Connected vehicle safety science, system, and framework

Kuan Wen Chen; Hsin Mu Tsai; Chih Hung Hsieh; Shou De Lin; Chieh Chih Wang; Shao Wen Yang; Shao Yi Chien; Chia Han Lee; Yu Chi Su; Chun Ting Chou; Yuh Jye Lee; Hsing Kuo Pao; Ruey Shan Guo; Chung Jen Chen; Ming Hsuan Yang; Bing Yu Chen; Yi Ping Hung

doi:10.1109/WF-IoT.2014.6803165

Connected vehicle safety science, system, and framework

Kuan Wen Chen, Hsin Mu Tsai, Chih Hung Hsieh, Shou De Lin, Chieh Chih Wang, Shao Wen Yang, Shao Yi Chien, Chia Han Lee, Yu Chi Su, Chun Ting Chou, Yuh Jye Lee, Hsing Kuo Pao, Ruey Shan Guo, Chung Jen Chen, Ming Hsuan Yang, Bing Yu Chen, Yi Ping Hung

College of Computing

Research output: Contribution to conference › Paper › peer-review

5 Citations (Scopus)

Abstract

In this paper, we propose a framework to develop an M2M-based (machine-to-machine) proactive driver assistance system. Unlike traditional approaches, we take the benefits of M2M in intelligent transportation system (ITS): 1) expansion of sensor coverage, 2) increase of time allowed to react, and 3) mediation of bidding for right of way, to help driver avoiding potential traffic accidents. To develop such a system, we divide it into three main parts: 1) driver behavior modeling and prediction, which collects grand driving data to learn and predict the future behaviors of drivers; 2) M2M-based neighbor map building, which includes sensing, communication, and fusion technologies to build a neighbor map, where neighbor map mentions the locations of all neighboring vehicles; 3) design of passive information visualization and proactive warning mechanism, which researches on how to provide user-needed information and warning signals to drivers without interfering their driving activities.

Original language	English
Pages	235-240
Number of pages	6
DOIs	https://doi.org/10.1109/WF-IoT.2014.6803165
Publication status	Published - 2014
Event	2014 IEEE World Forum on Internet of Things, WF-IoT 2014 - Seoul, Korea, Republic of Duration: 2014 Mar 6 → 2014 Mar 8

Other

Other	2014 IEEE World Forum on Internet of Things, WF-IoT 2014
Country/Territory	Korea, Republic of
City	Seoul
Period	14/3/6 → 14/3/8

All Science Journal Classification (ASJC) codes

Computer Networks and Communications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/WF-IoT.2014.6803165

Cite this

Chen, K. W., Tsai, H. M., Hsieh, C. H., Lin, S. D., Wang, C. C., Yang, S. W., Chien, S. Y., Lee, C. H., Su, Y. C., Chou, C. T., Lee, Y. J., Pao, H. K., Guo, R. S., Chen, C. J., Yang, M. H., Chen, B. Y., & Hung, Y. P. (2014). Connected vehicle safety science, system, and framework. 235-240. Paper presented at 2014 IEEE World Forum on Internet of Things, WF-IoT 2014, Seoul, Korea, Republic of. https://doi.org/10.1109/WF-IoT.2014.6803165

@conference{244a62370595467e9b98a967534f5f05,

title = "Connected vehicle safety science, system, and framework",

abstract = "In this paper, we propose a framework to develop an M2M-based (machine-to-machine) proactive driver assistance system. Unlike traditional approaches, we take the benefits of M2M in intelligent transportation system (ITS): 1) expansion of sensor coverage, 2) increase of time allowed to react, and 3) mediation of bidding for right of way, to help driver avoiding potential traffic accidents. To develop such a system, we divide it into three main parts: 1) driver behavior modeling and prediction, which collects grand driving data to learn and predict the future behaviors of drivers; 2) M2M-based neighbor map building, which includes sensing, communication, and fusion technologies to build a neighbor map, where neighbor map mentions the locations of all neighboring vehicles; 3) design of passive information visualization and proactive warning mechanism, which researches on how to provide user-needed information and warning signals to drivers without interfering their driving activities.",

author = "Chen, {Kuan Wen} and Tsai, {Hsin Mu} and Hsieh, {Chih Hung} and Lin, {Shou De} and Wang, {Chieh Chih} and Yang, {Shao Wen} and Chien, {Shao Yi} and Lee, {Chia Han} and Su, {Yu Chi} and Chou, {Chun Ting} and Lee, {Yuh Jye} and Pao, {Hsing Kuo} and Guo, {Ruey Shan} and Chen, {Chung Jen} and Yang, {Ming Hsuan} and Chen, {Bing Yu} and Hung, {Yi Ping}",

year = "2014",

doi = "10.1109/WF-IoT.2014.6803165",

language = "English",

pages = "235--240",

note = "2014 IEEE World Forum on Internet of Things, WF-IoT 2014 ; Conference date: 06-03-2014 Through 08-03-2014",

}

Chen, KW, Tsai, HM, Hsieh, CH, Lin, SD, Wang, CC, Yang, SW, Chien, SY, Lee, CH, Su, YC, Chou, CT, Lee, YJ, Pao, HK, Guo, RS, Chen, CJ, Yang, MH, Chen, BY & Hung, YP 2014, 'Connected vehicle safety science, system, and framework', Paper presented at 2014 IEEE World Forum on Internet of Things, WF-IoT 2014, Seoul, Korea, Republic of, 14/3/6 - 14/3/8 pp. 235-240. https://doi.org/10.1109/WF-IoT.2014.6803165

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AU - Chen, Kuan Wen

AU - Tsai, Hsin Mu

AU - Hsieh, Chih Hung

AU - Lin, Shou De

AU - Wang, Chieh Chih

AU - Yang, Shao Wen

AU - Chien, Shao Yi

AU - Lee, Chia Han

AU - Su, Yu Chi

AU - Chou, Chun Ting

AU - Lee, Yuh Jye

AU - Pao, Hsing Kuo

AU - Guo, Ruey Shan

AU - Chen, Chung Jen

AU - Yang, Ming Hsuan

AU - Chen, Bing Yu

AU - Hung, Yi Ping

PY - 2014

Y1 - 2014

N2 - In this paper, we propose a framework to develop an M2M-based (machine-to-machine) proactive driver assistance system. Unlike traditional approaches, we take the benefits of M2M in intelligent transportation system (ITS): 1) expansion of sensor coverage, 2) increase of time allowed to react, and 3) mediation of bidding for right of way, to help driver avoiding potential traffic accidents. To develop such a system, we divide it into three main parts: 1) driver behavior modeling and prediction, which collects grand driving data to learn and predict the future behaviors of drivers; 2) M2M-based neighbor map building, which includes sensing, communication, and fusion technologies to build a neighbor map, where neighbor map mentions the locations of all neighboring vehicles; 3) design of passive information visualization and proactive warning mechanism, which researches on how to provide user-needed information and warning signals to drivers without interfering their driving activities.

AB - In this paper, we propose a framework to develop an M2M-based (machine-to-machine) proactive driver assistance system. Unlike traditional approaches, we take the benefits of M2M in intelligent transportation system (ITS): 1) expansion of sensor coverage, 2) increase of time allowed to react, and 3) mediation of bidding for right of way, to help driver avoiding potential traffic accidents. To develop such a system, we divide it into three main parts: 1) driver behavior modeling and prediction, which collects grand driving data to learn and predict the future behaviors of drivers; 2) M2M-based neighbor map building, which includes sensing, communication, and fusion technologies to build a neighbor map, where neighbor map mentions the locations of all neighboring vehicles; 3) design of passive information visualization and proactive warning mechanism, which researches on how to provide user-needed information and warning signals to drivers without interfering their driving activities.

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DO - 10.1109/WF-IoT.2014.6803165

M3 - Paper

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T2 - 2014 IEEE World Forum on Internet of Things, WF-IoT 2014

Y2 - 6 March 2014 through 8 March 2014

ER -

Connected vehicle safety science, system, and framework

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All Science Journal Classification (ASJC) codes

UN SDGs

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