The perception of autonomous vehicles using radars has attracted increased
research interest due its ability to operate in fog and bad weather. However,
training radar models is hindered by the cost and difficulty of annotating
large-scale radar data. To overcome this bottleneck, we propose a
self-supervised learning framework to leverage the large amount of unlabeled
radar data to pre-train radar-only embeddings for self-driving perception
tasks. The proposed method combines radar-to-radar and radar-to-vision
contrastive losses to learn a general representation from unlabeled radar
heatmaps paired with their corresponding camera images. When used for
downstream object detection, we demonstrate that the proposed self-supervision
framework can improve the accuracy of state-of-the-art supervised baselines by
5.8% in mAP.

自主车辆的感知问题中，使用雷达来感知的能力使其引起了研究人员的兴趣，然而，由于昂贵和难以标注大规模雷达数据，训练雷达模型一直存在困难。为了解决这个瓶颈问题，我们提出了一种自我监督学习框架，利用大量无标签的雷达数据来预训练仅用于自动驾驶感知任务的雷达嵌入。该方法结合了雷达对雷达和雷达对视觉的对比损失，从配对的无标签雷达热图和相应的相机图像中学习出一种通用表示。在下游目标检测任务中应用时，我们证明了该自我监督框架能够提高现有监督基线方法的 mAP 准确度 5.8%。

利用自我监督学习引导自主雷达启动

Bootstrapping Autonomous Radars with Self-Supervised Learning

Coordination of radars can be performed in various ways. To be more resilient
radar networks can be coordinated in a decentralized way. In this paper, we
introduce a highly resilient algorithm for radar coordination based on
decentralized and collaborative bundle auctions. We first formalize our problem
as a constrained optimization problem and apply a market-based algorithm to
provide an approximate solution. Our approach allows to track simultaneously
multiple targets, and to use up to two radars tracking the same target to
improve accuracy. We show that our approach performs sensibly as well as a
centralized approach relying on a MIP solver, and depending on the situations,
may outperform it or be outperformed.

基于去中心化和协作束拍卖，介绍了一种高度弹性的雷达协调算法，可同时跟踪多个目标，并利用最多两个雷达跟踪相同目标以提高准确性。