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.
@article{arxiv.2309.15465,
title = {Cross-Dataset Experimental Study of Radar-Camera Fusion in Bird's-Eye View},
author = {Lukas Stäcker and Philipp Heidenreich and Jason Rambach and Didier Stricker},
journal= {arXiv preprint arXiv:2309.15465},
year = {2023}
}