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 conferencePaperpeer-review

5 Citations (Scopus)


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


Other2014 IEEE World Forum on Internet of Things, WF-IoT 2014
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications


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