# Bootstrapping Autonomous Driving Radars with Self-Supervised Learning Source: [Bootstrapping Autonomous Driving Radars with Self-Supervised Learning]() Collection index: [llms.txt]() Data updated: 2026-01-02T05:37:57.518Z Authors: Yiduo Hao, Sohrab Madani, Junfeng Guan, Mohammed Alloulah, Saurabh Gupta, Haitham Hassanieh Published: 2023-12-07T18:38:39.000Z ## Abstract 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}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on Radatron (test)](): Object Detection; Radatron (test); mAP: 52.6. - [Object Detection on RISE-Indoor](): Object Detection; RISE-Indoor; Success Rate (S1): 0. ## Original sources and implementation [Original paper]() [PDF]()