TY - GEN
T1 - Towards prediction of driving behavior via basic pattern discovery with BP-AR-HMM
AU - Hamada, Ryunosuke
AU - Kubo, Takatomi
AU - Ikeda, Kazushi
AU - Zhang, Zujie
AU - Shibata, Tomohiro
AU - Bando, Takashi
AU - Egawa, Masumi
PY - 2013/10/18
Y1 - 2013/10/18
N2 - Prediction of driving behaviors is important problem in developing the next-generation driving support system. In order to take account of diverse driving situations, it is necessary to deal with multiple time series data considering commonalities and differences among them. In this paper we utilize the beta process autoregressive hidden Markov model (BP-AR-HMM) that can model multiple time series considering common and different features among them using the beta process as a prior distribution. We apply the BP-AR-HMM to actual driving behavior data to estimate VAR process parameters that represent the driving behaviors, and with the estimated parameters we predict the driving behaviors of unknown test data. The results suggest that it is possible to identify the dynamical behaviors of driving operations using BP-AR-HMM, and to predict driving behaviors in actual environment.
AB - Prediction of driving behaviors is important problem in developing the next-generation driving support system. In order to take account of diverse driving situations, it is necessary to deal with multiple time series data considering commonalities and differences among them. In this paper we utilize the beta process autoregressive hidden Markov model (BP-AR-HMM) that can model multiple time series considering common and different features among them using the beta process as a prior distribution. We apply the BP-AR-HMM to actual driving behavior data to estimate VAR process parameters that represent the driving behaviors, and with the estimated parameters we predict the driving behaviors of unknown test data. The results suggest that it is possible to identify the dynamical behaviors of driving operations using BP-AR-HMM, and to predict driving behaviors in actual environment.
KW - Bayesian nonparametric approach
KW - beta process
KW - beta process autoregressive hidden Markov model
KW - driving behavior prediction
UR - http://www.scopus.com/inward/record.url?scp=84890530146&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890530146&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6638168
DO - 10.1109/ICASSP.2013.6638168
M3 - Conference contribution
AN - SCOPUS:84890530146
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2805
EP - 2809
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -