Vehicular communication is recently considered as one of the key future technology to improve the safety of vehicles, the efficiency of traffic and the comfort for both drivers and pedestrians. Vehicular communications, based on IEEE 802.11p, use the Enhanced Distributed Channel Access (EDCA) algorithm to support different levels of Quality of Service (QoS). In this paper, a machine learning neural network with Markov chain approach is proposed to ensure the delivery of urgent safety messages to the receiver whatever the situation of the network. We propose to control the rate of periodic messages in Control Channel (CCH), by modifying the back-off parameters according to the state of the buffer. We also use Radial Basis Function Neural Network (RBFNN) to adjust the EDCA back-off parameters, using the following parameters: the priority of message (P), the sensitivity of road (S), the threshold of buffer (T), and the type of vehicle (V). Our simulation is done using SUMO 0.22 simulator, NS 2.34 and awk scripts; the simulation was applied on Hamra area (Lebanon). The results show that our proposed models perform better compared to the IEEE 802.11p in terms of packet delivery ratio, throughput and end-to-end delay.

车辆通信最近被认为是提高车辆安全性，交通效率以及驾驶员和行人舒适度的未来关键技术之一。基于IEEE 802.11p的车辆通信使用增强型分布式信道访问（EDCA）算法来支持不同级别的服务质量（QoS）。在本文中，提出了一种采用马尔可夫链方法的机器学习神经网络，以确保无论网络情况如何，都可以向接收者发送紧急安全消息。我们建议通过根据缓冲区的状态修改退避参数来控制“控制信道”（CCH）中定期消息的速率。我们还使用径向基函数神经网络（RBFNN）使用以下参数来调整EDCA补偿参数：消息的优先级（P），道路的敏感度（S），缓冲区的阈值（T）和车辆类型（V）。我们的仿真是使用SUMO 0.22模拟器，NS 2.34和awk脚本完成的；该模拟应用于黎巴嫩的哈姆拉地区。结果表明，相对于IEEE 802.11p，我们提出的模型在数据包传输率，吞吐量和端到端延迟方面表现更好。