基于V2X环境下考虑多因素的信控交叉口车路协同优化

Vehicle Road Collaborative Optimization of Signal Control Intersection Considering Multiple Factors Based on V2X Environment

针对信号交叉口区域车辆间无法及时传递信息导致车辆通过交叉口区域存在一定的危险,通过V2X技术实现单一车辆动态引导和多车协同优化是缓解交通拥堵、提高通行效率的有效途径。本文主要围绕基于V2X环境下信号交叉口车路协同进行研究,通过概率神经网络筛选人车路对车辆安全行驶的影响因素,定义影响级别,由智能信号灯向云端传输数据,VANET访问云端获得相位数据,构建基于V2X环境下灯车路三态互联模型。通过比功率法定量估计车辆尾气排放,以尾气排放最低和交叉口延误最低为目标函数建立基于膜计算的多目标优化模型,并改善行人通过交叉口专用相位时间,提高信号交叉口的通行能力,降低信号交叉口的安全风险,提高驾驶人舒适性。

In the signalized intersection area,there is a certain danger of vehicles passing through the intersection area due to the failure of timely transmission of information between vehicles.The realization of dynamic guidance of single vehicle and collaborative optimization of multiple vehicles through V2X technology is an effective way to alleviate traffic congestion and improve traffic efficiency.This article mainly around the car road signal intersection coordinated V2X environment are studied,the main content of probabilistic neural network screening people car on the influence factors of vehicle driving safety,define the level of impact,by intelligent light to transmit data to the clouds,VANET phase data obtained access to the clouds,build based on V2X environment light car road three states interconnection model,Through quantitative estimate vehicle emissions than power method,with lowest emissions and intersection delay minimum as the objective function to establish a multi-objective optimization model based on membrane computing,and improve the special phase time through the intersection,to improve the traffic capacity of signal intersection,reduce signal intersection safety risk,improve the driving comfort.