Black ice detection using CNN for the Prevention of Accidents in Automated Vehicle

Abstract

Black ice is recognized as the main cause of major accidents in winter because it has characteristics that are difficult to identify with the naked eye. This is expected to be a potential cause of accidents in the era of automated vehicles as well. Accordingly, this study presents a CNN-based black ice detection plan to prevent traffic accidents caused by black ice. Due to the characteristic of black ice that is formed only in a certain environment, the data was augmented and the image of road environment in various environments was learned. Test results show that the proposed CNN model detected black ice with 96% accuracy and reproducibility(recall).