This paper proposes a road marking extraction algorithm based on a vehicle-mounted laser point cloud. Based on the ground attributes of road markings, CSF algorithm is used to obtain ground point clouds, eliminating the influence of nonground point clouds on the extraction of markings; according to the difference of normal vectors on both sides of the road, the area growth method is used to segment and extract road points from the ground point cloud, eliminating the interference of curbs and ground objects on both sides of the road; to speed up the processing efficiency of the algorithm, the pavement point cloud is projected to generate intensity feature images; for the uneven intensity distribution in the extraction of markings, the intensity feature images are divided into multiple subimage to reduce the incident angle and range of the image. To avoid misclassifying subimages that are all roads or markings, the proposed algorithm use the largest interclass variance to classify the subimages and select different thresholds based on the subimage category. Dynamic threshold segmentation is used by the calculation method. Finally, three actual road sections’ point cloud data are chosen for experimental verification in this paper. The results show that the recall rate, accuracy rate, and comprehensive evaluation index of road marking extraction are 92.8%, 96.8%, and 94.8%, respectively, suggesting that the algorithm in this paper is effective and feasible, has some practical application value, and supports the production of high-precision maps in unmanned driving.