Road Weather Condition Estimation Using Fixed and Mobile Based Cameras

Area of Science:

• Computer Vision
• Transportation Engineering
• Environmental Sensing

Background:

• Road weather condition classification is challenging due to environmental variability.
• Weather-related crashes account for a significant portion of vehicle accidents and fatalities.
• Transportation agencies widely deploy cameras for operational awareness.

Purpose of the Study:

• To develop a model for estimating roadway surface conditions using existing camera infrastructure.
• To classify road surface conditions into Clear, Rainy-Wet, and Snow categories.
• To leverage fixed Closed-circuit Television (CCTV) and mobile cameras for road weather monitoring.

Main Methods:

• Image processing techniques applied to camera feeds.
• Development of a classification model for three distinct road surface conditions.
• Evaluation of classification accuracy using both fixed CCTV and mobile snow plow dash-cam footage.

Main Results:

• Achieved 98.57% classification accuracy for CCTV cameras.
• Achieved 77.32% classification accuracy for mobile cameras.
• Demonstrated the model's effectiveness across varied weather and lighting conditions.

Conclusions:

• The proposed method effectively estimates road surface conditions from camera data.
• The classification system is suitable for autonomous snow plow route selection.
• The technology can aid in verifying extreme road conditions for enhanced safety.