Applying Bayesian nonparametrics to non-homogeneous driving operation data towards prediction

Ryunosuke Hamada, Takatomi Kubo, Kazushi Ikeda, Zujie Zhang, Tomohiro Shibata, Takashi Bando, Masumi Egawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 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 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 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 dynamical behaviors of driving operations using BP-AR-HMM, and with BP-AR-HMM we can predict driving behaviors better in actual environment than with HMM.

Original languageEnglish
Title of host publication2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Print)9780992862602
Publication statusPublished - 2013 Jan 1
Event2013 21st European Signal Processing Conference, EUSIPCO 2013 - Marrakech, Morocco
Duration: 2013 Sep 92013 Sep 13

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Other

Other2013 21st European Signal Processing Conference, EUSIPCO 2013
CountryMorocco
CityMarrakech
Period13/9/913/9/13

Keywords

  • Bayesian nonparametric approach
  • beta process
  • beta process autoregressive hidden Markov model
  • driving behavior prediction

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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