Radar Object Detection

Radar object detection aims to accurately identify and locate objects using radar sensor data, crucial for applications like autonomous driving. Current research focuses on improving the accuracy and robustness of radar-only object detection, addressing challenges like low resolution and noise through techniques such as multi-view fusion, knowledge distillation from lidar data (where available during training), and the use of novel architectures including convolutional neural networks (CNNs), recurrent CNNs, and transformers. These advancements are significant because radar's robustness to adverse weather conditions makes it a vital sensor for reliable perception in autonomous systems and other applications requiring robust object detection.