Abstract
This study investigates two methods for detecting black ice, a hazardous thin layer of ice on road surfaces. The rain sensor and deep learning methods were explored, while considering the influence of environmental conditions, such as temperature, humidity, and air voids in asphalt concrete, on black ice formation. Results from the rain sensor method showed that the electrical resistance involved cooling time and reached a constant value when black ice was formed. Lower air voids in asphalt concrete led to a higher chance of black ice formation, while humidity had a negligible effect. A vehicle module for detecting black ice was successfully developed using deep learning with 90% accuracy. Furthermore, experiments using infrared heating confirmed that environmental temperature affects the melting time and melting area of black ice. These findings can improve safety for drivers and help with route planning when travelling on black ice roads.
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