Driving Behavior Prediction at Roundabouts Based on Integrated Simulation Platform

Due to growing interest in automated driving, the need for better understanding of human driving behavior in uncertain environment, such as driving behavior at un-signalized crossroad and roundabout, has further increased. Driving behavior at roundabout is greatly influenced by different dynamic factors such as speed, distance and circulating flow of the potentially conflicting vehicles, and drivers should choose whether to leave or wait at the upcoming exit according to these factors. In this paper, the influential dynamic factors and driving behavior characteristics at the roundabout is analyzed in detail, random forest method is then deployed to predict the driving behavior. For training the driving behavior model, four typical roundabout layouts were created under a real-time driving simulator with PanoSim-RT and dSPACE. Traffic participants with different motion style were also set in the simulation platform to mimic real driving conditions. Ten drivers were chosen for the data acquisition. Samples of these drivers were used in training the random forest classifier. The out-of-bag errors indicates that random forest model has good performance in predicting the roundabout behaviors of human drivers. Results show that the geometric parameters have little contribution for predicting the driving behavior and the relative velocity between surrounding vehicle and master vehicle are most contributive.