Feature-Centric Approach for Learning-Based Prediction of Pavement Marking Retroreflectivity from Mobile LiDAR Data

Abstract

Given the crucial importance of pavement marking retroreflectivity in ensuring visibility for road safety, this research investigates the correlation between pavement marking reflectivity and LiDAR data. Empirical data were collected from eight road sections using both a handheld retroreflectometer and a mobile LiDAR. The approach proposed focuses on extracting important features from pavement marking regions of the LiDAR point cloud. A comprehensive feature extraction and feature selection process was employed. In addition, a well-rounded selection of learning algorithms was evaluated. A rigorous hold-out evaluation was incorporated, ensuring that the reported performance metrics were robustly generalizable. The best performing model was able to achieve an R2 of 0.824 on unseen data. The findings of this study illuminate the potential for leveraging relatively inexpensive mobile LiDAR sensors in combination with machine learning techniques in conducting efficient pavement marking assessments, not only to detect completely degraded markings, but to accurately estimate retroreflective properties.

Keywords

• road marking retroreflectivity
• LiDAR
• intensity
• machine learning
• AI
• road condition assessment