  This letter presents a novel radar based, single-frame, multi-class detection
method for moving road users (pedestrian, cyclist, car), which utilizes
low-level radar cube data. The method provides class information both on the
radar target- and object-level. Radar targets are classified individually after
extending the target features with a cropped block of the 3D radar cube around
their positions, thereby capturing the motion of moving parts in the local
velocity distribution. A Convolutional Neural Network (CNN) is proposed for
this classification step. Afterwards, object proposals are generated with a
clustering step, which not only considers the radar targets' positions and
velocities, but their calculated class scores as well. In experiments on a
real-life dataset we demonstrate that our method outperforms the
state-of-the-art methods both target- and object-wise by reaching an average of
0.70 (baseline: 0.68) target-wise and 0.56 (baseline: 0.48) object-wise F1
score. Furthermore, we examine the importance of the used features in an
ablation study.
