Journal of information and communication convergence engineering

  • 제11권2호
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  • Pages.95-102
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  • 2013
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  • 2234-8255(pISSN)
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  • 2234-8883(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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Stochastic Mixture Modeling of Driving Behavior During Car Following

  • Angkititrakul, Pongtep (Department of Media Science, Graduate School of Information Science, Nagoya University) ;
  • Miyajima, Chiyomi (Department of Media Science, Graduate School of Information Science, Nagoya University) ;
  • Takeda, Kazuya (Department of Media Science, Graduate School of Information Science, Nagoya University)
  • 투고 : 2012.12.28
  • 심사 : 2013.02.02
  • 발행 : 2013.06.30
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초록

This paper presents a stochastic driver behavior modeling framework which takes into account both individual and general driving characteristics as one aggregate model. Patterns of individual driving styles are modeled using a Dirichlet process mixture model, as a non-parametric Bayesian approach which automatically selects the optimal number of model components to fit sparse observations of each particular driver's behavior. In addition, general or background driving patterns are also captured with a Gaussian mixture model using a reasonably large amount of development data from several drivers. By combining both probability distributions, the aggregate driver-dependent model can better emphasize driving characteristics of each particular driver, while also backing off to exploit general driving behavior in cases of unseen/unmatched parameter spaces from individual training observations. The proposed driver behavior model was employed to anticipate pedal operation behavior during car-following maneuvers involving several drivers on the road. The experimental results showed advantages of the combined model over the model adaptation approach.

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참고문헌

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