Mobile LiDAR Deployment Optimization: Towards Application for Pavement Marking Stained and Worn Detection

Abstract

Clear and distinctive pavement markings play a critical role in providing traffic information and avoiding traffic crashes at night and in adverse weather conditions. Periodically inspecting pavement marking is essential but requires vast human and material resources currently. This paper attempts to use the low-channel LiDAR sensor to detect and evaluate the stain, wear, and cracks of pavement marking based on the research of Pike and Che that the retroreflectivity of pavement markings is highly correlated with the laser intensity of LiDAR. Thus, the deployment location of mobile LiDAR based on its built-in characteristics and mechanical structure is optimized. An optimization model is formed and solved with the elitist preservation genetic algorithm (EGA). The results show that the best installation height is 0.5 m. The rotation angle of LiDAR is 128.4°, 148.0° in the urban and highway scenarios, respectively. The field test results show that the pavement marking can obtain best the coverage, density, and effective use of LiDAR point cloud with the above setting. The field test’s mean relative error (MRE) is less than 5% and 10% in vertical installation and vertical installation with an inclination, respectively.