## 2 Cooperative Perception Information Fusion  
Some breakthroughs have been made in the research of autonomous vehicle multi-sensor fusion methods, including image fusion, point cloud fusion, and image-point cloud fusion [3; 16; 17; 18]. Although a multi-sensor redundant combination design can make up for the insufficiency of a single sensor in perception, reduce the uncertainty of target detection, and enhance the vehicle's effective perception of surrounding environmental information, the test results in real scenarios are not ideal. For example, the fusion of camera and LiDAR  
Figure 1: An overall diagram of the critical review on cooperative perception in the Internet of vehicles environment.  
can provide high-resolution image information and reduce the impact of lighting conditions, but the perception effect is still lacking in bad weather and obstacle occlusion. Furthermore, there are still many other problems to be overcome, such as imperfect information fusion methods, large amounts of data, limited network bandwidth, and spatiotemporal alignment problems. Figure 2 depicts the research trends of cooperative perception based on multi-sensor information fusion from 2010 to 2021. The data are from Google Scholar advanced searches of cooperative perception and different sensor fusion methods.  
From the perspective of vehicle-road cooperative perception information fusion, this section summarizes the existing research on image fusion, point cloud fusion, and image-point cloud information fusion at different locations and from multiple perspectives. This section also briefly summarizes its existing challenges. Table 1, below, qualitatively summarizes the strengths and weaknesses of the commonly utilized perception-based sensors in AV based on their technical characteristics and other external factors, such as weather and illumination conditions. At the same time, it compares the perception effect of using only multi-sensor fusion and V2X fusion.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **Camera** & **LiDAR** & **Radar** & **Only Fusion** & **Fusion and V2X** \\ \hline Range & \(\sim\) & \(\sim\) & \(\surd\) & \(\sim\) & \(\surd\) \\ \hline Resolution & \(\surd\) & \(\sim\) & \(\times\) & \(\surd\) & \(\surd\) \\ \hline Distance Accuracy & \(\sim\) & \(\surd\) & \(\surd\) & - & \(\surd\) \\ \hline Velocity & \(\sim\) & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline Color Perception (e.g., traffic lights) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ \hline Object Detection & \(\sim\) & \(\surd\) & \(\surd\) & - & \(\surd\) \\ \hline Object Classification & \(\surd\) & \(\sim\) & \(\times\) & \(\surd\) & \(\surd\) \\ \hline Lane Detection & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ \hline Obstacle Edge Detection & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) \\ \hline Illumination Conditions & \(\times\) & \(\surd\) & \(\surd\) & - & \(\surd\) \\ \hline Weather Conditions & \(\times\) & \(\sim\) & \(\surd\) & - & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of the commonly employed sensors in self-driving cars, only sensors fusion, and combined V2X. The “\(\surd\)” symbol indicates that the sensor operates competently under the specific factor. The “-” indicates that the sensor performs reasonably well under the specific factor. The “\(\times\)” indicates that the sensor does not operate well under specific factors relative to the other sensors.  
Figure 2: The increasing amount of research on cooperative perception multi-sensor information fusion in the Internet of vehicles environment.