A comparative study of time series modeling for driving behavior towards prediction
Ryunosuke Hamada, Takatomi Kubo, Kazushi Ikeda, Zujie Zhang, Takashi Bando, Masumi Egawa · 2013
Prediction of driving behaviors is an important problem in developing a next-generation driving support system. In order to take diverse driving situations into account, it is necessary to model multiple driving operation time series data. In this study we modeled multiple driving operation time series with four modeling methods including beta process autoregressive hidden Markov model (BP-AR-HMM), which we used in our previous study. We quantitatively compared the modeling methods with respect to prediction accuracies, and concluded that BP-AR-HMM excelled the other modeling methods in modeling multiple driving operation time series and predicting unknown driving operations. The result suggests that BP-AR-HMM estimated behaviors of a driver and transition probabilities between the behaviors more successfully than the other methods, because BP-AR-HMM can deal with commonalities and differences among multiple time series, but the others cannot. Therefore BP-AR-HMM may help us to predict driver behaviors in real environment and to develop the next-generation driving support system.