A Road Narrowing Condition Estimation from In-vehicle Camera Videos via Late-Fusion based on Confidence Level Integration of Multiple Classifiers

Abstract

In snowy and cold regions, piled snow on road shoulders may cause traffic congestion and even traffic accidents. Maintaining the functionality of urban transportation can be a severe problem. In order to maintain the effective width of roads during the winter, piled snow on the road shoulders is removed and cleared. However, getting road information requires a great deal of time and workforce. In this paper, we propose a novel method for classifying the effective road width, narrowed by piled snow on road shoulders, based on videos captured from in-vehicle cameras using features focused on piled snow. Estimating road narrowing conditions from in-vehicle cameras will enable ordinary vehicles to collect road information and create an environment that does not require much time or workforce to gather road information.