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 conferencePaper

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 languageEnglish
Pages235-240
Number of pages6
DOIs
Publication statusPublished - 2014 Jan 1
Event2014 IEEE World Forum on Internet of Things, WF-IoT 2014 - Seoul, Korea, Republic of
Duration: 2014 Mar 62014 Mar 8

Other

Other2014 IEEE World Forum on Internet of Things, WF-IoT 2014
CountryKorea, Republic of
CitySeoul
Period14/3/614/3/8

Fingerprint

Systems science
Rights of way
Highway accidents
Fusion reactions
Visualization
Communication
Sensors

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Chen, K. W., Tsai, H. M., Hsieh, C. H., Lin, S. D., Wang, C. C., Yang, S. W., ... 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
Chen, Kuan Wen ; Tsai, Hsin Mu ; Hsieh, Chih Hung ; Lin, Shou De ; Wang, Chieh Chih ; Yang, Shao Wen ; Chien, Shao Yi ; Lee, Chia Han ; Su, Yu Chi ; Chou, Chun Ting ; Lee, Yuh Jye ; Pao, Hsing Kuo ; Guo, Ruey Shan ; Chen, Chung Jen ; Yang, Ming Hsuan ; Chen, Bing Yu ; Hung, Yi Ping. / 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 p.
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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}",
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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

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

2014. 235-240 Paper presented at 2014 IEEE World Forum on Internet of Things, WF-IoT 2014, Seoul, Korea, Republic of.

Research output: Contribution to conferencePaper

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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

Y1 - 2014/1/1

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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Chen KW, Tsai HM, Hsieh CH, Lin SD, Wang CC, Yang SW et al. Connected vehicle safety science, system, and framework. 2014. 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