  Object type classification for automotive radar has greatly improved with
recent deep learning (DL) solutions, however these developments have mostly
focused on the classification accuracy. Before employing DL solutions in
safety-critical applications, such as automated driving, an indispensable
prerequisite is the accurate quantification of the classifiers' reliability.
Unfortunately, DL classifiers are characterized as black-box systems which
output severely over-confident predictions, leading downstream decision-making
systems to false conclusions with possibly catastrophic consequences. We find
that deep radar classifiers maintain high-confidences for ambiguous, difficult
samples, e.g. small objects measured at large distances, under domain shift and
signal corruptions, regardless of the correctness of the predictions. The focus
of this article is to learn deep radar spectra classifiers which offer robust
real-time uncertainty estimates using label smoothing during training. Label
smoothing is a technique of refining, or softening, the hard labels typically
available in classification datasets. In this article, we exploit
radar-specific know-how to define soft labels which encourage the classifiers
to learn to output high-quality calibrated uncertainty estimates, thereby
partially resolving the problem of over-confidence. Our investigations show how
simple radar knowledge can easily be combined with complex data-driven learning
algorithms to yield safe automotive radar perception.
