Location Prediction of Vehicles in VANETs Using A Kalman Filter

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 aroun...

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Bibliographic Details
Published inWireless personal communications Vol. 80; no. 2; pp. 543 - 559
Main Authors Feng, Huifang, Liu, Chunfeng, Shu, Yantai, Yang, Oliver W. W.
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.01.2015
Subjects
Ad hoc networks
Communications Engineering
Computer Communication Networks
Engineering
Kalman filters
Management
Mathematical models
Networks
Neural networks
Optimization
Roads
Signal,Image and Speech Processing
Vehicles
Location prediction
Vehicular ad-hoc network
Kalman filter
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Summary: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 network-based methods. In all traces, the proposed model exhibits superior prediction accuracy than the other prediction schemes.
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ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-014-2025-3