## 1 Introduction
In short, the communication process of V2X cannot adapt to the characteristics of the high-speed mobility of vehicles and dynamic changes of data, which eventually leads to low utilization and a poor flexibility of resource allocation in the V2X network. Therefore, to cope with this severe challenge, it is necessary to deeply fuse V2X massive heterogeneous data, comprehensively analyze communication behavior, and design congestion control algorithms suitable for vehicle communication to alleviate network congestion and provide efficient network services [11]. The applications of V2X are diversified, and its communication demands are also diversified at the same time [12; 13; 14; 15]. For different types of applications, the importance of information (system broadcast information, safety application information, traffic management-type application information and entertainment application information, etc.) in vehicle communications is different, which leads to different requirements for network performance parameters during transmission. Moreover, Quality of Service (QoS) plays a crucial role in influencing the different services in V2X. Currently, most traditional congestion control algorithms consider how to utilize bandwidth or delay more efficiently. In recent years, scholars at home and abroad have done a lot of in-depth research on DRL-based V2X congestion control and proposed various algorithms [16; 17; 18], however, these algorithms do not combine different service requirements such as safety, convenience and entertainment in vehicular networking and QoS to control network congestion in V2X. This makes it difficult to meet the diversified service requirements of large-scale vehicle users. This paper proposes an intelligent congestion control strategy in heterogeneous V2X based on deep reinforcement learning (ICCDRL) to address the diverse needs of network applications for connected vehicle end devices. We summarize the major contributions of this work as follows. * Firstly, through an analysis of the heterogeneous V2X network architecture, a congestion control model based on deep reinforcement learning (DRL) is established using Markov's random memoryless property;
* Secondly, we obtain the QoS parameters of different services in vehicle communications, calculate the minimum cost according to the importance of the service, and define the overhead weights and congestion sensitivity factor \(\lambda_{ODL}\) according to the different importance of services;
* In addition, by observing the current network state information, using the deep reinforcement learning PPO2 algorithm to learn from historical experience, and with the help of congestion-sensitive factors, a large amount of historical QoS data is used as a training set to optimize actions by combining the current state information of the network and selecting the congestion window size at the next moment. Thus, an intelligent congestion control strategy driven by QoS on-demand is formed;
* Finally, we build the ns-3 simulation platform to verify the performance of the ICCDRL proposed in the paper. The paper consists of five chapters and is summarized below: First, related works are discussed in more detail in Section 2. Then, in Section 3, we depict the construction of the Model of Intelligent Congestion Control for Heterogeneous Vehicle Networking Based on DRL. Following this, the details of the ICCDRL technology are introduced in the same Section. Section 4 describes the design and the process of the experiments in detail before introducing and analyzing the high performance and convergence of the ICCDRL with specific information. Finally, the paper is summarized in Section 5.