# Prediction of Friction Coefficient Based on 3D Texture Characteristics of Road Surfaces
###### Abstract  
Accurate assessment of road pavement friction is crucial for maintaining road safety. This study explores the prediction of the friction coefficient (\(\mu\)) using 3D texture parameters of pavement surfaces. Measurements were conducted on 17 different rural road sections using the Traction Watcher One (TWO) for friction coefficients and a newly developed Static Road Scanner (SRS) for surface texture. Multiple linear regression models were created, incorporating texture parameters such as the valley material portion (\(S_{m2,ML}\)), arithmetic mean peak curvature (\(S_{pc,MAC}\)), and dale void volume (\(V_{\text{vz,PS}}\)). The results demonstrate a strong correlation between texture characteristics and friction, with \(R^{2}\) values up to 0.80 and an RMSE as low as 0.076, validating the model's accuracy. This approach highlights the potential of using non-contact texture measurements for reliable prediction of friction, offering a significant advancement in pavement management and safety. skid resistance; friction coefficient; 3D texture parameters; pavement texture; predictive modeling +
Footnote †: journal: Medical  
Considering the aforementioned details and the rapid advancements in surface sensing and data processing technologies, there is a growing focus on predicting skid resistance through non-contact texture measurements. In [11], an analytical model was proposed, demonstrating the importance of hierarchical surface structures in predicting anisotropic friction, adhesion, and wear in rough surface interactions. To predict the friction coefficient, mathematical models simulating a rubber element sliding over a rough surface have been developed [12; 13; 14]. The authors of [15] compare the results obtained by the proposed analytical model [14] with numerical results. The article [16] shows how the theory of contact mechanics and rubber friction developed by [PERSON] was further extended. These models, though complex and physically grounded, require input on the rubber's viscoelastic properties, which can vary. Nevertheless, promising results have emerged from combining [PERSON]'s model with texture measurements on asphalt samples [17]. However, for texture-based skid resistance prediction, only pavement surface characteristics should be considered, keeping other variables constant for accurate comparisons and degradation model determinations. Researchers often use statistical regression models to predict skid resistance by correlating texture descriptors with measured friction coefficients. Various methodologies exist within this approach. For instance, the pavement texture-friction relationship was analyzed using image analysis techniques in [18]. Another study [19] explored the correlation between texture spectral levels and friction values at different speeds. A multiresolution analysis using three-dimensional (3D) asphalt pavement texture was used for asphalt pavement friction estimation [20]. The studies [21; 22] explored pavement surface fractal characteristics regarding the friction coefficient. Additionally, a model for evaluating pavement skid resistance using 3D texture data was developed, yielding satisfactory test results as documented in [23]. The research on the skid resistance performance of asphalt pavement based on 3D texture feature parameters [24; 25] introduces 3D texture characteristics to investigate the mechanism of pavement wear in rainy weather and conducts a comparative analysis of aggregate wear. Recent advancements in road surface perception using vision sensors, such as convolutional neural networks for classifying road surface conditions, have demonstrated significant potential in vehicle trajectory planning and driving assistance systems, particularly for autonomous vehicles [26; 27]. Despite extensive research and several promising studies, the relationship between texture and friction remains not fully understood.