Various computational methods for highway health monitoring in terms of detection of black ice: a sustainable approach in Indian context

Abstract

Abstract Black ice is responsible for dangerous road-related incidents that can cause collisions and harm vehicle drivers and pedestrians. Visual examination and weather forecasts are two standard traditional methods for detecting black ice on roads, but they are often inaccurate and may not deliver the vehicle driver with up-to-date information on road conditions. The evolution of Industry 4.0 enabling technologies such as wireless sensor network (WSN), Internet of Things (IoT), cloud computing, and machine learning (ML) has been capable of detecting events in real time. This study aims to analyse the integration of the WSN, IoT, ML, and image processing for black ice detection. The qualitative research method is followed in this study, where the problems of black ice detection are studied. Following this, the role of Industry 4.0 enabling technologies is analyzed in detail for black ice detection. According to the study, we can detect black ice using different methods, but some methods need to be refined if we talk about the prediction. By merging different technologies, we can improve the overall architecture and create an algorithm that works with images and physical variables like temperature, humidity, due point, and road temperature, which were responsible for black ice formation, and predict the chances of black ice formation by training the system.

Keywords

• WSN
• Black ice
• Road surface monitoring system
• Machine learning
• IoT
• CNN