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.

翻译：城市日益复杂,需要一种高效率的算法来获取和处理来自自治车辆的各种传感器信息。在本文中,我们引入一种算法来利用图像的物体探测结果进行适应性抽样,并利用压缩遥感(CS)获取雷达数据。这种新算法的动机是假设:在有限的取样预算下,将更多的采样预算分配给目标地区,而不是统一取样,最终会提高相关物体探测性能。我们通过动态地将较低的采样率分配给诸如公共汽车等物体,而不是行人,从而在有利益对象的地区之间实现比基线更好的重建。我们使用线性编程将采样率分配自动化,并显示大量节省时间,同时将雷达区块的尺寸减少2倍。我们还分析了用于获取雷达的硬件效率的二元半对角测量矩阵,并显示其性能类似于高斯和宾里珀穆特堡 Diagonal矩阵。我们对牛津雷达数据集的实验显示,以10%的采样率有效地重建了兴趣对象。最后,我们利用Nuscenes雷达和图像数据开发一个基于变式的2D物体探测网络。