  While self-supervised monocular depth estimation in driving scenarios has
achieved comparable performance to supervised approaches, violations of the
static world assumption can still lead to erroneous depth predictions of
traffic participants, posing a potential safety issue. In this paper, we
present R4Dyn, a novel set of techniques to use cost-efficient radar data on
top of a self-supervised depth estimation framework. In particular, we show how
radar can be used during training as weak supervision signal, as well as an
extra input to enhance the estimation robustness at inference time. Since
automotive radars are readily available, this allows to collect training data
from a variety of existing vehicles. Moreover, by filtering and expanding the
signal to make it compatible with learning-based approaches, we address radar
inherent issues, such as noise and sparsity. With R4Dyn we are able to overcome
a major limitation of self-supervised depth estimation, i.e. the prediction of
traffic participants. We substantially improve the estimation on dynamic
objects, such as cars by 37% on the challenging nuScenes dataset, hence
demonstrating that radar is a valuable additional sensor for monocular depth
estimation in autonomous vehicles.
