3D Multi-Modal Object Detection Based on Cross-Attention Feature Fusion

Abstract

In Advanced Driver Assistance Systems (ADAS), environmental perception and object detection are crucial for ensuring safe autonomous driving. Single-modality systems often struggle under adverse weather conditions, underscoring the need for multi-modal approaches. Current fusion methods typically rely on simplistic concatenation of multi-modal features, which neglects semantic alignment and does not fully exploit inter-modal correlations. This paper proposes a crossattention feature fusion specifically designed to enhance the global correlation between camera and radar features. By dynamically adjusting feature weights through cross-attention, our approach significantly improves feature integration. Furthermore, we propose a depth-weighted voting fusion strategy to select the most accurate sensor depth, thereby enhancing decision-making stability. Experimental results on the nuScenes dataset show substantial improvements, with mean Average Precision (mAP) of 0.399 and mean Average Translation Error (mATE) of 0.602, highlighting the effectiveness of our approach in enhancing the robustness and accuracy of multi-modal fusion.