# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 1 Introduction  
In recent years, with the increasing improvement of living standards, people's demand for transportation tools has gradually increased, and the number of civil vehicles has also been increasing. By the end of 2020, the number of civil cars in China reached 280 million, and the mass popularization of cars has caused increasingly serious urban traffic jams. The ensuing traffic safety issues have attracted people's attention. Even though the government has come up with new laws and regulations to regulate drivers' driving behavior, there are nearly 200,000 traffic accidents in China every year, resulting in a large number of casualties and property losses. The development of national road traffic should not only pay attention to the quantity and ignore the quality. While realizing the improvement of people's living standard, we should also deal with the social hidden dangers brought by it. Given driver problems, manual driving can reduce some dangers for traffic safety. Therefore, in order tosolve the traffic safety hazards caused by the increase in vehicles, people put their eyes on more intelligent and standardized management of unmanned driving technology [1]. Driverless technology, which allows drivers to safely free their hands and reduce the incidence of traffic accidents, has always been a key direction in the field of automotive research. Domestic scholars' research on unmanned driving technology started in 2014, while foreign research on it started as early as 2004. Although domestic and foreign experts and scholars have made many major breakthroughs in unmanned driving technology and 5G communication technology has become a new driving force for its development, the key to whether unmanned driving technology can be fully utilized lies in whether the communication capacity of vehicle-mounted networks can be improved. Without a high level of in-vehicle communication capability, the adoption of driverless technology will also be difficult. The vehicular network is an important module for building intelligent transportation, and the successful construction of a vehicular network is of great significance in solving traffic congestion and reducing the incidence of traffic accidents. Therefore, the improvement of the communication capability of vehicle networks is the key field of vehicle network technology research. Cooperative transmission technology is the breakthrough to improve the communication technology of vehicular networks. The application of multi-point cooperative transmission technology to the communication of vehicular networks can effectively save the transmission time of the vehicular network, improve the transmission efficiency of data and signals as much as possible, and reduce the probability of transmission errors in the vehicular network and the loss of the wrong frequency and spectrum. In the traditional communication and transmission network, all users can only receive the signal transmitted by a base station, which is a single point transmission. One of the key technologies of LTE-A is the cooperative service of relay selection with the help of neighboring vehicle network users. By coordinating the information of multiple base stations and forming a cooperative overlapping area of base stations to transmit signals to terminals, the communication efficiency can be improved. The application of multi-point cooperative transmission technology [2] in vehicular networks can transmit signals to edge terminals by using adjacent vehicles as relay nodes [3] and occupy communication resources originally belonging to other vehicular network terminals. The connectivity of a vehicular network can be greatly improved by adding adjacent vehicles to the communication of the vehicular network in the form of relay nodes. The selection of relay nodes determines the performance of the multi-point cooperative transmission system. Therefore, how to better coordinate transmission relay node selection has become the focus of attention.