Bootstrapping Autonomous Driving Radars with Self-Supervised Learning

arXiv:2312.04519v3 Announce Type: replace
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 …

arxiv autonomous autonomous driving bootstrapping cs.cv driving self-supervised learning supervised learning type