Paper Abstract and Keywords |
Presentation |
2013-03-15 15:35
Towards driving behavior prediction: Applying nonparametric Bayesian approach to driving operation time series data Ryunosuke Hamada, Takatomi Kubo, Kazushi Ikeda, Zujie Zhang, Tomohiro Shibata (NAIST), Takashi Bando, Masumi Egawa (DENSO) CAS2012-142 SIP2012-173 CS2012-148 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Prediction of driving behaviors is important problem in developing a 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 study 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 operation data to estimate vector autoregressive process parameters that represent the segmental driving behaviors, and with the estimated parameters we investigate whether we can predict the driving behaviors of unknown test data. Prediction accuracy of test data using BP-AR-HMM is compared with that of using classical HMM. The results suggest that it is possible to identify the dynamics behaviors of driving operations using BP-AR-HMM, and with BP-AR-HMM we can predict driving behaviors precisely in actual environment than with HMM. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
time series analysis / driving operation prediction / Bayesian nonparametric approach / beta process autoregressive hidden Markov model / beta process / / / |
Reference Info. |
IEICE Tech. Rep., vol. 112, no. 485, SIP2012-173, pp. 265-270, March 2013. |
Paper # |
SIP2012-173 |
Date of Issue |
2013-03-07 (CAS, SIP, CS) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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CAS2012-142 SIP2012-173 CS2012-148 |
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