# Prediction of Friction Coefficient Based on 3D Texture Characteristics of Road Surfaces
## 2 Measurement Tools and Experimental Methods
### Methodology  
To explore the potential of predicting the friction coefficient using texture parameters, measurements were carried out on 17 different rural road pavements. The key criterion for site selection was maximizing the diversity of friction coefficients, representing a range of pavement surfaces with varying levels of microtexture and macrotexture. The profiles for key texture parameters were acquired on dry surfaces, as this is standard practice for capturing surface morphology accurately. The friction measurements, however, were conducted with a water film to simulate wet conditions. Friction coefficient (\(\mu\)) measurements were performed 6 to 9 times, depending on value variability, with outliers omitted. For further evaluation, average values for each interval were considered over a 5 m section, totaling 170 points across all sections, with measurements at 0.5 m intervals.  
Measurements were conducted under similar weather conditions within a short time-frame to minimize climatic influences. This study did not consider pavement material, type, degradation, wearing course composition, or traffic load.  
Adhering to previous experimental recommendations, special care was taken to ensure precise localization of measurements. Prior experiments showed that 2D texture parameters inadequately described friction levels, prompting the evaluation of surface morphology using 3D texture parameters.  
Following the use of the SRS, raw data were processed and analyzed. Using X, Y, Z coordinates, surfaces for each measuring spot were created and aligned horizontally. An example of a measured and leveled surface with a selected profile is shown in Figure 4.  
Figure 3: The Static Road Scanner and the measurement points marked on the road surface. (1—Marked corners; 2—Measurement points; 3—Computer; 4—Generator; 5—Static Road Scanner; 6—Steel grid).  
During road surface scanning, outliers can occur due to dust particles or measurement errors from reflections, resulting in inaccurate surface images. That is why the raw dataset was filtered and outliers were removed. Various methods exist for removing outliers, each targeting specific inaccuracies. In this study, outliers were removed according to the maximum slope of surface irregularities, set to 60\({}^{\circ}\) based on the SRS device specifications.  
The processed result was the primary surface (PS), which includes irregularities spanning microtexture and macrotexture levels. As well as for primary surface, all texture parameters were also analyzed for filtered surfaces representing macrotexture and microtexture. The microtexture surface (MIC) was derived from the primary surface by applying a high-pass L-filter to suppress macrotexture wavelengths, while the macrotexture surface (MAC) was created using a low-pass S-filter to suppress microtexture wavelengths. The filtration process utilized Gaussian filters, as specified in [28].  
After the primary processing of pavement surface data, more than 80 texture parameters were evaluated to establish the connection between texture and road friction. These parameters included standard height measures, as well as spatial, hybrid, functional, functional volume, and feature characteristics.