Winter Road Surface Condition Recognition Using Semantic Segmentation and the Generative Adversarial Network: A Case Study of Iowa, U.S.A.

Abstract

Adverse road surface conditions (RSCs) are a significant concern for traffic safety and mobility during the winter season. Dealing with this issue requires the prompt execution of maintenance operations to restore safe driving conditions. The faster maintenance personnel are made aware of the dangerous conditions, the faster they can mobilize to mitigate adverse RSCs. Therefore, the speed at which RSC information is provided is vital for reducing potential road-related incidents and maintaining mobility. Intending to improve the delivery of RSC information, this study proposes developing a deep learning-based method through the usage of road weather information system (RWIS) images to automate the RSC classification process. While the RWIS collects images that give a direct view of the road, the traditional manual way of utilizing these images for RSC monitoring is laborious and time-consuming. To overcome this challenge as well as other limitations associated with previously developed RSC recognition methods, the application of deep neural networks using imagery data is investigated here. More specifically, the application of semantic segmentation (SS) and generative adversarial network (GAN) techniques in automating RSC recognition was adopted in an attempt to predict the locations of drivable areas and indicate the snow hazard level solely based on RWIS images. Case study results from Iowa, U.S.A., show that both SS and GAN techniques perform their tasks with a high degree of accuracy.