Trajectory prediction of a lane changing vehicle based on driver behavior estimation and classification

Abstract

Accurate trajectory prediction of a lane changing vehicle is a key issue for risk assessment and early danger warning in advanced driver assistance systems(ADAS). This paper proposes a trajectory prediction approach for a lane changing vehicle considering high-level driver status. A driving behavior estimation and classification model is developed based on Hidden Markov Models(HMMs). The lane change behavior is estimated by observing the vehicle state emissions in the beginning stage of a lane change procedure, and then classified by the classifier before the vehicle crosses the lane mark. Furthermore, the future trajectory of the lane changing vehicle is predicted in a statistical way combining the driver status estimated by the classifier. The classifier is trained and tested using naturalistic driving data, which shows satisfactory performance in classifying driver status. The trajectory prediction method generates different trajectories based on the classification results, which is important for the design of both autonomous driving controller and early danger warning systems.