摘要
车联网的不断发展加速了车辆通信的发展,在车联网的DSRC通信模式下,车辆的位置信息受到高密度广播和电磁辐射等因素的干扰,导致GPS采集的原始车辆信息数据丢失,本文提出了基于最小二乘支持向量机的信标数据补齐算法。与以往研究中利用车辆运行的历史趋势预测车辆位置的方法不同,该方法试图找出一个函数来建立车辆的丢失值与过去值的关系,采用非线性函数逼近,结合卡尔曼滤波来预测缺失的车辆位置。为了验证该算法的有效性,人为丢失部分真实的原始数据进行补齐验算。结果表明,补齐后的车辆位置数据与真实数据的平均相对误差为0.45%,最大绝对相对误差为8.25%。与PWHOG算法、差分矩阵、移动平均数据预处理等方法相比,该方法具有无需提取历史趋势数据、计算精度高的优点。适用于车联网环境下车辆位置的实时采集,可以减少检测时间并降低计算的复杂度。
The continuous development of VANET has accelerated the development of V2X communication.In the DSRC communication mode of VANET,the location information of the vehicles is interfered by factors such as highdensity broadcasting and electromagnetic radiation,which can lead to the loss of the original vehicle information data collected by GPS easily.To solve it,this paper proposed the Least Squared SVM based Beacon Data Complete Algorithm.Unlike previous studies that historical trends of vehicle operation were mainly used to predict vehicle location,this method attempts to find a function,which is used to establish the relationship between the lost value and the past value of the vehicle.On this basis,a nonlinear function approximation strategy is used to predict the position of the missing vehicle.Part of the original data was lost artificially to complete checking calculation and to verify the effectiveness of it.The results show that the average relative error between the complemented vehicle position data and the real data is 0.45%and the maximum absolute relative error is 8.25%.This method has the advantage of not needing to extract historical trend data and high calculation accuracy compared with the methods such as PWHOG algorithm,difference matrix,and moving average data preprocessing.It is suitable for real-time acquisition of vehicle position of VANET and can reduce detection time and computational complexity.
作者
于德新
郭海波
莫元富
常丽君
Yu Dexin;Guo Haibo;Mo Yuanfu;Chang Lijun(Jilin University of Architecture and Technology,Changchun 130000,China;School of Transportation Jilin University,Changchun 130022,China;Jilin Key Laboratory of Roud Trffic,Changchun 130022,China;Xia Men King Long Motor group Co.,Ltd.,Xiamen 361012,China)
出处
《市政技术》
2021年第4期17-22,共6页
Journal of Municipal Technology
关键词
智能交通
车联网
车辆位置缺失预测
最小二乘支持向量机
intelligent transportation
vehicle networking
vehicle position missing prediction
least squares support vector machine
