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

8 Scopus citations


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
StatePublished - 1 Jan 2014
Event2014 IEEE World Forum on Internet of Things, WF-IoT 2014 - Seoul, Korea, Republic of
Duration: 6 Mar 20148 Mar 2014


Conference2014 IEEE World Forum on Internet of Things, WF-IoT 2014
Country/TerritoryKorea, Republic of


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


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