No abstract

This article describes the use of a commercial UWB radar for vehicle classification and lane occupation detection using real-world data acquired in an urban environment. We compare two radar image processing schemes: one based on deep learning using raw data produced by the radar, and a second method employing traditional machine learning algorithms using features extracted from raw data. We verify experimentally that both schemes lead to reasonably accurate estimates without the need of large training sets.