  Radar-based road user detection is an important topic in the context of
autonomous driving applications. The resolution of conventional automotive
radar sensors results in a sparse data representation which is tough to refine
during subsequent signal processing. On the other hand, a new sensor generation
is waiting in the wings for its application in this challenging field. In this
article, two sensors of different radar generations are evaluated against each
other. The evaluation criterion is the performance on moving road user object
detection and classification tasks. To this end, two data sets originating from
an off-the-shelf radar and a high resolution next generation radar are
compared. Special attention is given on how the two data sets are assembled in
order to make them comparable. The utilized object detector consists of a
clustering algorithm, a feature extraction module, and a recurrent neural
network ensemble for classification. For the assessment, all components are
evaluated both individually and, for the first time, as a whole. This allows
for indicating where overall performance improvements have their origin in the
pipeline. Furthermore, the generalization capabilities of both data sets are
evaluated and important comparison metrics for automotive radar object
detection are discussed. Results show clear benefits of the next generation
radar. Interestingly, those benefits do not actually occur due to better
performance at the classification stage, but rather because of the vast
improvements at the clustering stage.
