Road Surface Friction Estimation for Winter Conditions Utilising General Visual Features
The WCamNet model was developed to predict road surface friction from roadside camera images during winter conditions. It combines general visual features from a pre-trained DINOv2 foundation model with a Convolutional Neural Network (CNN) pipeline. This hybrid approach outperformed previous models, achieving lower Mean Absolute Error and Root Mean Square Error in predicting friction values.