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. Code is available at \url{https://github.com/yiduohao/Radical}.

翻译：由于能够在雾天和恶劣天气中运行，利用雷达的自动驾驶车辆感知技术引起了越来越多的研究兴趣。然而，雷达模型的训练受到大规模雷达数据标注成本和难度的制约。为克服这一瓶颈，我们提出了一种自监督学习框架，利用大量未标注的雷达数据，为自动驾驶感知任务预训练纯雷达嵌入。该方法结合雷达-雷达对比损失和雷达-视觉对比损失，从未标注的雷达热图及其对应相机图像中学习通用表征。在下游目标检测任务中，我们证明所提出的自监督框架能将现有监督基线的最先进平均精度（mAP）提升$5.8\%$。代码已开源在\url{https://github.com/yiduohao/Radical}。