###### Abstract  
Cooperative perception, as a critical technology of intelligent connected vehicles, aims to use wireless communication technology to interact and fuse environmental information obtained by edge nodes with local perception information, which can improve vehicle perception accuracy, reduce latency, and eliminate perception blind spots. It has become a current research hotspot. Based on the analysis of the related literature on the Internet of vehicles (IoV), this paper summarizes the multi-sensor information fusion method, information sharing strategy, and communication technology of autonomous driving cooperative perception technology in the IoV environment. Firstly, cooperative perception information fusion methods, such as image fusion, point cloud fusion, and image-point cloud fusion, are summarized and compared according to the approaches of sensor information fusion. Secondly, recent research on communication technology and the sharing strategies of cooperative perception technology is summarized and analyzed in detail. Simultaneously, combined with the practical application of V2X, the influence of network communication performance on cooperative perception is analyzed, considering factors such as latency, packet loss rate, and channel congestion, and the existing research methods are discussed. Finally, based on the summary and analysis of the above studies, future research issues on cooperative perception are proposed, and the development trend of cooperative perception technology is forecast to help researchers in this field quickly understand the research status, hotspots, and prospects of cooperative perception technology. IoV; autonomous driving; cooperative perception; multi-sensor information fusion; DSRC; C-V2X; congestion control 2022220  
signs, road information, obstacle information, and so on, is available [3; 4]. However, autonomous vehicles above level 3 require continuous, real-time situational awareness of the surrounding environment [5], which is difficult to achieve by only relying on the perceptual ability of the vehicle itself for the following reasons:  
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* Insufficient environmental perception information. The perception module of autonomous vehicles primarily relies on numerous onboard sensors, such as LiDAR, cameras, millimeter-wave radar, etc. [6]. However, affected by various factors, such as sensor characteristics, obstacle occlusion, illumination, and bad weather, the vehicle's perception range is limited, resulting in blind spots in the field of view and making it difficult to provide a full range of perception information for autonomous driving, which will cause autonomous vehicles to fail to detect imminent danger in a timely manner. For example, a Tesla Model X perception system mistakenly identified the white side of a tractor turning left in front of it as the sky, resulting in an accident in 2016 [7]. In 2019, a Tesla Model 3 Autopilot system driving at high speed failed to accurately identify the vehicle in front and perpendicular to itself and make braking decisions, resulting in a serious traffic accident [8]. * It is difficult for in-vehicle computing systems to process a large amount of multisource heterogeneous sensor data in real-time. There is no unified format for various sensor data types, and the fusion processing is complicated. In addition, for the object recognition network of an RGB camera, if the image resolution is 320 \(\times\) 320 KB and the frequency of generating data is 50 Hz, the amount of data, in this case, will reach 14 MB/S [9]. The current method of processing massive amounts of data, usually equipped with high-performance computers for autonomous driving, will greatly increase the cost of autonomous vehicles. In addition, the diversity of multisource and heterogeneous sensor data formats will also increase the difficulty of data processing.