  Radar sensors are an important part of driver assistance systems and
intelligent vehicles due to their robustness against all kinds of adverse
conditions, e.g., fog, snow, rain, or even direct sunlight. This robustness is
achieved by a substantially larger wavelength compared to light-based sensors
such as cameras or lidars. As a side effect, many surfaces act like mirrors at
this wavelength, resulting in unwanted ghost detections. In this article, we
present a novel approach to detect these ghost objects by applying data-driven
machine learning algorithms. For this purpose, we use a large-scale automotive
data set with annotated ghost objects. We show that we can use a
state-of-the-art automotive radar classifier in order to detect ghost objects
alongside real objects. Furthermore, we are able to reduce the amount of false
positive detections caused by ghost images in some settings.
