  Deep learning (DL) has recently attracted increasing interest to improve
object type classification for automotive radar.In addition to high accuracy,
it is crucial for decision making in autonomous vehicles to evaluate the
reliability of the predictions; however, decisions of DL networks are
non-transparent. Current DL research has investigated how uncertainties of
predictions can be quantified, and in this article, we evaluate the potential
of these methods for safe, automotive radar perception. In particular we
evaluate how uncertainty quantification can support radar perception under (1)
domain shift, (2) corruptions of input signals, and (3) in the presence of
unknown objects. We find that in agreement with phenomena observed in the
literature,deep radar classifiers are overly confident, even in their wrong
predictions. This raises concerns about the use of the confidence values for
decision making under uncertainty, as the model fails to notify when it cannot
handle an unknown situation. Accurate confidence values would allow optimal
integration of multiple information sources, e.g. via sensor fusion. We show
that by applying state-of-the-art post-hoc uncertainty calibration, the quality
of confidence measures can be significantly improved,thereby partially
resolving the over-confidence problem. Our investigation shows that further
research into training and calibrating DL networks is necessary and offers
great potential for safe automotive object classification with radar sensors.
