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

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

9 Scopus citations

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
State	Published - 1 Jan 2014
Event	2014 IEEE World Forum on Internet of Things, WF-IoT 2014 - Seoul, Korea, Republic of Duration: 6 Mar 2014 → 8 Mar 2014

Conference

Conference	2014 IEEE World Forum on Internet of Things, WF-IoT 2014
Country/Territory	Korea, Republic of
City	Seoul
Period	6/03/14 → 8/03/14

Keywords

connected vehicle
driver assistance system
intelligent transportation system
internet-of-things

UN SDGs

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

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10.1109/WF-IoT.2014.6803165

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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

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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.",

keywords = "connected vehicle, driver assistance system, intelligent transportation system, internet-of-things",

author = "Kuan-Wen Chen and Tsai, {Hsin Mu} and Hsieh, {Chih Hung} and Lin, {Shou De} and Chieh-Chih Wang and Yang, {Shao Wen} and Chien, {Shao Yi} and Chia-Han Lee and Su, {Yu Chi} and Chou, {Chun Ting} and Yuh-Jye Lee 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",

month = jan,

day = "1",

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",

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Chen, K-W, Tsai, HM, Hsieh, CH, Lin, SD, Wang, C-C, Yang, SW, Chien, SY, Lee, C-H, Su, YC, Chou, CT, Lee, Y-J, 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, 6/03/14 - 8/03/14 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 - 2014/1/1

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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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KW - driver assistance system

KW - intelligent transportation system

KW - internet-of-things

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DO - 10.1109/WF-IoT.2014.6803165

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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

Abstract

Conference

Keywords

UN SDGs

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