  The growing urban complexity demands an efficient algorithm to acquire and
process various sensor information from autonomous vehicles. In this paper, we
introduce an algorithm to utilize object detection results from the image to
adaptively sample and acquire radar data using Compressed Sensing (CS). This
novel algorithm is motivated by the hypothesis that with a limited sampling
budget, allocating more sampling budget to areas with the object as opposed to
a uniform sampling ultimately improves relevant object detection performance.
We improve detection performance by dynamically allocating a lower sampling
rate to objects such as buses than pedestrians leading to better reconstruction
than baseline across areas with objects of interest. We automate the sampling
rate allocation using linear programming and show significant time savings
while reducing the radar block size by a factor of 2. We also analyze a Binary
Permuted Diagonal measurement matrix for radar acquisition which is
hardware-efficient and show its performance is similar to Gaussian and Binary
Permuted Block Diagonal matrix. Our experiments on the Oxford radar dataset
show an effective reconstruction of objects of interest with 10% sampling rate.
Finally, we develop a transformer-based 2D object detection network using the
NuScenes radar and image data.
