Real Time Thermal Image Based Machine Learning Approach for Early Collision Avoidance System of Snowplows

Abstract

In an effort to reduce vehicle collisions with snowplows in poor weather conditions, this paper details the development of a real time thermal image based machine learning approach to an early collision avoidance system for snowplows, which intends to detect and estimate the distance of trailing vehicles. Due to the operational conditions of snowplows, which include heavy-blowing snow, traditional optical sensors like LiDAR and visible spectrum cameras have reduced effectiveness in detecting objects in such environments. Thus, we propose using a thermal infrared camera as the primary sensor along with machine learning algorithms. First, we curate a large dataset of thermal images of vehicles in heavy snow conditions. Using the curated dataset, two machine-learning models based on the modified ResNet architectures were trained to detect and estimate the trailing vehicle distance using real-time thermal images. The trained detection network was capable of detecting trailing vehicles 99.0% of the time at 1500.0 ft distance from the snowplow. The trained trailing distance network was capable of estimating distance with an average estimation error of 10.70 ft. The inference performance of the trained models is discussed, along with the interpretation of the performance.