered according to either the point cloud elevation information or installation location of scanning system. Second, the point cloud is over-segmented using the voxel adaptive hypervoxel segmentation method, allowing the separate segmentation of road edge features. Third, the boundary point extraction algorithm and driving path of scanning system are used to complete the extraction of road edges, subsequently dividing the driving area according to the road edge information. Finally, lane lines are extracted using local adaptive threshold segmentation and spatial density filtering. Experimental results show that the extraction accuracy of the road edge height and road width are 92.6% and 98.4%, deviation degree of lane line is less than 4%, and maximum deviation distance is no more than 0.04 m.