Mobile Light Detection and Ranging for Automated Pavement Friction Estimation

Abstract

Rapid detection and maintenance of pavement friction is essential for roadway crash prevention. Traditional measurement methods are time-consuming, labor-intensive, and inefficient. This paper proposes a new method to rapidly estimate pavement friction by using a light detection and ranging (LiDAR) sensor. Eight parameters are developed to capture the texture and material information of pavement. The British pendulum number is adopted as the reference of pavement friction with the data collection and processing approaches stated. An ordered logit regression model is utilized to estimate the level of pavement friction, with an average accuracy of 75.86%. The model shows that both textures and material information contribute to pavement friction. Some experimental tests are conducted to explore the potential impact of illumination, showing that lighting and road shading do not affect measurements. The proposed LiDAR-based method is able to assist for rapid, economical, and automatic estimation of pavement friction.