  Occupancy grid mapping is an important component in road scene understanding
for autonomous driving. It encapsulates information of the drivable area, road
obstacles and enables safe autonomous driving. Radars are an emerging sensor in
autonomous vehicle vision, becoming more widely used due to their long range
sensing, low cost, and robustness to severe weather conditions. Despite recent
advances in deep learning technology, occupancy grid mapping from radar data is
still mostly done using classical filtering approaches.In this work, we propose
learning the inverse sensor model used for occupancy grid mapping from
clustered radar data. This is done in a data driven approach that leverages
computer vision techniques. This task is very challenging due to data sparsity
and noise characteristics of the radar sensor. The problem is formulated as a
semantic segmentation task and we show how it can be learned using lidar data
for generating ground truth. We show both qualitatively and quantitatively that
our learned occupancy net outperforms classic methods by a large margin using
the recently released NuScenes real-world driving data.
