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.

翻译：通过利用互补的传感器信息，雷达与摄像头融合系统有望为高级驾驶辅助系统和自动驾驶功能提供高鲁棒性与高可靠性的感知系统。近年来，基于摄像头的目标检测技术进展为采用鸟瞰视角特征图的雷达-摄像头融合开辟了新的可能性。本文提出了一种新颖且灵活的融合网络，并在nuScenes和View-of-Delft两个数据集上评估其性能。实验表明，摄像头分支需要大规模且多样化的训练数据，而雷达分支则更受益于高性能雷达。通过迁移学习，我们提升了摄像头在较小数据集上的表现。结果进一步证明，雷达-摄像头融合方法的性能显著优于仅依赖摄像头或仅依赖雷达的基线方法。