Raw Radar

Raw radar data processing is shifting from traditional signal processing pipelines to direct, deep learning-based approaches for improved object detection, scene reconstruction, and semantic segmentation. Current research focuses on developing neural network architectures, including convolutional neural networks (CNNs), recurrent CNNs, and transformers, to efficiently process raw radar signals and fuse them with other sensor modalities (e.g., camera, LiDAR) for enhanced perception in autonomous driving and robotics. This move towards raw data processing unlocks richer information within radar signals, leading to more robust and accurate perception systems, particularly in challenging weather conditions, and reduces reliance on computationally expensive pre-processing steps.