Generating lane-change trajectories of individual drivers

Abstract

This paper describes a method to generate vehicle trajectories of lane change paths for individual drivers. Although each driver has a consistent preferance in the lane change behavior, lane-changing time and vehicle trajectory are uncertain due to the presence of surrounding vehicles. To model this uncertainty, we propose a statistical driver model. We assume that a driver plans various vehicle trajectories depending on the surrounding vehicles and then selects a safe and comfortable trajectory. Lane change patterns of each driver are modeled with a hidden Markov model (HMM), which is trained using longitudinal vehicle velocity, lateral vehicle position, and their dynamic features. Vehicle trajectories are generated from the HMM in a maximum likelihood criterion at random lane-changing time and state duration. Experimental results show that vehicle trajectories generated from the HMM included a similar trajectory to that of a target driver.