# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 1 Introduction  
Perception is essential for enabling autonomous vehicles to understand their surrounding traffic environment, leading to significant advancements in research. The release of several large-scale open-source datasets [1, 2, 3] has facilitated the development of highly effective models [4, 5, 6, 7]. However, single-vehicle perception systems have notable limitations, primarily due to the restricted range of vehicle-mounted sensors, which can create blind spots in detection. Additionally, outfitting vehicles with a dense array of sensors can be prohibitively expensive. Vehicle-infrastructure cooperative perception addresses these challenges by facilitating information sharing between vehicles and infrastructure, thereby enhancing the perception capabilities of autonomous vehicles [8; 9]. It extends the perception range and reduces blind spots, effectively mitigating some limitations inherent in single-vehicle systems [10; 11; 12; 13]. Infrastructure sensors, typically installed at higher positions, provide different viewing angles compared to vehicle-mounted sensors, enabling vehicles to detect obstacles at greater distances and within blind spots that would otherwise remain invisible. Furthermore, vehicle-infrastructure cooperative perception enhances the robustness and reliability of the overall perception system; in the event of a failure in the vehicle's perception system, the infrastructure can serve as a backup, ensuring continued functionality. Despite its potential, research on vehicle-infrastructure cooperative perception is still in its early stages and faces numerous challenges. A critical issue is the effective fusion of features from vehicle and infrastructure sensors, which directly impacts the performance of cooperative perception. Therefore, there is an urgent need to explore effective methods for integrating vehicle-infrastructure features. Our research focuses on the fusion of vehicle-infrastructure features to enhance the perception performance of cooperative systems. Differences in installation angles, resolutions, and environmental conditions between vehicle and infrastructure sensors lead to distinct distributions of acquired feature information, resulting in a domain gap. This discrepancy complicates the direct fusion of features, limiting the maximization of cooperative performance. Previous work has explored various methods for effective feature fusion. For instance, F-Cooper [14] employs the max-out method to highlight important features and filter out trivial ones, and Ref. [15] concatenates vehicle-infrastructure features; meanwhile, [PERSON] et al. [16] implement a weighting scheme for merging. However, these methods typically perform fusion at a single resolution, which limits their ability to fully leverage complementary information across different scales, such as local spatial details and global semantic context. Additionally, existing methods do not sufficiently account for distribution differences between vehicle and infrastructure features. Simple concatenation or weighting can lead to interference between these two feature sources, resulting in inconsistently or suboptimally fused features and ultimately degrading detection accuracy. Currently, datasets in the field of vehicle-infrastructure cooperative perception include both simulation datasets and real-world datasets. However, simulation datasets do not fully capture the complexity and uncertainty of real-world scenarios, such as calibration errors, localization errors, delays, and communication issues, which are practical challenges that require urgent investigation [17]. The only existing real-world dataset, DAIR-V2X [18], is insufficient for verifying the generalization of algorithms. Moreover, existing vehicle-infrastructure perception datasets typically only contain annotations for vehicles, lacking other important categories like pedestrians and cyclists, which restricts comprehensive studies of traffic scenarios.