Driver intention estimation via discrete hidden Markov model

In this paper, driver intention estimation near a road intersection is presented, using discrete hidden Markov models (HMM) and the Hybrid State System (HSS) framework as basis. The development of Advanced Driver Assistance Systems (ADAS) has assisted drivers in many driving scenarios and resulted in safe driving. Developing techniques to estimate driver's intention leads to the advancement of ADAS. As a large number of accidents occur near road intersections, estimating the intention of a driver at an intersection is vital. The methods developed in this paper can be applied in ADAS to take appropriate measures in reducing accidents. The driver decisions are depicted as a Discrete State System (DSS) at a higher level and the continuous vehicle dynamics are depicted as a Continuous State System (CSS) at a lower level in the HSS framework. In the proposed technique, the vehicle's continuous observations including speed and yaw-rate, are used to estimate the driver's intention at each time step. In this work, the speed and yaw-rate are discretized in such a way that the important features about the driver's intention such as "go straight," "stop," "turn right," or "turn left" at the intersection, are abstractedly represented in the form of symbols. Naturalistic driving data, which is collected using a vehicle fitted with sensors, is used to train and test the developed model. The results from the proposed approach show high accuracy in estimating the driver's intention at a road intersection.