A prediction method of missing vehicle position information based on least square support vector machine

Abstract

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 high-density 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 the complexity of detection time.