Modern vehicles use automated driving assistance systems (ADAS) products to automate certain aspects of driving, which improves operational safety. In the U.S. in 2020, 38,824 fatalities occurred due to automotive accidents, and typically about 25% of these are associated with inclement weather. ADAS features have been shown to reduce potential collisions by up to 21%, thus reducing overall accidents. But ADAS typically utilize camera sensors that rely on lane visibility and the absence of obstructions in order to function, rendering them ineffective in inclement weather. To address this research gap, we propose a new technique to estimate snow coverage so that existing and new ADAS features can be used during inclement weather. In this study, we use a single camera sensor and historical weather data to estimate snow coverage on the road. Camera data was collected over 6 miles of arterial roadways in Kalamazoo, MI. Additionally, infrastructure-based weather sensor visibility data from an Automated Surface Observing System (ASOS) station was collected. Supervised Machine Learning (ML) models were developed to determine the categories of snow coverage using different features from the images and ASOS data. The output from the best-performing model resulted in an accuracy of 98.8% for categorizing the instances as either none, standard, or heavy snow coverage. These categories are essential for the future development of ADAS products designed to detect drivable regions in varying degrees of snow coverage such as clear weather (the none condition) and our ongoing work in tire track detection (the standard category). Overall this research demonstrates that purpose-built computer vision algorithms are capable of enabling ADAS to function in inclement weather, widening their operational design domain (ODD) and thus lowering the annual weather-related fatalities.