RADAR Scene

Radar scene understanding aims to extract meaningful information from radar data to enable autonomous driving and other applications. Current research focuses on developing deep learning architectures, often incorporating transformers and attention mechanisms, to address the challenges of noisy, sparse radar data, improving semantic segmentation and scene flow estimation. This involves leveraging cross-modal supervision from other sensors like cameras and LiDAR, and exploring self-supervised learning techniques to overcome the scarcity of labeled radar datasets. Advances in this area are crucial for enhancing the robustness and reliability of autonomous systems, particularly in challenging weather conditions.