## 2 Cooperative Perception Information Fusion
### Image-Point Fusion
In addition, some studies have compared the perception effects of different sensor combinations and found that the data fusion of cameras and LiDARs has richer environmental information and better target detection performance. However, due to the difference in the number of wire bundles and the detection distance, the amount of environmental information data collected by LiDAR is very different, which will have a certain negative impact on the timeliness of cooperative perception and the accuracy of target recognition. [PERSON] et al. [51] proposed a LiDAR-camera sensor fusion method for long-distance point cloud sparse problems. [PERSON] et al. [4] proposed an image-point cloud-based cooperative perception system, which mainly solves the problem of blind spot perception with intelligent sensors of AV at intersections. The roadside terminal is used to send the global map of the laser point cloud to the autonomous vehicle. In turn, the vehicle sends its position to the RSU. Finally, the RSU sends the perception result of the intersection to the vehicle. The experimental results show that the method improves the perception range and accuracy of vehicles at intersections. However, the real-time performance of perception is poor. References [52; 53] utilize image and point cloud fusion algorithms for better environmental perception. The final decision is made by the telecommunication control unit by extracting objects from the environment that affect the multi-channel V2X communication system. In addition, an attempt is made to solve the problem of communication performance degradation due to the surrounding environment of V2X communication vehicles to improve the stability of multi-channel telecommunication control units. [PERSON] et al. [54] achieved a breakthrough in the camera-LiDAR fusion strategy to cope with different scene changes. By cascading point clouds and images to generate fusion networks, including single-modality modes of laser point clouds only and multimodality modes of laser point clouds and camera images, it dynamically responds to more environmental changes and achieves higher perceived accuracy. Table 4 presents the existing research on image-point cloud fusion methods. In addition, the spatial and temporal alignment of sensors is a prerequisite for information fusion in the sensor information fusion process. Spatial alignment considers the coordinate transformation of the targets of sensor data from different sources. In contrast, temporal alignment mainly focuses on how to solve the problem of the time difference between sensing information collected from edge nodes and received by other vehicles. Spatial alignment is often based on a constant turn rate and acceleration motion model. Temporal alignment typically transforms object coordinates using unscented transformations. Based on these two key problems, research on spatial and temporal alignment in multi-sensor fusion is equally important [55; 56; 57]. [PERSON] et al. [55] summarized and analyzed the existing alignment methods, proposed using the unpredicted sender state for transformation, and compared it with the existing coordinate transformation method using the compensated sender motion prediction. Experiments show that the former has more obvious advantages, which not only reduces the computational complexity but also has more accurate state estimation. In multi-sensor perception data fusion in advanced fusion architecture, each sensor and each vehicle will preprocess their raw data, and the temporal alignment and spatial alignment in the post-data fusion process are essential factors to ensure the accuracy of the results [58].