Research on Cooperative Vehicle–Infrastructure Perception Integrating Enhanced Point-Cloud Features and Spatial Attention

Vehicle–infrastructure cooperative perception (VICP) overcomes the sensing limitations and field-of-view constraints of single-vehicle intelligence by integrating multi-source information from onboard and roadside sensors. However, in complex urban environments, system robustness—particularly regarding blind-spot coverage and feature representation—is severely compromised by occlusion (static and dynamic) and distance-induced point cloud sparsity. To address these challenges, this paper proposes a 3D object detection framework incorporating point cloud feature enhancement and spatial adaptive fusion. First, to mitigate feature degradation under sparse and occluded conditions, a Redefined-SENet (R-SENet) attention module is embedded into the feature encoding stage. This module employs a dual-dimensional squeeze-and-excitation mechanism—across pillars and intra-pillar points—to adaptively recalibrate key geometric features. Concurrently, a Feature Pyramid Backbone Network (FPB-Net) is constructed to enhance unified target modeling across varying distances via multi-scale extraction and cross-layer aggregation. Second, a Spatial Adaptive Feature Fusion (SAFF) module is introduced to resolve feature heterogeneity and spatial misalignment. By explicitly encoding feature origins and leveraging spatial attention, SAFF enables dynamic weighting and complementary fusion of fine-grained vehicle-side features and global roadside semantics. Experiments on the DAIR-V2X benchmark and a custom dataset demonstrate that the proposed method outperforms state-of-the-art approaches, achieving Average Precision (AP) scores of 0.762 and 0.694 at IoU 0.5, and 0.617 and 0.563 at IoU 0.7, respectively. Furthermore, the inference speed satisfies real-time requirements, validating the method’s effectiveness and potential for engineering deployment.