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

A Simulation-based End-to-End Learning Framework for Evidential Occupancy Grid Mapping