Instance-Based Clustering of Road Markings with Wear and Occlusion from Mobile Lidar Data

Road markings are essential features to convey important information to various roadway users such as pedestrians, bicyclists, and motorists. Although mobile laser scanning (MLS) technology provides dense and spatially accurate data, automated identification of road markings remains a challenge. Although approaches have been developed to detect road markings from point clouds, prior studies have not thoroughly investigated road discretization and road marking clustering, which are essential for effective instance-level identification and management of road markings. To improve road discretization, our approach uses line segmentation to divide the trajectory of MLS data and fit circles to individual segments. By calculating the intersection between the road points and the circle’s center for each trajectory segment, this method ensures no overlap occurs when discretizing roads with sharp turns. To improve road marking clustering, the discretized points are rasterized onto a two-dimensional (2D) image and clustered using connected component labeling. Individual markings are skeletonized to detect junctions and corners, which enables the separation of underclustered road markings. Overclustered road markings are then merged using a rule-based approach optimized with a traffic line manual. The proposed approach was evaluated through extensive experiments using 28 MLS data sets containing 2,340 road marking clusters with complex geometry and wear. Out of these, only 64 instances (2.7%) were falsely clustered, achieving a precision rate of 97.5% and a recall rate of 99.7%.

Efficient road maintenance and improved road safety rely heavily on the clear visibility of road markings, which guide diverse users, such as pedestrians, cyclists, and drivers. Traditional methods for inspecting these markings are often time-consuming and prone to human error. In this study, we use MLS technology to collect comprehensive and precise data on roadways. We have developed an automated technique that significantly enhances the identification and management of road markings. This method accurately rasterizes and segments roadway lidar data—even in areas with sharp turns—to apply advanced image processing for the recognition of individual markings, including those that are substantially worn or have complex geometries. This simplifies the geometric data in a GIS database such that each line object is more representative of how someone would manually digitize the marking. Tested on numerous data sets, our approach achieved an accuracy rate of over 97% in clustering individual road markings. The developed approach not only streamlines roadway maintenance but also offers potential benefits for intelligent transportation systems. It contributes to safer autonomous vehicle navigation and supports informed decision-making regarding road infrastructure, ultimately improving all road users’ experience.