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.