Radar-Based Classification of Automotive-Related Scenarios using Temporal Information

Human gesture classification using radar sensors is becoming indispensable in the era of autonomous driving. Challenging automotive-related gestures need different techniques for accurate and quick classification. In this paper, classifications based on temporal radar data using CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), and BRNNs (Bidirectional Recurrent Neural Networks) are going to be introduced and compared. RNNs have the advantage of getting the classification decision even before the arrival of the total data stream. Twelve challenging scenarios are presented, together with the signal processing chain. Classification is performed on successive range-velocity diagrams, which spares the step of time-frequency processing already used in other applications. Four different models are introduced which result in high classification accuracies. This shows a high potential of employing radar for gesture classification using temporal information.