  Automotive radar has increasingly attracted attention due to growing interest
in autonomous driving technologies. Acquiring situational awareness using
multimodal data collected at high sampling rates by various sensing devices
including cameras, LiDAR, and radar requires considerable power, memory and
compute resources which are often limited at an edge device. In this paper, we
present a novel adaptive radar sub-sampling algorithm designed to identify
regions that require more detailed/accurate reconstruction based on prior
environmental conditions' knowledge, enabling near-optimal performance at
considerably lower effective sampling rates. Designed to robustly perform under
variable weather conditions, the algorithm was shown on the Oxford raw radar
and RADIATE dataset to achieve accurate reconstruction utilizing only 10% of
the original samples in good weather and 20% in extreme (snow, fog) weather
conditions. A further modification of the algorithm incorporates object motion
to enable reliable identification of important regions. This includes
monitoring possible future occlusions caused by objects detected in the present
frame. Finally, we train a YOLO network on the RADIATE dataset to perform
object detection directly on RADAR data and obtain a 6.6% AP50 improvement over
the baseline Faster R-CNN network.

Computer Vision and Pattern Recognition;Signal Processing