  For many automated driving functions, a highly accurate perception of the
vehicle environment is a crucial prerequisite. Modern high-resolution radar
sensors generate multiple radar targets per object, which makes these sensors
particularly suitable for the 2D object detection task. This work presents an
approach to detect 2D objects solely depending on sparse radar data using
PointNets. In literature, only methods are presented so far which perform
either object classification or bounding box estimation for objects. In
contrast, this method facilitates a classification together with a bounding box
estimation of objects using a single radar sensor. To this end, PointNets are
adjusted for radar data performing 2D object classification with segmentation,
and 2D bounding box regression in order to estimate an amodal 2D bounding box.
The algorithm is evaluated using an automatically created dataset which consist
of various realistic driving maneuvers. The results show the great potential of
object detection in high-resolution radar data using PointNets.
