Smart Vehicle Navigation System Using Hidden Markov Model and RFID Technology

• Published in: Wireless personal communications Vol. 90; no. 4; pp. 1717 - 1742
• Main Authors: Malekian, Reza, Kavishe, A. F., Maharaj, B. T., Gupta, P. K., Singh, G., Waschefort, H.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2016; Springer Nature B.V
• Subjects: Algorithms; Communications Engineering; Computer Communication Networks; Engineering; Field programmable gate arrays; Global Positioning System; Global positioning systems; GPS; Habits; Intelligent vehicles; Markov chains; Mathematical models; Navigation; Navigation systems; Networks; Path predictors; Predictions; Radio frequency identification; Road design; Road transportation; Satellite tracking; Signal,Image and Speech Processing; Tracking (position); Transportation planning; Vehicles; Hidden Markov model; Prediction algorithms; RFID; Vehicle tracking; Navigation path
• ISSN: 0929-6212; 1572-834X
• DOI: 10.1007/s11277-016-3419-1

The road transport of dangerous goods has been the subject of research with increasing frequency in recent years. Global positioning system (GPS) based vehicle location devices are used to track vehicles in transit. However, this tracking technology suffers from inaccuracy and other limitations. In addition, real-time tracking of vehicles through areas shielded from GPS satellites is difficult. In this paper, the authors have addressed the implementation of a smart vehicle navigation system capable of using radio frequency identification based on information about navigation paths. For prediction of paths and accurate determination of navigation paths in advance, predictive algorithms have been used based on the hidden Markov model. At the core of the system there is an existing field programmable gate array board and hardware for collection of navigation data. A communication protocol and a database to store the driver’s habit data have been designed. From the experimental results obtained, an accurate navigation path prediction is consistently achieved by the system. In addition, once-off disturbances to the driver habits have been filtered out successfully.