IDEAS home Printed from https://ideas.repec.org/a/igg/jwnbt0/v3y2014i4p21-35.html
   My bibliography  Save this article

Multi-System Integration Scheme for Intelligence Transportation System Applications

Author

Listed:
  • Chih-Chiang Kuo

    (Institute for Information Industry, Taipei, Taiwan, R.O.C.)

  • Jyun-Naih Lin

    (Institute for Information Industry, Taipei, Taiwan, R.O.C.)

  • Syue-Hua Wu

    (Institute for Information Industry, Taipei, Taiwan, R.O.C.)

  • Cheng-Hsuan Cho

    (Institute for Information Industry, Taipei, Taiwan, R.O.C.)

  • Yi-Hong Chu

    (Institute for Information Industry, Taipei, Taiwan, R.O.C.)

  • Frank Chee Da Tsai

    (Institute for Information Industry, Taipei, Taiwan, R.O.C.)

Abstract

With more and more devices being introduced into vehicles to provide additional driving functionality, driving experience is seemingly getting more complex and frustrated due to an influx of individual messages. To help manage the information, individuals need a mechanism to orchestrate the operations of the in-vehicle devices so that such frustration can be eased. In this study, the authors propose an in-vehicle communication scheme for multi-system integration, where an effective system service discovery protocol is used. This proposed design also contains mutual communication methods and the data exchange methodologies for system integration. Experimental results with performance evaluation demonstrate the success of the proposed design.

Suggested Citation

  • Chih-Chiang Kuo & Jyun-Naih Lin & Syue-Hua Wu & Cheng-Hsuan Cho & Yi-Hong Chu & Frank Chee Da Tsai, 2014. "Multi-System Integration Scheme for Intelligence Transportation System Applications," International Journal of Wireless Networks and Broadband Technologies (IJWNBT), IGI Global, vol. 3(4), pages 21-35, October.
    • Handle: RePEc:igg:jwnbt0:v:3:y:2014:i:4:p:21-35
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWNBT.2014100102
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Yu & He, Yingying & Zhang, Likai, 2023. "Recognition method of abnormal driving behavior using the bidirectional gated recurrent unit and convolutional neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jwnbt0:v:3:y:2014:i:4:p:21-35. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.