Automotive Radar Sub-Sampling via Object Detection Networks: Leveraging Prior Signal Information

Abstract

In recent years, automotive radar has attracted considerable attention due to the 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 the information about prior environmental conditions, 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 radar dataset to achieve accurate scene reconstruction utilizing only 10% of the collected samples in good weather. In the case of the RADIATE dataset acquired during extreme weather conditions (snow, fog), only 20% of the samples were sufficient to enable robust scene reconstruction. A further modification of the algorithm incorporates object motion to enable reliable identification of regions that require attention. This includes monitoring possible future occlusions caused by the objects detected in the present frame. Finally, we train a YOLO network on the RADIATE dataset to perform object detection, obtaining 6.6% AP50 improvement over the baseline Faster R-CNN network.

Keywords

• Compressive sensing
• automotive radar
• sub-sampling
• Faster R-CNN
• YOLO
• object detection