In Vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) is a significant mode of communication in which vehicles communicate with other moving vehicles with the aid of wireless transceivers. Due to the rapid mobility of vehicles, network connectivity over VANETs is frequently unstable, especially in sparse highways. This paper analyzes V2V connectivity dynamics by designing the microscopic mobility and lane changing decision model using an adaptive cursive control mechanism and recurrent neural network. Extensive simulators like SUMO and NS2 analyze the validity of this proposed model. The proposed analytical model provides a framework for examining the impact of mobility dependent metrics such as vehicle velocity, acceleration/deceleration, safety gap, vehicle arrival rate, vehicle density and network metric data delivery rate for characterizing the VANET connectivity of the proposed network. The simulation results synchronized those of the proposed model, which illustrated that the developed analytical model of this work is effective.

在车辆自组织网络（VANET）中，车对车（V2V）是一种重要的通信模式，其中，车辆通过无线收发器与其他行驶中的车辆通信。由于车辆的快速移动性，VANET上的网络连接通常不稳定，尤其是在稀疏的高速公路上。本文通过使用自适应草书控制机制和递归神经网络设计微观流动性和车道变更决策模型，分析了V2V连通性动力学。诸如SUMO和NS2之类的广泛仿真器分析了该提议模型的有效性。拟议的分析模型提供了一个框架，可用于检查与移动性相关的指标（如车速，加速/减速，安全距离，车辆到达率，车辆密度和网络度量数据传输速率，以表征所提议网络的VANET连接性。仿真结果同步了所提出模型的仿真结果，说明所开发的分析模型是有效的。