## 2 Cooperative Perception Information Fusion
### Image Fusion  
Research on cooperative perception based on image information in the IoV environment started early and is relatively mature because the images have the advantages of high resolution and a good target classification effect, the research and applications of visual sensors are older, and the price is cheap. Image data is a common type of shared data between autonomous vehicles and other perception nodes. The amount of raw video data collected by the camera is large among all kinds of environmental perception information, and the transmission of each video frame will cause the network to be overloaded, increase the transmission latency, and affect the timeliness of cooperative perception. In order to reduce the heavy network load caused by the amount of data, traditional image compression [19], deep learning [20], and other methods reduce the size of the transmitted image data to ensure the timeliness of cooperative perception. [PERSON] et al. [21] adopted image compression to reduce the size of data transmission. Combined with a lossy learning image compression method, the overall bit rate is reduced by an auto-encoder with an adversarial loss function [22], which effectively relieves pressure on the wireless communication channel. Different from traditional methods, [PERSON] et al. [23] used the principle of separating static background and dynamic foreground, as well as the technique of video frame background removal and noise reduction calculated by pixel value, to quickly extract dynamic foreground from video frames. By using the generative adversarial network technique, reintegrating the static background and dynamic foreground into a new video frame, the correct driving decisions can be made based on the static background and dynamic foreground. According to the real dataset test, the method can reduce the transmission load of the video image data by more than 85 percent and decrease the processing time of perception information to 27.7% of the original on the premise of ensuring that important environmental perception information is not lost, effectively realizing the cooperative environment perception of autonomous vehicles and roadside infrastructure. In addition, many scholars have studied the existing image fusion strategies for limitations and blind spots in the perceived field of view and range. [PERSON] et al. [24] used a deep learning method to extract key information from the vision system and expanded the perception range of the host vehicle by fusing the bird's-eye view generated by the perception information of other vehicles in the vehicle network. This approach deepened the self-driving vehicle's ability to understand its environment. [PERSON] et al. [25] used cooperative relative positioning and map merging [26] to realize the cooperative perception of vehicles with a common field of view by sharing the image perception information of adjacent vehicles with the host vehicle. [PERSON] et al. [27] used V2V communication technology, based on a 3D inter-vehicle projection model and selected feature point matching to estimate the geometric transformation parameters to achieve inter-vehicle image information fusion through deep-affine transformation. It effectively overcomes the problem of perceived blind spots in traffic jams, but it overlooks the impact of viewing angles, and, in some cases, there will be size deviations. If the perspective issue is taken into account, the scalable 5G multi-access edge computing-driven vehicle infrastructure cooperative system has a broader prospect. Through the distributed data fusion method, the environmental information detected by RSU cameras in multiple areas is interactively and perceptually fused.