基于车辆运行管理的多目标化V2V数据传递优化研究

Research on Vehicle Operation Management target V2V Data Optimization Based on Vehicle tracking

车辆自组织网络作为提高交通效率和提升交通安全的有效手段受到了广泛关注并被应用于道路设施监控、交通拥塞和交通事故预警、乘客间信息共享等方面。文章分析了车辆自组织网络独有的特征,提出了一种基于车辆轨迹的多目标优化车与车互联(vehicle-to-vehicle,V2V)通讯技术应用模型,利用车辆轨迹信息和道路交通统计的手段来挖掘多目标马尔可夫决策过程的参数,提供两种方法求解多目标马尔可夫决策过程,最终得到交叉路口处数据包传递的最优决策,利用此决策来进行数据的转发,同时还提出了错误恢复过程进行目的交付叉路口预测错误恢复。最后,通过采集3 996辆不同类型车辆的运行数据,验证了方案的有效性。

Vehicular ad-hoc network as an effective approach to enhance traffic efficiency and safety has attracted much attention.Vehicular ad-hoc network is wildly used in road infrastructure monitoring,traffic congestion and accident warning,passenger information sharing between applications.In this research issue,we analyzed the characteristics of vehicular ad-hoc network and the key issues and challenges in its data delivery issue.We analyzed the advantages and disadvantages of these solutions,and proposed a trajectory-based multi-objective optimal V2V data forwarding scheme called TMODF.We provided two methods for solving multi-objective MDP,the optimal routing policy is then developed by solving the multi-objective MDP.Then,the optimal routing policy was used to do data forwarding.We also proposed an error recovery process which was used to do destination intersection prediction error recovery.Finally,through collecting the data of 3 996 different types of vehicles,to verify the effectiveness of the proposed scheme.