Bidirectional Complementary Cross-Attention and Temporal Adaptive Fusion for 3D Object Detection in Intelligent Transportation Scenes

Multi-sensor fusion represents a primary approach for enhancing environmental perception in intelligent transportation scenes. Among diverse fusion strategies, Bird’s-Eye View (BEV) perspective-based fusion methods have emerged as a prominent research focus owing to advantages such as unified spatial representation. However, current BEV fusion methods still face challenges with insufficient robustness in cross-modal alignment and weak perception of dynamic objects. To address these challenges, this paper proposes a Bidirectional Complementary Cross-Attention Module (BCCA), which achieves deep fusion of image and point cloud features by adaptively learning cross-modal attention weights, thereby significantly improving cross-modal information interaction. Secondly, we propose a Temporal Adaptive Fusion Module (TAFusion). This module effectively incorporates temporal information within the BEV space and enables efficient fusion of multi-modal features across different frames through a two-stage alignment strategy, substantially enhancing the model’s ability to perceive dynamic objects. Based on the above, we integrate these two modules to propose the Dual Temporal and Transversal Attention Network (DTTANet), a novel camera and LiDAR fusion framework. Comprehensive experiments demonstrate that our proposed method achieves improvements of 1.42% in mAP and 1.26% in NDS on the nuScenes dataset compared to baseline networks, effectively advancing the development of 3D object detection technology for intelligent transportation scenes.