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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

Analysis of real-world driver’s frustration

http://hdl.handle.net/2237/14601
http://hdl.handle.net/2237/14601
b8cf3126-3517-4c65-88a1-66a75e48c8cf
名前 / ファイル ライセンス アクション
1054.pdf 1054.pdf (628.6 kB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2011-04-19
タイトル
タイトル Analysis of real-world driver’s frustration
言語 en
著者 Malta, Lucas

× Malta, Lucas

WEKO 39920

en Malta, Lucas

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Miyajima, Chiyomi

× Miyajima, Chiyomi

WEKO 39921

en Miyajima, Chiyomi

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Kitaoka, Norihide

× Kitaoka, Norihide

WEKO 39922

en Kitaoka, Norihide

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Takeda, Kazuya

× Takeda, Kazuya

WEKO 39923

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 © 2011 IEEE. Reprinted, with permission, from Lucas Malta; Chiyomi Miyajima; Norihide Kitaoka; Kazuya Takeda, Analysis of real-world driver’s frustration, Intelligent Transportation Systems, IEEE Transactions on, Mar/2011
抄録
内容記述 This study investigates a method for estimating a driver’s spontaneous frustration in the real world. In line with a specific definition of emotion, the proposed method integrates information about the environment, the driver’s emotional state, and the driver’s responses in a single model. Driving data are recorded using an instrumented vehicle on which multiple sensors are mounted. While driving, drivers also interact with an automatic speech recognition (ASR) system to retrieve and play music. Using a Bayesian network, we combine knowledge on the driving environment, assessed through data annotation, speech recognition errors, driver’s emotional state (frustration), and driver’s responses measured through facial expressions, physiological condition, and gas- and brake-pedal actuation. Experiments are performed with data from 20 drivers.We discuss the relevance of the proposed model and features of frustration estimation. When all of the available information is used, the overall estimation achieves a true positive rate of 80% and a false positive rate of 9% (i.e., the system correctly estimates 80% of the frustration and, when drivers are not frustrated, makes mistakes 9% of the time).
言語 en
内容記述タイプ Abstract
出版者
言語 en
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 AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1109/TITS.2010.2070839
ISSN
収録物識別子タイプ PISSN
収録物識別子 1524-9050
書誌情報 en : Intelligent Transportation Systems, IEEE Transactions on

巻 12, 号 1, p. 109-118, 発行日 2011-03
著者版フラグ
値 author
URI
識別子 http://dx.doi.org/10.1109/TITS.2010.2070839
識別子タイプ DOI
URI
識別子 http://hdl.handle.net/2237/14601
識別子タイプ HDL
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