Location Prediction of Vehicles in VANETs Using A Kalman Filter

Abstract

Location information is very important for many applications of vehicular networks such as routing, network management, data dissemination protocols, road congestion, etc. If some reliable prediction is done on vehicle's next move, then resources can be allocated optimally as the vehicle moves around. This would increase the performance of VANETs. A Kalman filter is employed for predicting the vehicle's future location in this paper. We conducted experiments using both real vehicle mobility traces and model-driven traces. We quantitatively compare the prediction performance of a Kalman filter and neural networkbased methods. In all traces, the proposed model exhibits superior prediction accuracy than the other prediction schemes.

GPSR+Predict: An Enhancement for GPSR to Make Smart Routing Decision by Anticipating Movement of Vehicles in VANETs

“…In fact, there are different mathematical extrapolation methods with different levels of complexity allowing the estimation of future positions of vehicles [35][36][37]. For this study, we have opted to use a method based on previous positions, direction and speed.…”

Smart Vehicle Navigation System Using Hidden Markov Model and RFID Technology

“…These paths will affect the accuracy of future predicted navigation paths by increasing the time it takes for the matching algorithm to find an existing matching navigation path. Feng et al [11] proposed a new method using a Kalman filter? to predict the reliable location of vehicles' next move.…”

Prediction of link residual lifetime using Kalman filter in vehicular ad hoc networks

“…The initial values for P k/k−1 i.e when k = 0 is taken in such a way that the diagonal elements are very high value and non diagonal elements are fixed at zero. Thus initial value of P k/k−1 at k = 0 is given by [19] …”

“…The optimal estimate is derived by the Kalman filter based on minimizing the mean square error [16]. Due to the simplicity and robust nature of the Kalman filter and its different forms, they are extensively used for velocity and location prediction techniques in ad hoc networks [17][18] [19]. S. Ammoun et al in [17] performs a trajectory prediction and estimation using Kalman filter for anticipating the risks of collision of vehicles at the junctions.…”

“…Barrios et al in [18] proposes an improved collision avoidance system by predicting the positions of the vehicles with the help of global positioning system using extended Kalman filter. Feng et al [19] determines the location of vehicles using a Kalman filter and a comparative study is done between Kalman filter and artificial neural networks. In this paper, assuming the vehicles to follow Gauss-Markov mobility model, we present an algorithm for predicting the link residual lifetime in VANETs by using the location and relative velocity information obtained from the Kalman filter.…”