  This paper introduces a novel hybrid architecture that enhances radar-based
Dynamic Occupancy Grid Mapping (DOGM) for autonomous vehicles, integrating deep
learning for state-classification. Traditional radar-based DOGM often faces
challenges in accurately distinguishing between static and dynamic objects. Our
approach addresses this limitation by introducing a neural network-based DOGM
state correction mechanism, designed as a semantic segmentation task, to refine
the accuracy of the occupancy grid. Additionally a heuristic fusion approach is
proposed which allows to enhance performance without compromising on safety. We
extensively evaluate this hybrid architecture on the NuScenes Dataset, focusing
on its ability to improve dynamic object detection as well grid quality. The
results show clear improvements in the detection capabilities of dynamic
objects, highlighting the effectiveness of the deep learning-enhanced state
correction in radar-based DOGM.
