  With the recent boost in autonomous driving, increased attention has been
paid on radars as an input for occupancy mapping. Besides their many benefits,
the inference of occupied space based on radar detections is notoriously
difficult because of the data sparsity and the environment dependent noise
(e.g. multipath reflections). Recently, deep learning-based inverse sensor
models, from here on called deep ISMs, have been shown to improve over their
geometric counterparts in retrieving occupancy information. Nevertheless, these
methods perform a data-driven interpolation which has to be verified later on
in the presence of measurements. In this work, we describe a novel approach to
integrate deep ISMs together with geometric ISMs into the evidential occupancy
mapping framework. Our method leverages both the capabilities of the
data-driven approach to initialize cells not yet observable for the geometric
model effectively enhancing the perception field and convergence speed, while
at the same time use the precision of the geometric ISM to converge to sharp
boundaries. We further define a lower limit on the deep ISM estimate's
certainty together with analytical proofs of convergence which we use to
distinguish cells that are solely allocated by the deep ISM from cells already
verified using the geometric approach.
