This study aims to validate the feasibility of using LiDAR reflectivity data to quantitatively estimate the retroreflectivity of road lane markings. The goal is to establish the optimal scanning conditions considering the channel position, angle of incidence, and vehicle speed for an accurate and consistent retroreflectivity assessment in mobile environments. Fifteen standard lane marking samples with known retroreflectivity values were scanned using an OS1-128 LiDAR sensor under controlled field conditions. A two-phase experiment was conducted: (1) a speed-based test to assess the influence of vehicle velocity (20-80 km/h) on LiDAR reflectivity measurements, and (2) a channel–angle–distance test using a static testbed to analyze the relationship between retroreflectivity, LiDAR channel position (that is, the angle of incidence), and measurement distance. Ground truth retroreflectivity values were obtained using a high-precision handheld retroreflectometer. Reflectivity measurements showed a strong correlation with standard retroreflectivity values, particularly at scanning angle between 100-115° and distances of 4.9-5.6 m. The coefficient of determination (R2) exceeded 0.97 across optimal conditions. Speedrelated tests confirmed that the LiDAR-based reflectivity remained stable with a minimal RMSE (< 5), even under high-speed driving scenarios. LiDAR sensors provided reliable and contactless estimates of pavement marking retroreflectivity when the channel angle and scanning distance were appropriately selected. The findings demonstrated that channel-specific calibration and incidence angle correction significantly improved the measurement accuracy. This suggests a practical path forward for automated large-scale retroreflectivity monitoring in road asset management systems.

algorithms for deriving and analyzing retroreflectivity influence factors through regression analysis. METHODS : An experimental road lane was created to examine the trends of retroreflectivity and LiDAR intensity values, and a controlled indoor experiment was conducted to identify influencing factors. The optimal algorithm was developed by regression analysis of the experimental data. RESULTS : The significance probability (P-value) through SPSS linear regression analysis was 0.000 for measured height, 0.001 for perpendicular angle, 0.157 for vertical angle, and 0.000 for LiDAR intensity, indicating that measured height, vertical angle, and LiDAR intensity are significant factors because the significance probability is less than 0.05, and vertical angle is not significant. The NNR regression model performed the best, so the measurement data with height (1.2m, 2m, 2.2m) and vertical angle (11.3°, 12.3°, 13.5°) were analyzed to derive the optimal LiDAR Intensity measurement height and vertical angle. CONCLUSIONS : For each LiDAR measurement height and vertical angle, the highest correlation between LiDAR Intensity and retroreflectivity was found at a measurement height of 1.2 meters and a vertical angle of 12.3°, where the model learning accuracy (R2) was the highest.

PURPOSES : The purpose of this study is to verify the effect of improving the retroreflectivity of pavement marking by increasing the refractive index of glass bead. METHODS : Pavement marking test-beds has been installed in National Highway 19, 42 and KICT Yeoncheon SOC Center. In testbeds several marking sections were installed for each type of marking materials and glass beads. In this test-beds initial dry and wet retroreflectivity were measured and analyzed. RESULTS : When the refractive index of glass bead was adjusted upward in water based paint(glass bead No.1→ No.2), dry retroreflectivity increased by about 30 to 70%, and wet retroreflectivity slightly increased by about 10 to 40%. When using glass bead No. 2 in water based paint, it was found to meet the standards of the Road Traffic Act. However, since wet retroreflectivity of water based paint slightly exceeds the standard value, a follow-up investigation is needed to determine how long this performance can be maintained. When using glass bead No.1 in MMA(methyl methacrylate), the average wet retroreflectivity was evaluated to be 128 to 150 mcd/lx/m2, and when using glass bead No.2, the average wet retroreflectivity was evaluated to be about 200 to 270 mcd/lx/m2. Accordingly, MMA showed the best performance compared to other paints. CONCLUSIONS : When using glass bead No.1 in water-soluble paints and thermoplastic, it did not meet the wet retroreflectivity standards of the Road Traffic Act. But when using glass bead No.2, it met the wet retroreflectivity standards. As a result of analyzing the road marking budget according to the upward adjustment of the refractive index of glass bead, it was analyzed that if only the material class was adjusted upward, the cost would increase by more than twice the current budget. In order to decrease this budget increase rate(to increase service life), it is necessary to strengthen quality control standards for pavement marking and develop scientific-systematic quality control techniques.