Relative Flatness Analysis in Mobile Pavement Condition Monitoring Using Hybrid Solid-State LiDAR

Abstract

Given the need for more efficient and cost-effective periodic pavement monitoring across extensive road networks, this paper presents an innovative solution that leverages affordable solid-state Light Detection and Ranging (LiDAR) for mobile pavement condition monitoring. Although these sensors produce less precise point clouds and are primarily engineered for environmental perception rather than accurate measurement, this paper introduces a novel approach, relative flatness analysis (RFA), which adapts to these lower quality point clouds to quantify and visualize the relative roughness of road pavements. The proposed method has been systematically validated, offering valuable insights into the configuration of RFA parameters under varying data quality. Furthermore, the effectiveness of RFA has been demonstrated in both controlled and practical environments, showing great potential. Although the precision limitations of these LiDAR point clouds render them unsuitable for standard quality index computation, such as the International Roughness Index (IRI) or roughness index (RI), RFA can effectively capture the relative flatness of the pavement from this type of data, providing a novel solution for routine maintenance patrols.