  One essential step to realize modern driver assistance technology is the
accurate knowledge about the location of static objects in the environment. In
this work, we use artificial neural networks to predict the occupation state of
a whole scene in an end-to-end manner. This stands in contrast to the
traditional approach of accumulating each detection's influence on the
occupancy state and allows to learn spatial priors which can be used to
interpolate the environment's occupancy state. We show that these priors make
our method suitable to predict dense occupancy estimations from sparse, highly
uncertain inputs, as given by automotive radars, even for complex urban
scenarios. Furthermore, we demonstrate that these estimations can be used for
large-scale mapping applications.
