PnPDA⁺: A Meta Feature-Guided Domain Adapter for Collaborative Perception ^†

Although cooperative perception enhances situational awareness by enabling vehicles to share intermediate features, real-world deployment faces challenges due to heterogeneity in sensor modalities, architectures, and encoder parameters across agents. These domain gaps often result in semantic inconsistencies among the shared features, thereby degrading the quality of feature fusion. Existing approaches either necessitate the retraining of private models or fail to adapt to newly introduced agents. To address these limitations, we propose PnPDA⁺, a unified and modular domain adaptation framework designed for heterogeneous multi-vehicle cooperative perception. PnPDA⁺ consists of two key components: a Meta Feature Extraction Network (MFEN) and a Plug-and-Play Domain Adapter (PnPDA). MFEN extracts domain-aware and frame-aware meta features from received heterogeneous features, encoding domain-specific knowledge and spatial-temporal cues to serve as high-level semantic priors. Guided by these meta features, the PnPDA module performs adaptive semantic conversion to enhance cross-agent feature alignment without modifying existing perception models. This design ensures the scalable integration of emerging vehicles with minimal fine-tuning, significantly improving both semantic consistency and generalization. Experiments on OPV2V show that PnPDA⁺ outperforms state-of-the-art methods by 4.08% in perception accuracy while preserving model integrity and scalability.