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.
Abstract (translated)
在严寒的冬季条件下，道路表面的摩擦系数会因路面上积雪、冰和水混合物的影响而大大不同。道路与车辆轮胎之间的摩擦是定义车辆动力学的重要参数，因此获取道路表面摩擦信息对于多个智能交通应用至关重要，例如安全控制自动车辆或警示驾驶员道路湿滑情况。本文从路边摄像机对道路表面摩擦进行计算机视觉评估。之前的研究已经广泛探讨了使用卷积神经网络从图像中评估道路表面状况。本文提出了一种混合深度学习架构WCamNet，包括预训练的视觉 transformer模型和卷积模块。架构的动机是结合 transformer 模型提供的通用视觉特征以及卷积模块的微调特征提取特性。为了验证该方法，从国家芬兰道路基础设施网络的路边摄像机和光学道路表面摩擦传感器中收集了广泛的數據。得到的结果表明，与之前的方法相比，所提出的 WCamNet 在预测从路边摄像机图像中预测道路表面摩擦方面表现优异。
URL
https://arxiv.org/abs/2404.16578
