End-to-end system for object detection from sub-sampled radar data

TL;DR

This paper introduces an end-to-end radar-based object detection system that operates effectively with only 20% of the data, enhancing autonomous vehicle sensing in adverse weather and lighting conditions.

Contribution

It presents a novel sub-sampling and detection pipeline that improves radar data efficiency and detection accuracy in challenging environments.

Findings

Robust detection with 20% radar samples in snow, fog, and night conditions.

Achieved 1.1% AP50 gain across scenes using sub-sampled data.

Demonstrated improved detection in motorway scenarios.

Abstract

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.