V2V-APG: Adversarial Progressive Generalization for Vehicle-to-Vehicle Cooperative Perception

Vehicle-to-Vehicle (V2V) cooperative perception markedly broadens scene comprehension by aggregating observations from multiple connected vehicles. However, existing methods often suffer from performance degradation when confronted with domain gaps between training and deployment environments. To bridge this domain gap, we introduce V2V-APG, an Adversarial Progressive Generalization framework for cross-domain V2V cooperative perception. V2V-APG first leverages a Spatial-Interaction Perception Attention network to simultaneously model intra-vehicle spatial dependencies and inter-vehicle relationships, yielding rich, discriminative features. Additionally, we propose a Domain-Aware Dynamic Gradient Reversal Layer to dynamically align feature distributions based on real-time domain discrepancies. Finally, a Confidence-Guided Progressive Pseudo-label Refinement strategy iteratively refines target-domain pseudo-labels through adaptive thresholding and curriculum learning, boosting supervision quality without ground-truth annotations. Extensive experiments on the OPV2V and V2V4Real datasets demonstrate that V2V-APG outperforms state-of-the-art methods in cross-domain tasks, achieving superior generalization and robustness.