  This paper presents an novel object type classification method for automotive
applications which uses deep learning with radar reflections. The method
provides object class information such as pedestrian, cyclist, car, or
non-obstacle. The method is both powerful and efficient, by using a
light-weight deep learning approach on reflection level radar data. It fills
the gap between low-performant methods of handcrafted features and
high-performant methods with convolutional neural networks. The proposed
network exploits the specific characteristics of radar reflection data: It
handles unordered lists of arbitrary length as input and it combines both
extraction of local and global features. In experiments with real data the
proposed network outperforms existing methods of handcrafted or learned
features. An ablation study analyzes the impact of the proposed global context
layer.
