We present a driving route prediction method that is based on HiddenMarkovModel (HMM). Thismethod can accurately predict
a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we
propose the route recommendation system architecture, where route predictions play important role in the system. Secondly,
we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to
make preparation for route predictions using a method of training set extension based on K-means++ and the add-one (Laplace)
smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of
the route predictions that is based on HMM are shown.