Abstract Manager

Pavement rutting is a common but critical form of deterioration in road surfaces that result in an uncomfortable ride for drivers while exposing them to potential safety risks including hydroplaning and loss of skid resistance. Transportation agencies periodically inspect and monitor road conditions for pavement distresses and plan rehabilitation procedures accordingly, but this routine process is labor-intensive, time-consuming, and prohibitively expensive. A significant amount of research was conducted to automate pavement crack detection, but this is not the case for equally important distresses such as permanent deformation and pavement roughness as measured by the international rutting index (IRI). Efforts to automate rutting inspection processes were previously tackled using both pixel and point cloud methods. Cross-section pavement profiles are commonly used from mobile/terrestrial laser scans, and computational geometry techniques are employed to determine the rutting severity. However, only using cross-section profiles of the pavement rut and generating images from point cloud data may not provide a complete picture of the extent and distribution of distresses across the entire pavement. To capture the complete surface geometry of the pavement and better assess road conditions, this research proposes a tile-based novel method for measuring and quantifying pavement rutting, roughness, and potholes using point cloud data by using road surface fitting and computational geometry algorithms to identify distresses in new point cloud pavement sections and qualitatively measure its severity. The effectiveness of this method is demonstrated through experiments on real-world pavement point cloud data. Overall, our method offers a fast and reliable way to measure pavement distresses using point clouds, providing a more comprehensive view of the condition of the pavement surface compared to traditional methods. The ability to extract IRI and rutting information from point cloud pavement data is critical for entities looking to fully utilize mobile LiDAR datasets for pavement distress analysis to evaluate the condition of pavements and help determine the need for maintenance or rehabilitation.