  For autonomous driving, radar sensors provide superior reliability regardless
of weather conditions as well as a significantly high detection range.
State-of-the-art algorithms for environment perception based on radar scans
build up on deep neural network architectures that can be costly in terms of
memory and computation. By processing radar scans as point clouds, however, an
increase in efficiency can be achieved in this respect. While Convolutional
Neural Networks show superior performance on pattern recognition of regular
data formats like images, the concept of convolutions is not yet fully
established in the domain of radar detections represented as point clouds. The
main challenge in convolving point clouds lies in their irregular and unordered
data format and the associated permutation variance. Therefore, we apply a
deep-learning based method introduced by PointCNN that weights and permutes
grouped radar detections allowing the resulting permutation invariant cluster
to be convolved. In addition, we further adapt this algorithm to radar-specific
properties through distance-dependent clustering and pre-processing of input
point clouds. Finally, we show that our network outperforms state-of-the-art
approaches that are based on PointNet++ on the task of semantic segmentation of
radar point clouds.
