Pavement markings are produced with retroreflective materials to enhance visibility for motorists, particularly at night. Retroreflectivity evaluation throughout an extensive highway network for maintenance and asset management purposes is a critical, yet challenging task for transportation agencies because visual evaluation can often be subjective and inconsistent, while field measurement can be time-consuming. Mobile Light Detection and Ranging (Lidar) datasets can potentially provide a safe, cost-effective, and reliable method of performing the required evaluation. This paper presents an empirical model for radiometric calibration of Lidar intensity information from the Leica Pegasus:Two system for pavement marking evaluation. The model was developed using dense handheld retroreflectometer measurements and mobile Lidar data collected in a variety of geometric configurations on a test site consisting of various markings with varying degrees of wear. The quantitative accuracy assessment of the proposed radiometric calibration model for estimating retroreflectivity was conducted to another independent dataset collected in different lanes and system configurations.
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