Automated interpretation and understanding of the driving environment using image processing is a challenging task, as most current vision-based systems are not designed to work in dynamically-changing and naturalistic real-world settings. For instance, road weather condition classification using a camera is a challenge due to high variance in weather, road layout, and illumination conditions. Most transportation agencies, within the U.S., have deployed some cameras for operational awareness. Given that weather related crashes constitute 22% of all vehicle crashes and 16% of crash fatalities, this study proposes using these same cameras as a source for estimating roadway surface condition. The developed model is focused on three road surface conditions resulting from weather including: Clear (clear/dry), Rainy-Wet (rainy/slushy/wet), and Snow (snow-covered/partially snow-covered). The camera sources evaluated are both fixed Closed-circuit Television (CCTV) and mobile (snow plow dash-cam). The results are promising; with an achieved 98.57% and 77.32% road weather classification accuracy for CCTV and mobile cameras, respectively. Proposed classification method is suitable for autonomous selection of snow plow routes and verification of extreme road conditions on roadways.
Keywords: CCTV; Mobile camera; Neural networks; Road weather classification; Scene classification; VGG16.