# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 5 Conclusions  
To tackle the significant challenge of domain gap arising from sensor characteristics in real-world applications, we introduce the Multi-Scale Dynamic Feature Fusion Module. This module integrates vehicle and infrastructure features through a multi-scale fusion strategy that operates at both fine-grained spatial and broad semantic levels. This approach facilitates a more effective exploration of the correlations and complementarities between vehicle and infrastructure features.  
During the feature fusion process at each scale, we implement two key components: the Multi-Source Dynamic Interaction Module (MSDI) and the Per-Point Self-Attention Module (PPSA). The MSDI module adaptively aggregates vehicle-infrastructure features across spatial and channel dimensions in response to environmental variations, while the PPSA module optimizes feature aggregation at each spatial location through a self-attention mechanism. This combination ensures that vehicle-infrastructure features at every scale are fully utilized and effectively fused.  
Additionally, we have constructed DZGSet, a self-collected real-world cooperative perception dataset that includes annotations for multiple object categories. Extensive validation on both the DAIR-V2X and the self-collected DZGSet datasets demonstrates state-of-the-art performance. Compared to advanced cooperative perception methods, our approach achieves improvements of 0.037 in [EMAIL_ADDRESS] and 0.043 in [EMAIL_ADDRESS] on the DZGSet dataset, and improvements of 0.034 in [EMAIL_ADDRESS] and 0.028 in [EMAIL_ADDRESS] on the DAIR-V2X dataset. The ablation studies further confirm that each proposed module significantly contributes to these performance gains.  
While LiDAR point clouds provide precise 3D information, they often lack color and texture details. By combining high-resolution images with LiDAR point clouds, we can compensate for this deficiency, resulting in a more comprehensive environmental perception. In future work, we aim to extend our method to incorporate multi-modal data integration.  
In conclusion, the proposed method achieves excellent vehicle-infrastructure cooperative perception performance by efficiently fusing vehicle-infrastructure features, thereby providing high-precision and robust data support that serves as a solid foundation for decision making in autonomous vehicles.  
Conceptualization, J.L.; methodology, J.L.; software, J.L.; validation, J.L.; formal analysis, J.L.; investigation, J.L.; resources, X.W. and P.W.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, X.W. and P.W.; visualization, J.L.; supervision, X.W. and P.W.; project administration, X.W. and P.W.; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Commission of Shanghai Municipality (22 dz1203400).  
Not applicable.  
Data Availability Statement: The datasets used in this paper are DAIR-V2X and DZGSet. The DAIR-V2X dataset can be accessed at: [[https://github.com/AIR-THU/DAIR-V2X](https://github.com/AIR-THU/DAIR-V2X)]([https://github.com/AIR-THU/DAIR-V2X](https://github.com/AIR-THU/DAIR-V2X)) (accessed on 8 April 2024). For the DZGSet dataset, please contact the corresponding author directly.  
We thank [PERSON] for their contributions to resources, manuscript review, and supervision.  
The authors declare no conflicts of interest.