  We present a new method to combine evidential top-view grid maps estimated
based on heterogeneous sensor sources. Dempster's combination rule that is
usually applied in this context provides undesired results with highly
conflicting inputs. Therefore, we use more advanced evidential reasoning
techniques and improve the conflict resolution by modeling the reliability of
the evidence sources. We propose a data-driven reliability estimation to
optimize the fusion quality using the Kitti-360 dataset. We apply the proposed
method to the fusion of LiDAR and stereo camera data and evaluate the results
qualitatively and quantitatively. The results demonstrate that our proposed
method robustly combines measurements from heterogeneous sensors and
successfully resolves sensor conflicts.
