  Camera and Lidar processing have been revolutionized with the rapid
development of deep learning model architectures. Automotive radar is one of
the crucial elements of automated driver assistance and autonomous driving
systems. Radar still relies on traditional signal processing techniques, unlike
camera and Lidar based methods. We believe this is the missing link to achieve
the most robust perception system. Identifying drivable space and occupied
space is the first step in any autonomous decision making task. Occupancy grid
map representation of the environment is often used for this purpose. In this
paper, we propose PolarNet, a deep neural model to process radar information in
polar domain for open space segmentation. We explore various input-output
representations. Our experiments show that PolarNet is a effective way to
process radar data that achieves state-of-the-art performance and processing
speeds while maintaining a compact size.
