基于交通时空特征的车辆全局路径规划算法

Vehicle global path planning algorithm based on spatiotemporal characteristics of traffic

为降低混合动力汽车(HEV)的出行时间和出行能耗,提出了一种基于时空动态交通信息的路径规划算法。分析了影响车辆通行时间和全程最低能耗的因素。一种基于广义回归网络(GRNN)模型,拟合计算了道路通行时间以及整体路径的全程能耗。构建了基于并行A*算法的车辆路径规划算法,为确定起终点位置后的车辆,规划了一条耗时更短、更加节能的路径。进行了仿真对比试验。结果表明:相比于依据平均车速与道路功率的计算方法,该算法能够获得更优的出行路径,可降低车辆能耗11%以上,缩短行车时间13%以上。因而,该算法可为车辆规划更优的路径。

A path planning algorithm based on spatiotemporal dynamic traffic information was proposed to reduce the travel time and energy consumption of hybrid electric vehicles(HEV).The factors that affect the vehicle travel time and the minimum energy consumption in the whole path were analyzed.The travel time and the path energy consumption are calculated based on the generalized regression network(GRNN)model.A vehicle path planning algorithm based on parallel A*algorithm was constructed to plan a shorter timeconsuming or more energy-saving path for vehicles after determining the starting and ending positions.The virtual simulation test was implemented.The results show that the proposed algorithm can obtain a better travel path and reduce vehicle energy consumption by more than 11%or driving time by more than 13%compared with the calculation methods through average speed or power parameters.Therefore,the proposed algorithm can plan a better route for vehicles.