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Advanced Driver Assistance Systems (ADAS) generally utilize cameras to provide limited automation functions to enhance driver safety.ADAS uses computer vision (CV) to extract vehicle surrounds, lane boundaries, drivable regions, and nearby objects. ADAS systems fail, however, when the vehicle is operating in adverse conditions (e.g., obscured lane lines). We introduce a new ADAS CV method that was evaluated in snowy weather and lane line occlusion scenarios by recognizing tire tracks in the snow. This approach was previously developed using classical machine learning techniques (ML) but has now been expanded to include a variety of convolutional neural network (CNN) models. Using an instrumented automation research vehicle, a custom dataset was collected. A data preparation pipeline was constructed for data labeling and model training. The CNN models outperform the classical ML model in …