CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation presents a solution to enhance camera-radar fusion performance through a unique knowledge distillation approach, aiming to close the gap with LiDAR-Camera (LC) fusion methods.

• Addresses the challenge of integrating cost-effective camera-radar sensors for 3D object detection.
• Proposes Camera-Radar Knowledge Distillation (CRKD) with novel distillation losses tailored for cross-modality learning.
• Utilizes Bird’s-Eye-View (BEV) representation to facilitate feature transfer from LC teacher models.
• The method shows promising results on the nuScenes dataset and outlines the potential for deployment in consumer automobiles.

The strategy outlined in this work can substantially influence the development of more accessible and reliable 3D object detection systems, promoting safer and smarter autonomous driving technologies.