  Robust and accurate sensing is of critical importance for advancing
autonomous automotive systems. The need to acquire situational awareness in
complex urban conditions using sensors such as radar has motivated research on
power and latency-efficient signal acquisition methods. In this paper, we
present an end-to-end signal processing pipeline, capable of operating in
extreme weather conditions, that relies on sub-sampled radar data to perform
object detection in vehicular settings. The results of the object detection are
further utilized to sub-sample forthcoming radar data, which stands in contrast
to prior work where the sub-sampling relies on image information. We show
robust detection based on radar data reconstructed using 20% of samples under
extreme weather conditions such as snow or fog, and on low-illuminated nights.
Additionally, we generate 20% sampled radar data in a fine-tuning set and show
1.1% gain in AP50 across scenes and 3% AP50 gain in motorway condition.
