CoFormerNet: A Transformer-Based Fusion Approach for Enhanced Vehicle-Infrastructure Cooperative Perception

Abstract

Vehicle–infrastructure cooperative perception is becoming increasingly crucial for autonomous driving systems and involves leveraging infrastructure’s broader spatial perspective and computational resources. This paper introduces CoFormerNet, which is a novel framework for improving cooperative perception. CoFormerNet employs a consistent structure for both vehicle and infrastructure branches, integrating the temporal aggregation module and spatial-modulated cross-attention to fuse intermediate features at two distinct stages. This design effectively handles communication delays and spatial misalignment. Experimental results using the DAIR-V2X and V2XSet datasets demonstrated that CoFormerNet significantly outperformed the existing methods, achieving state-of-the-art performance in 3D object detection.