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  1. 論文誌(トランザクション)
  2. データベース(TOD)[電子情報通信学会データ工学研究専門委員会共同編集]
  3. Vol.9
  4. No.4

In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

https://ipsj.ixsq.nii.ac.jp/records/176477
https://ipsj.ixsq.nii.ac.jp/records/176477
a9a6d32b-7f5e-47b8-bcb2-1d41d0abd0c1
名前 / ファイル ライセンス アクション
IPSJ-TOD0904002.pdf IPSJ-TOD0904002.pdf (1.6 MB)
Copyright (c) 2016 by the Information Processing Society of Japan
オープンアクセス
Item type Trans(1)
公開日 2016-12-22
タイトル
タイトル In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance
タイトル
言語 en
タイトル In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance
言語
言語 eng
キーワード
主題Scheme Other
主題 [研究論文] data stream management system (DSMS), distributed stream processing, real-time scheduling, earliest deadline first (EDF), sensor fusion, automotive system, advanced driver assistance system (ADAS)
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
著者所属
Graduate School of Information Science, Nagoya University
著者所属
Institute of Innovation for Future Society, Nagoya University
著者所属
Center for Embedded Computing Systems, Nagoya University / Mobility Research Center, Doshisha University
著者所属
Center for Embedded Computing Systems, Nagoya University/Graduate School of Applied Informatics, University of Hyogo
著者所属
Graduate School of Information Science, Nagoya University
著者所属
Graduate School of Information Science, Nagoya University
著者所属
Center for Embedded Computing Systems, Nagoya University/Institute of Innovation for Future Society, Nagoya University
著者所属(英)
en
Graduate School of Information Science, Nagoya University
著者所属(英)
en
Institute of Innovation for Future Society, Nagoya University
著者所属(英)
en
Center for Embedded Computing Systems, Nagoya University / Mobility Research Center, Doshisha University
著者所属(英)
en
Center for Embedded Computing Systems, Nagoya University / Graduate School of Applied Informatics, University of Hyogo
著者所属(英)
en
Graduate School of Information Science, Nagoya University
著者所属(英)
en
Graduate School of Information Science, Nagoya University
著者所属(英)
en
Center for Embedded Computing Systems, Nagoya University / Institute of Innovation for Future Society, Nagoya University
著者名 Akihiro, Yamaguchi

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Akihiro, Yamaguchi

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Yousuke, Watanabe

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Yousuke, Watanabe

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Kenya, Sato

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Kenya, Sato

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Yukikazu, Nakamoto

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Yukikazu, Nakamoto

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Yoshiharu, Ishikawa

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Yoshiharu, Ishikawa

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Shinya, Honda

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Shinya, Honda

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Hiroaki, Takada

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Hiroaki, Takada

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著者名(英) Akihiro, Yamaguchi

× Akihiro, Yamaguchi

en Akihiro, Yamaguchi

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Yousuke, Watanabe

× Yousuke, Watanabe

en Yousuke, Watanabe

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Kenya, Sato

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Yukikazu, Nakamoto

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en Yukikazu, Nakamoto

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Yoshiharu, Ishikawa

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Shinya, Honda

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Hiroaki, Takada

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論文抄録
内容記述タイプ Other
内容記述 Data stream management systems (DSMSs) are suitable for managing and processing continuous data at high input rates with low latency. For advanced driver assistance including autonomous driving, embedded systems use a variety of onboard sensor data with communications from outside the vehicle. Thus, the software developed for such systems must be able to handle large volumes of data and complex processing. We develop a platform that integrates and manages data in an automotive embedded system using a DSMS. However, because automotive data processing, which is distributed in in-vehicle networks of the embedded system, is time-critical and must be reliable to reduce sensor noise, it is difficult to identify conventional DSMSs that meet these requirements. To address these new challenges, we develop an automotive embedded DSMS (AEDSMS). This AEDSMS precompiles high-level queries into executable query plans when designing automotive systems that demand time-criticality. Data stream processing is distributed in in-vehicle networks appropriately, where real-time scheduling and senor data fusion are also applied to meet deadlines and enhance the reliability of sensor data. The main contributions of this paper are as follows: (1) we establish a clear understanding of the challenges faced when introducing DSMSs into the automotive field; (2) we propose an AEDSMS to tackle these challenges; and (3) we evaluate the AEDSMS during run-time for advanced driver assistance.
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.25(2017) (online)
------------------------------
論文抄録(英)
内容記述タイプ Other
内容記述 Data stream management systems (DSMSs) are suitable for managing and processing continuous data at high input rates with low latency. For advanced driver assistance including autonomous driving, embedded systems use a variety of onboard sensor data with communications from outside the vehicle. Thus, the software developed for such systems must be able to handle large volumes of data and complex processing. We develop a platform that integrates and manages data in an automotive embedded system using a DSMS. However, because automotive data processing, which is distributed in in-vehicle networks of the embedded system, is time-critical and must be reliable to reduce sensor noise, it is difficult to identify conventional DSMSs that meet these requirements. To address these new challenges, we develop an automotive embedded DSMS (AEDSMS). This AEDSMS precompiles high-level queries into executable query plans when designing automotive systems that demand time-criticality. Data stream processing is distributed in in-vehicle networks appropriately, where real-time scheduling and senor data fusion are also applied to meet deadlines and enhance the reliability of sensor data. The main contributions of this paper are as follows: (1) we establish a clear understanding of the challenges faced when introducing DSMSs into the automotive field; (2) we propose an AEDSMS to tackle these challenges; and (3) we evaluate the AEDSMS during run-time for advanced driver assistance.
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.25(2017) (online)
------------------------------
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AA11464847
書誌情報 情報処理学会論文誌データベース(TOD)

巻 9, 号 4, 発行日 2016-12-22
ISSN
収録物識別子タイプ ISSN
収録物識別子 1882-7799
出版者
言語 ja
出版者 情報処理学会
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