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In a mixed-traffic scenario where both autonomous vehicles and human-drivingvehicles exist, a timely prediction of driving intentions of nearbyhuman-driving vehicles is essential for the safe and efficient driving of anautonomous vehicle. In this paper, a driving intention prediction method basedon Hidden Markov Model (HMM) is proposed for autonomous vehicles. HMMsrepresenting different driving intentions are trained and tested with fieldcollected data from a flyover. When training the models, either discrete orcontinuous characterization of the mobility features of vehicles is applied.Experimental results show that the HMMs trained with the continuouscharacterization of mobility features can give a higher prediction accuracywhen they are used for predicting driving intentions. Moreover, when thesurrounding traffic of the vehicle is taken into account, the performances ofthe proposed prediction method are further improved.

A Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Driving