## 2 Cooperative Perception Information Fusion
### Point Cloud Fusion
At the same time, the registration method better matches point cloud data from diverse views, demonstrating that using perceptual data from multiple perspectives has a better auxiliary effect on cooperative perception, but that this increases the detection time. Feature-level fusion refers to extracting feature information from original data and then performing fusion, which is currently the most studied and widely used fusion method, considering that the original point cloud data sharing will be limited by the bandwidth and delay of the wireless communication network. Based on a previous study [29], [PERSON] et al. [31] innovatively proposed a point cloud feature-based cooperative perceptual fusion scheme, including a combined voxel feature fusion and spatial feature fusion method. This scheme can effectively improve the accuracy of perception while meeting the requirements of time delay and limited communication bandwidth. The results show that the detection effect at a range of 20 m is improved by about 10%, and the further distance is improved by about 30%. [PERSON] et al. [32] used a multi-target tracking method [33] based on vehicle-mounted LiDAR and a voxel clustering algorithm to obtain the state of the surrounding environment. This method fuses the preliminary tracking results with the perception information of the RSU and other vehicles to generate the motion trajectory of the target vehicle. By using point cloud data from multiple objects, it can continuously operate under the condition of limited LiDAR perception or V2V communication failure, perceive the position of surrounding vehicles, ensure the stability of cooperative perception, and improve the accuracy of target tracking. Decision-level fusion refers to extracting the detected objects from each individual sensor and fusing the detection results. The fusion speed is fast and it is more suitable for data sharing, but there are few studies on it at present. [PERSON] et al. [34] proposed an early fusion (data-level fusion) and late fusion (decision-level fusion) 3D object detection cooperative perception scheme. This scheme adjusts the tradeoff between different fusion methods according to the sensor visibility and detection distance, selecting early fusion for high sensor visibility and short-range detection to ensure the detection effect and reduce the communication cost. On the contrary, if the visibility is poor and the point cloud data at the far position is small, late fusion is selected, and, finally, the two detected 3D bounding boxes are fused and shared, as shown in Figure 4. Experiments show that by fusing LiDAR data from different stages and positions of roadside units, spatial diversity with overlapping fields of view can be fully utilized to increase the density of point clouds, reduce false negative detection, more accurately estimate the bounding boxes of detected objects, and improve object detection accuracy. In short, the multi-sensor data fusion method has a great impact on the timeliness and accuracy of cooperative perception. For example, sharing the original point cloud data has high sensing accuracy, but it will increase the detection time and transmission load, which cannot meet the low-latency requirements of cooperative perception. However, sharing the processed point cloud data can reduce the data transmission time, but the perceived accuracy will be reduced. Therefore, balancing the accuracy and real-time performance of perception is crucial to the effectiveness of cooperative perception. Table 3, below, summarizes the point cloud fusion methods classified by different fusion levels. Figure 4: Early and late fusion schemes for cooperative 3D object detection. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors, Year** & **Key Research Points** & **Findings** & **Remarks** \\ \hline \multirow{8}{*}{[PERSON] et al. 2019 [PERSON] et al. 2019 [PERSON] et al. 2019 [PERSON] et al. 2019 [PERSON] et al.