Long Range Detection

Long-range object detection, crucial for autonomous driving and robotics, aims to accurately identify objects at distances exceeding typical sensor ranges. Current research heavily focuses on developing efficient, sparse 3D object detection models, often leveraging LiDAR data and incorporating multi-modal fusion with camera data to overcome the sparsity and limitations of individual sensor modalities. These advancements utilize techniques like adaptive feature diffusion, sparse query-based frameworks, and temporal recurrence to improve accuracy and computational efficiency in long-range scenarios, significantly impacting the safety and reliability of autonomous systems.