  Evidential occupancy grid maps (OGMs) are a popular representation of the
environment of automated vehicles. Inverse sensor models (ISMs) are used to
compute OGMs from sensor data such as lidar point clouds. Geometric ISMs show a
limited performance when estimating states in unobserved but inferable areas
and have difficulties dealing with ambiguous input. Deep learning-based ISMs
face the challenge of limited training data and they often cannot handle
uncertainty quantification yet. We propose a deep learning-based framework for
learning an OGM algorithm which is both capable of quantifying first- and
second-order uncertainty and which does not rely on manually labeled data.
Results on synthetic and on real-world data show superiority over other
approaches. Source code and datasets are available at
https://github.com/ika-rwth-aachen/EviLOG
