NDLI: An improved driver-behavior model with combined individual and general driving characteristics

Content Provider	IEEE Xplore Digital Library
Author	Angkititrakul, P. Miyajima, C. Takeda, K.
Copyright Year	2012
Description	Author affiliation: Department of Media Science, Nagoya University, Nagoya, Aichi 464-8603, Japan (Angkititrakul, P.; Miyajima, C.; Takeda, K.)
Abstract	In this paper, we propose 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 Dirichlet process mixture model, a nonparametric 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 observed 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 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 adapted model previously proposed.
Starting Page	426
Ending Page	431
File Size	788675
Page Count	6
File Format	PDF
ISBN	9781467321198
ISSN	19310587
e-ISBN	9781467321181
DOI	10.1109/IVS.2012.6232177
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-03
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Data models Adaptation models Predictive models Training Trajectory Stochastic processes
Content Type	Text
Resource Type	Article

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