# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 4 Experiment
### Datasets  
#### 4.1.1 DZGSet Dataset  
To address the limitations of existing vehicle-infrastructure cooperative perception datasets, particularly regarding the number of object categories, we developed the DZGSet dataset. This dataset is collected from real-world scenarios and encompasses multi-modal, multi-category data for vehicle-infrastructure cooperative perception. The dataset construction process includes the following:  
Hardware configuration: The collection of the DZGSet dataset relies on an advanced hardware setup, featuring an 80-line resolution LiDAR and a 1920 \(\times\) 1080 pixel camera installed on infrastructure, alongside a 16-line resolution LiDAR and a camera with the same specifications mounted on vehicle. More detailed specifications of the sensors are shown in Table 1. Both vehicle and infrastructure are equipped with time servers to ensure precise synchronization across all devices. Additionally, vehicles are outfitted with a Real-Time Kinematic (RTK) system for the real-time acquisition of high-precision pose information. This comprehensive configuration enhances the richness and accuracy of the dataset by capturing detailed multi-modal data from both vehicle and infrastructure perspectives. Software configuration: On the software side, high-precision time synchronization is achieved through time servers, allowing accurate alignment of data from various sources. Furthermore, precise calibration techniques are employed to ensure the spatial coordinate alignment between the different sensors, enabling data from diverse perspectives to be processed within a unified spatial coordinate system. Data collection and annotation: Data are collected in real-world road environments through multiple driving experiments. During each experiment, data from both vehicle \begin{table}
\begin{tabular}{l c c c c} \hline
**Sensor** & **Type** & **Details** & **Manufacturer** & **Country of Origin** \\ \hline Inf. LiDAR & RS-Ruby Lite & \begin{tabular}{c} 80 beams, 10 Hz capture frequency, 360\({}^{\circ}\) \\ horizontal FOV, \(-25^{\circ}\) to 15\({}^{\circ}\) vertical FOV, \\ \(\leq\)250 m range, \(\pm\)3 cm accuracy \\ \end{tabular} & RoboSense & Shenzhen, China \\ \hline Inf. Camera & \begin{tabular}{c} Deeproute Vision \\ Camera \\ \end{tabular} & \begin{tabular}{c} RGB, 25 Hz capture frequency, \\ 1920 \(\times\) 1080 resolution \\ \end{tabular} & DeepRoute & Shenzhen, China \\ \hline Veh. LiDAR & RS-LiDAR-16 & \begin{tabular}{c} 16 beams, 10 Hz capture frequency, 360\({}^{\circ}\) \\ horizontal FOV, \(-15^{\circ}\) to 15\({}^{\circ}\) vertical FOV, \\ \(\leq\)150 m range, \(\pm\)2 cm accuracy \\ \end{tabular} & RoboSense & Shenzhen, China \\ \hline Veh. Camera & \
\begin{tabular}{c} LI-USB30- \\ AR203 ZWDR \\ \end{tabular} &
\begin{tabular}{c} RGB, 30 Hz capture frequency, \\ 1920 \(\times\) 1080 resolution \\ \end{tabular} & Leopard Imaging & Fremont, CA, USA \\ \hline \end{tabular}
\end{table} 
Table 1: Sensor specifications in DZGSet. Veh. stands for vehicle, and Inf. stands for infrastructure. and infrastructure sensors are recorded. Professional annotators then perform high-quality manual annotations on objects based on image and point cloud data. The DZGSet dataset features 3D annotations for LiDAR point clouds and 2D annotations for camera images. The dataset primarily annotates on three object categories: \"car\", \"pedestrian\", and \"cyclist\". When annotating point clouds, annotators assign a category and ID to each object, along with a 7-degree-of-freedom (DoF) 3D bounding box. The parameters for the 3D bounding box include the center position coordinates (\(x\), \(y\), \(z\)) and the dimensions and orientation defined by length, width, height, and yaw. During image annotation, annotators also label each object's category and ID, along with its 4-degree-of-freedom 2D bounding box. The parameters for the 2D bounding box consist of the center position coordinates (\(x\), \(y\)) and the dimensions defined by length and width.