An Automated Pavement Marking Retroreflectivity Condition Assessment Method Using Mobile LiDAR and Video Log Images

Abstract

Pavement markings are a key transportation asset and traffic control device that facilitate safe and predictable driving. The effectiveness of pavement markings is dependent on their condition, particularly during nighttime and adverse weather. The Federal Highway Administration (FHWA) has developed regulations to guide minimum pavement marking retroreflectivity levels, which poses a potential challenge to public agencies because the current practice of visual inspection is labor intensive and the results can be subjective. To address the identified challenges and needs of public agencies, the objective of this research is twofold: (1) to serve as a proof of concept for the use of mobile light detection and ranging (LiDAR) to locate and assess the pavement markings for selected testing sections by developing and evaluating new automated LiDAR processing algorithms, and (2) to investigate the feasibility of identifying the deterioration trend of the retroreflectivity condition. This study developed a complete pavement marking inventory with retroreflectivity conditions for the 14 selected testing sections and also compared historical and current data to inform the deterioration trends of three types of marking materials, including polyurea, epoxy, and thermoplastic. The findings of this study will guide future phases with a larger selection of testing sections, material types, and roadway characteristics. The outcomes of the series of studies will help better define the benefit-to-cost ratio for different marking materials and eventually lead to the development of public agencies' pavement marking standards.