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.

翻译：利用雷达进行自主车辆感知因其在雾天及恶劣天气下的工作能力而日益受到研究关注。然而，雷达模型的训练受到大规模雷达数据标注成本高、难度大的制约。为突破这一瓶颈，本文提出一种自监督学习框架，通过利用大量未标注雷达数据预训练仅基于雷达的嵌入表示，以服务于自动驾驶感知任务。该方法结合雷达-雷达对比损失与雷达-视觉对比损失，从未标注的雷达热力图及其对应的相机图像中学习通用表征。在下游目标检测任务中，实验表明所提出的自监督框架可将现有监督基线方法的平均精度（mAP）提升5.8%。