Adaptive Cross-Attention Gated Network for Radar-Camera Fusion in BEV Space

Fusing multimodal sensors for 3D object detection has been extensively researched in the field of autonomous driving. However, existing multimodal sensor fusion methods still struggle to provide reliable detection across different modalities under diverse environmental conditions. Specifically, straightforward methods like summation or concatenation in radar-camera fusion may lead to spatial misalignment and fail to localize objects in complex scenes. To address this, we propose Adaptive Cross-Attention Gated Network (ACAGN) to enhance radar-camera fusion capabilities in Bird's-Eye View (BEV) space. Our approach integrates a deformable cross-attention and an adaptive gated network mechanism. The deformable cross-attention aligns radar and camera features from BEV with greater spatial precision, handling variations between those features effectively. Meanwhile, the adaptive gated network dynamically filters and prioritizes the most relevant information from each sensor. This dual approach improves stability and robustness of detection, as demonstrated through extensive evaluations on the nuScenes dataset.