Driver intention prediction using model-added Bayesian network

Abstract

The autonomous driving technology requires reliable detection and prediction of the surrounding environment. Predicting the lane change intention of the surrounding traffic is critical to evaluate the potential threat around the host vehicle. This paper develops a lane change maneuver prediction algorithm based on a newly proposed driver model combined with a Bayesian network. The innovation of the proposed algorithm is the utilization of the driver model while calibrating and executing the Bayesian network. The prediction algorithm can provide not only the driver’s intention but also the probability associated with the intention. The Next-Generation Simulation data sets are used to develop and validate the prediction model. In total, there are more than 2000 lane change events used in this paper. The result shows that the proposed prediction algorithm can provide an accurate prediction of the surrounding vehicle’s lane change maneuver.