End-to-end system for object detection from sub-sampled radar data
This addresses robust sensing for autonomous vehicles in challenging conditions, representing an incremental improvement over prior methods.
The paper tackles object detection from sub-sampled radar data for autonomous vehicles, achieving robust detection with only 20% of radar samples in extreme weather and low-light conditions, and showing gains of 1.1-3% in AP50 metrics.
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.