  In a mixed-traffic scenario where both autonomous vehicles and human-driving
vehicles exist, a timely prediction of driving intentions of nearby
human-driving vehicles is essential for the safe and efficient driving of an
autonomous vehicle. In this paper, a driving intention prediction method based
on Hidden Markov Model (HMM) is proposed for autonomous vehicles. HMMs
representing different driving intentions are trained and tested with field
collected data from a flyover. When training the models, either discrete or
continuous characterization of the mobility features of vehicles is applied.
Experimental results show that the HMMs trained with the continuous
characterization of mobility features can give a higher prediction accuracy
when they are used for predicting driving intentions. Moreover, when the
surrounding traffic of the vehicle is taken into account, the performances of
the proposed prediction method are further improved.
