  Detecting obstacles is crucial for safe and efficient autonomous driving. To
this end, we present NVRadarNet, a deep neural network (DNN) that detects
dynamic obstacles and drivable free space using automotive RADAR sensors. The
network utilizes temporally accumulated data from multiple RADAR sensors to
detect dynamic obstacles and compute their orientation in a top-down bird's-eye
view (BEV). The network also regresses drivable free space to detect
unclassified obstacles. Our DNN is the first of its kind to utilize sparse
RADAR signals in order to perform obstacle and free space detection in real
time from RADAR data only. The network has been successfully used for
perception on our autonomous vehicles in real self-driving scenarios. The
network runs faster than real time on an embedded GPU and shows good
generalization across geographic regions.
