车辆驾驶环形轨迹人机交互导航仿真模型研究  

Research on Human-Computer Interactive Navigation Simulation Model of Vehicle Driving Circular Trajectory

作　　者：崔俊杰 张卫正 CUi Jun-jie;ZHANG Wei-zheng(Department of Industrial Design,Zhengzhou University of Light Industry,Zhengzhou Henan 450002,China;College of Computer and Communication Engineering,Zhengzhou University of Light Industry,Zhengzhou Henan 450002,China)

机构地区：[1]郑州轻工业大学,河南郑州450002 [2]郑州轻工业大学计算机与通信工程学院,河南郑州450002

出　　处：《计算机仿真》2023年第9期88-91,114,共5页Computer Simulation

基　　金：2020年度河南省重点研发与推广专项(202102310653)。

摘　　要：当前的车辆导航方法忽略了对人机交互指令的运用,导致面对不同路况时该方法得到的路径缺乏合理性。为此构建车辆驾驶环形轨迹人机交互导航模型。根据置信椭圆得出满足条件的待匹配集合,计算D-S非空集合匹配框,确定车辆行驶方向。通过归一化处理mass函数决策值,利用地图匹配算法计算目标路线与其它路线差值,获得当前车辆和所在道路位置。以车辆行驶路径为依据,根据不同长度的路径节点构建路径树,得出起点至终点最短环形路径。计算车辆当前状态向量和误差矩阵的初始值,根据结果剔除导航模型中过程噪声和观测噪声,获取出增广状态向量,更新车辆状态信息完成最终导航。仿真结果表明,所构建模型可以根据不同路况,高效完成合理路线匹配。This paper designed a model of human-computer interactive navigation of vehicle driving circular trajectory.According to the confidence ellipse,we got the set to be matched satisfying the condition,and calculated the matching box of the D-S nonempty set,thus determining the driving direction of the vehicle.Moreover,we normalized the decision value of the mass function,and then used the map-matching algorithm to calculate the difference between the target route and other routes,so that we obtained the location of the current vehicle and the road.Based on the driving path,we constructed different path trees according to the path nodes with different lengths.And then,we got the shortest ring path.Furthermore,we calculated the initial values of the state vector and error matrix of the current vehicle.Based on the results,we eliminated the process noise and observation noise from the navigation model,thus obtaining the augmented state vector.After updating the state information of the vehicle,we completed the final navigation.Simulation results prove that the model can efficiently complete reasonable route matching according to different road conditions.

关 键 词：车辆驾驶 人机交互 导航模型 地图匹配算法 证据理论 

分 类 号：TP27[自动化与计算机技术—检测技术与自动化装置]