## 2 Cooperative Perception Information Fusion
### Summary  
Based on the existing literature, this section summarizes the current research status of cooperative perception technology for autonomous driving in the IoV environment from the perspective of cooperative perception information fusion methods, focusing on summarizing image fusion, point cloud fusion, and image-point cloud fusion methods. By comparison, we found that the fusion of image point clouds under multimodality and multi-view perspectives is the best method for cooperative perception (as shown in Table 3). Compared to single-vehicle multi-sensor fusion, cooperative perception information fusion has more advantages in terms of sensing range, accuracy, and reliability. Comparing the information fusion at different stages, the fusion and sharing of the processed data can not only reduce the amount of data transmitted but also significantly reduce the network communication load. The fusion and sharing of the original data are better for cooperative perception, but they will increase the communication burden and have a certain  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors, Year** & **Key Research Points** & **Findings** & **Remarks** \\ \hline \multirow{6}{*}{[PERSON] et al. 2019 Jiang et al. 2019 Jiang et al. 2019 (2019)} & Used millimeter-wave radar & \multirow{6}{*}{Strong anti-interference ability. However, the detection frequency was low and cannot meet the real-time requirements.} \\  & to filter the target and map it & & \\  & to the image to obtain the & & \\  & region of interest, weighted & & \\  & the detection value and & & \\  & estimated value of the two, & & \\  & and improved the perception & & \\  & accuracy. & & \\ \hline \multirow{6}{*}{Fu et al. 2020 Fu et al. 2020 (2020)} & A fusion perception method & \multirow{6}{*}{Both horizontal and vertical had better detection results.} & \multirow{6}{*}{No actual deployment.} \\  & of roadside camera & & \\  & millimeter-wave radar was & & \\  & proposed, and the Kalman & & \\  & filter was used to evaluate the & & \\  & pros and cons of the & & \\  & perception results. & & \\ \hline \multirow{6}{*}{[PERSON] et al. 2020 Wang et al. 2020 (2020)} & Combined with real road & \multirow{6}{*}{Fast and automatic acquisition of roadside perception fusion} & Attempt to combine depth \\  & scenes, filtered background objects detected by radar to & & \\  & achieve the automatic & & \\  & calibration of multiple & & \\  & sensors. & & \\ \hline \multirow{6}{*}{Saito et al. 2021 Saito et al. 2021 (2021)} & Projected the point cloud data & \multirow{6}{*}{Improved target shape recovery rate and discernible distance.} & Further adjustments to real-time models and panoramic cameras to expand the fusion range. \\  & to the accuracy of target & & \\  & detection. & & \\ \hline \multirow{6}{*}{Duan et al. 2021 Duan et al. 2021 (2021)} & An image–point cloud & \multirow{6}{*}{Effectively extended the detection range.} & A large amount of calculation and poor real-time performance. \\  & to the detected objects within the & & \\  & perception range to the & & \\  & vehicle. & & \\ \hline \multirow{6}{*}{Gu et al. 2021 Gu et al. 2021 (2021)} & Utilized point cloud and image concatenation to form a & \multirow{6}{*}{Multimodality for more environmental changes.} & Improved the detection accuracy of the road and had good real-time performance. \\  & point cloud–image multimodal & & \\  & mode fusion network. & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary and classification of research on image–point cloud fusion methods.  
negative impact on the cooperative perception results. In the future, direct and effective cooperative perception fusion algorithms, and the interactive fusion of distributed sensor data, will become research trends. At the same time, the dataset construction of vehicle-road cooperation will be a key factor in ensuing research on and evaluation of vehicle-road cooperation algorithms. Table 5 shows a comparison of different sensor fusion methods.