  To implement autonomous driving, one essential step is to model the vehicle
environment based on the sensor inputs. Radars, with their well-known
advantages, became a popular option to infer the occupancy state of grid cells
surrounding the vehicle. To tackle data sparsity and noise of radar detections,
we propose a deep learning-based Inverse Sensor Model (ISM) to learn the
mapping from sparse radar detections to polar measurement grids. Improved
lidar-based measurement grids are used as reference. The learned radar
measurement grids, combined with radar Doppler velocity measurements, are
further used to generate a Dynamic Grid Map (DGM). Experiments in real-world
highway scenarios show that our approach outperforms the hand-crafted geometric
ISMs. In comparison to state-of-the-art deep learning methods, our approach is
the first one to learn a single-frame measurement grid in the polar scheme from
radars with a limited Field Of View (FOV). The learning framework makes the
learned ISM independent of the radar mounting. This enables us to flexibly use
one or more radar sensors without network retraining and without requirements
on 360{\deg} sensor coverage.
