  By exploiting complementary sensor information, radar and camera fusion
systems have the potential to provide a highly robust and reliable perception
system for advanced driver assistance systems and automated driving functions.
Recent advances in camera-based object detection offer new radar-camera fusion
possibilities with bird's eye view feature maps. In this work, we propose a
novel and flexible fusion network and evaluate its performance on two datasets:
nuScenes and View-of-Delft. Our experiments reveal that while the camera branch
needs large and diverse training data, the radar branch benefits more from a
high-performance radar. Using transfer learning, we improve the camera's
performance on the smaller dataset. Our results further demonstrate that the
radar-camera fusion approach significantly outperforms the camera-only and
radar-only baselines.
