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.

通过利用互补的传感器信息，雷达和相机融合系统具有为高级驾驶员辅助系统和自动驾驶功能提供高度鲁棒可靠的感知系统的潜力。最近相机基于物体检测的进展为鸟瞰特征图的雷达相机融合提供了新的可能性。本文提出了一种新颖而灵活的融合网络，并在两个数据集（nuScenes 和 View-of-Delft）上评估其性能。我们的实验结果表明，虽然相机分支需要大量多样化的训练数据，但雷达分支更受到高性能雷达的影响。使用迁移学习，我们改善了较小数据集上相机的性能。我们的结果进一步证明了雷达相机融合方法明显优于仅相机和仅雷达的基线模型。