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  1. A500 情報学部/情報学研究科・情報文化学部・情報科学研究科
  2. A500a 雑誌掲載論文
  3. 学術雑誌

Generation of Pedal Operation Patterns of Individual Drivers in Car-Following for Personalized Cruise Control

http://hdl.handle.net/2237/9597
http://hdl.handle.net/2237/9597
6e398174-a410-426f-a751-d70cd136628d
名前 / ファイル ライセンス アクション
Miyajima_2.pdf Miyajima_2.pdf (562.5 kB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2008-03-18
タイトル
タイトル Generation of Pedal Operation Patterns of Individual Drivers in Car-Following for Personalized Cruise Control
言語 en
著者 Nishiwaki, Yoshihiro

× Nishiwaki, Yoshihiro

WEKO 22512

en Nishiwaki, Yoshihiro

Search repository
Miyajima, Chiyomi

× Miyajima, Chiyomi

WEKO 22513

en Miyajima, Chiyomi

Search repository
Kitaoka, Norihide

× Kitaoka, Norihide

WEKO 22514

en Kitaoka, Norihide

Search repository
Itou, Katsunobu

× Itou, Katsunobu

WEKO 22515

en Itou, Katsunobu

Search repository
Takeda, Kazuya

× Takeda, Kazuya

WEKO 22516

en Takeda, Kazuya

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利
言語 en
権利情報 Copyright © 2007 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.
抄録
内容記述 This paper presents a method to generate car-following patterns for individual drivers. We assume that driving is a recursive process. A driver recognizes a road environment such as velocity and following distance and adjusts gas and brake pedal positions. A vehicle status changes according to the driver's operation and the road environment changes according to the vehicle status. Driving patterns of each driver are modeled with a Gaussian mixture model (GMM), which is trained as a joint probability distribution of following distance, velocity, pedal position signals and their dynamics. Gas and brake pedal operation patterns are generated from the GMMs in a maximum likelihood criterion so that the conditional probability is maximized for a given environment i.e., following distance and velocity. Experimental results for a driving simulator show that car-following patterns generated from GMMs for three different drivers maintain their individual driving characteristics.
言語 en
内容記述タイプ Abstract
出版者
言語 ja
出版者 IEEE
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1109/IVS.2007.4290218
ISBN
関連タイプ isPartOf
識別子タイプ ISBN
関連識別子 1-4244-1068-1
ISSN
収録物識別子タイプ PISSN
収録物識別子 1931-0587
書誌情報 en : IEEE Intelligent Vehicles Symposium

p. 823-827, 発行日 2007
フォーマット
application/pdf
著者版フラグ
値 publisher
URI
識別子 http://hdl.handle.net/2237/9597
識別子タイプ HDL
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