## 2 Cooperative Perception Information Fusion
### Point Cloud Fusion  
The fusion of point cloud data from various angles and viewpoints has distinct advantages over the fusion of picture data in terms of the perception of the surrounding \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors, Year** & **Key Research Points** & **Findings** & **Remarks** \\ \hline \multirow{4}{*}{[PERSON] et al. 2020 [15]} & Multiple roadside camera & The detection time was & Distributed interactive fusion \\  & perception data mapping to & increased by about 45\%, and & deployment of sensors for a \\  & form a global semantic & the detection accuracy was & wider range of cooperative \\  & description. & increased by about 10\%. & perception without increasing \\ \hline \multirow{4}{*}{[PERSON] et al. 2020 [21]} & Used a lossy learning method & Image compression & \\  & for image compression to & requirements were high, and & \\  & relieve the pressure on & late fusion results of & \\  & wireless communication & segmentation mask cascades & \\  & channels. & were optimal. & \\ \hline \multirow{4}{*}{[PERSON] et al. 2021 [23]} & Based on the separation & & \\  & principle of static background & & \\  & and dynamic foreground, the & & \\  & dynamic foreground was & & \\  & extracted, and the static & & \\  & background and dynamic & & \\  & foreground were re-fused by a & & \\  & generative adversarial & & \\  & network. & & \\ \hline \multirow{4}{*}{[PERSON] et al. 2018 [24]} & A bird’s-eye view generated & & \\  & by integrating the perception & & \\  & information of other vehicles & & \\  & expanded the perception & & \\  & range, shared the processed & & \\  & image information, and & & \\  & reduced the network burden. & & \\ \hline \multirow{4}{*}{[PERSON] et al. 2018 [25]} & Utilized image feature point & Fusion of perception & \\  & matching for data fusion to & information from other & \\  & form vehicle cooperative & vehicles and conversion to its & \\  & perception with a common & own coordinate system. & \\  & field of view. & & \\ \hline \multirow{4}{*}{[PERSON] et al. 2018 [27]} & Used feature point matching & & \\  & to estimate geometric & & \\  & transformation parameters to & & \\  & solve perception blind spots & & \\  & in congestion. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary and analysis of image fusion research methods. Figure 3: Object detection from different angles based on image fusion. environment. LiDAR has the advantages of high spatial resolution, a wide detection range, and little influence from light. It plays an important role in the application of target recognition, ranging, and positioning in autonomous driving. This subsection describes and evaluates the available research in accordance with data-level, feature-level, and decision-level fusion categories [28] and focuses on the impact of abundant point cloud data and constrained network bandwidth on the efficacy of cooperative perception. Data-level fusion research started early, with high precision but a large amount of data. This fusion method does not require any preprocessing of the data, and the heterogeneity of different data processing algorithms will not affect the accuracy of data being shared between vehicles. [PERSON] et al. [29] were the first to suggest employing shared raw point cloud data to achieve cooperative perception, and they referenced a neural network for spare point-cloud object detection to detect objects in low-density point cloud data. This method effectively improves the perception range and detection accuracy of the vehicle. Simultaneously, it proves that existing DSRC communication technology can meet the data transmission requirements of point cloud fusion in the region of interest (ROI). Similarly, [PERSON] et al. [30] introduced a state estimation framework for multivehicle LiDAR localization and sensor data fusion that utilized raw sensor data between different vehicles and achieved ground truth generation in an offline manner.