Road Surface Friction Estimation for Winter Conditions Utilising General Visual Features

TL;DR

This paper presents WCamNet, a hybrid deep learning model combining visual transformers and convolutional layers, for estimating road surface friction in winter conditions from roadside images, outperforming previous methods.

Contribution

Introduction of WCamNet, a novel hybrid deep learning architecture that leverages visual transformers and convolutional blocks for improved road friction estimation from images.

Findings

WCamNet outperforms previous models in accuracy.

The approach effectively utilizes general visual features.

Extensive Finnish dataset supports the results.

Abstract

In below freezing winter conditions, road surface friction can greatly vary based on the mixture of snow, ice, and water on the road. Friction between the road and vehicle tyres is a critical parameter defining vehicle dynamics, and therefore road surface friction information is essential to acquire for several intelligent transportation applications, such as safe control of automated vehicles or alerting drivers of slippery road conditions. This paper explores computer vision-based evaluation of road surface friction from roadside cameras. Previous studies have extensively investigated the application of convolutional neural networks for the task of evaluating the road surface condition from images. Here, we propose a hybrid deep learning architecture, WCamNet, consisting of a pretrained visual transformer model and convolutional blocks. The motivation of the architecture is to combine general visual features provided by the transformer model, as well as finetuned feature extraction properties of the convolutional blocks. To benchmark the approach, an extensive dataset was gathered from national Finnish road infrastructure network of roadside cameras and optical road surface friction sensors. Acquired results highlight that the proposed WCamNet outperforms previous approaches in the task of predicting the road surface friction from the roadside camera images.