  The unique properties of radar sensors, such as their robustness to adverse
weather conditions, make them an important part of the environment perception
system of autonomous vehicles. One of the first steps during the processing of
radar point clouds is often the detection of clutter, i.e. erroneous points
that do not correspond to real objects. Another common objective is the
semantic segmentation of moving road users. These two problems are handled
strictly separate from each other in literature. The employed neural networks
are always focused entirely on only one of the tasks. In contrast to this, we
examine ways to solve both tasks at the same time with a single jointly used
model. In addition to a new augmented multi-head architecture, we also devise a
method to represent a network's predictions for the two tasks with only one
output value. This novel approach allows us to solve the tasks simultaneously
with the same inference time as a conventional task-specific model. In an
extensive evaluation, we show that our setup is highly effective and
outperforms every existing network for semantic segmentation on the RadarScenes
dataset.
