  Most intelligent transportation systems use a combination of radar sensors
and cameras for robust vehicle perception. The calibration of these
heterogeneous sensor types in an automatic fashion during system operation is
challenging due to differing physical measurement principles and the high
sparsity of traffic radars. We propose - to the best of our knowledge - the
first data-driven method for automatic rotational radar-camera calibration
without dedicated calibration targets. Our approach is based on a coarse and a
fine convolutional neural network. We employ a boosting-inspired training
algorithm, where we train the fine network on the residual error of the coarse
network. Due to the unavailability of public datasets combining radar and
camera measurements, we recorded our own real-world data. We demonstrate that
our method is able to reach precise and robust sensor registration and show its
generalization capabilities to different sensor alignments and perspectives.
