###### Abstract
In order to make up for the insufficiency of autonomous driving perception capability and data calculation above level 3, advanced sensing technology, edge computing, communication, and other technologies need to be combined to build an autonomous driving cooperative perception system in the IoV environment, enhancing perception accuracy, improving the perception range, and reducing delay. Furthermore, this will provide more accurate and rich environmental information for autonomous vehicles in real-time and lay the foundation for the realization of autonomous driving above level 3 [10; 11]. Simultaneously, cooperative perception in the IoV environment can reduce the number of onboard sensors, lower the cost of autonomous vehicles, and speed up the commercialization of high-level autonomous driving. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) cooperative perception are the two main implementation methods, and the perception effect is also affected by various factors such as weather, obstacle occlusion, and computing power. At the same time, a large amount of multisource, heterogeneous sensor data must put forward higher requirements for communication performance. In the entire IoV cooperative perception system, this mainly includes the fusion, processing, and sharing of cooperative perception data. Among these, the multi-sensor information fusion method, information sharing strategies, and communication technology are the keys to realizing the cooperative perception of autonomous driving in the IoV environment. Multi-sensor information fusion is a prerequisite for cooperative perception. Compared to single-vehicle information fusion, cooperative perception multi-sensor fusion in the IoV environment has more abundant information sources, wider viewing angles, stronger adaptability, and can achieve higher perception accuracy and range [5; 12]. Machine learning, as the mainstream technology of target detection, predicts and improves the perceptibility of the target by training the model, realizing the selective transmission of target perception information with the vehicle-road coordination device [9; 13]. However, the existing research on cooperative perception information fusion methods is still in the stage of rapid development, and there are still deficiencies in real environment test applications and target detection research. Wireless communication networks play a bridge role in cooperative perception in the IoV environment, which is an important guarantee for information sharing. The quality of communication will directly affect the effect of cooperative perception. In addition, the cooperative perception information sharing strategy is also crucial to ensure the accuracy and timeliness of perception. Usually, the strategy of only broadcasting the target information that the autonomous vehicle cannot perceive is used to reduce the communication load, avoid the redundancy of perception information, and better optimize the balance point of perception-fusion-communication for autonomous driving cooperative perception in the IoV environment. Perception accuracy and end-to-end latency are two essential indicators for evaluating cooperative perception technology. Some researchers concluded that deploying a multiaccess edge computing (MEC) infrastructure could improve the effectiveness of cooperative perception and greatly reduce end-to-end communication delays by comparing the perception effect and transmission delay with a single intelligent vehicle and cloud-based computing [14; 15]. Figure 1 provides an overall diagram of the critical review on cooperative perception in the IoV environment. The paper is organized as follows. Section 1 introduces the background, significance, and related research status of autonomous driving cooperative perception technology in the IoV environment and points out that cooperative perception technology plays a key role in autonomous driving.