Decision of Driver Intention of a Surrounding Vehicle Using Hidden Markov Model with Optimizing Parameter Estimation

Abstract

The decision of a driver intention of the surrounding vehicle is one of the essential parts of autonomous driving. Notably, the intention of cut-in is significant for longitudinal motion control of an ego vehicle on highways. However, there is no method directly to measure other vehicles' intentions. In the paper, using a hidden Markov model (HMM), we conducted a study on decision-making for the driver intention of the surrounding vehicle in a curved road. We also formulated a parameter estimation method for a transition probability to get a maximal expectation of the intention with the Baum-Welch algorithm. To validate the proposed scheme, we performed a computational simulation to determine the driver's intention. It turns out that the proposed system provides the driver intention earlier than a method with fixed transition probability.