本論文研發一套學習各種駕駛者其駕駛習性的系統，主要是因為目前尚未發展出依駕駛習性而量身訂做的駕駛安全輔助系統。現今市面上各車廠推出的駕駛安全輔助系統皆是使用同一標準來評估行車安全，因此本論文希望能透過駕駛習性圖的建立來分析駕駛的特徵，進而為駕駛打造出專屬的安全輔助系統，以符合駕駛的需求也同時維護行車的安全。
　　本系統所建構的駕駛習性圖是由多個行車關係圖組合而成，而行車關係圖包含許多節點和連結，每一個節點代表一個行車因素的大幅改變，每一個連結表示其前後節點的發生順序。從駕駛模擬儀所讀入的原始資料，擷取其行車因素的改變量可形成節點。節點經由階層式合併、權重計算和模糊化的步驟，讓連續的資料轉化為離散的資料表示，進而組成圖像化的行車關係圖。一個行車關係圖代表該駕駛的一個駕駛行為表現，而我們認為行為表現序列能夠表示駕駛的一個駕駛習性，多個駕駛行為表現可以整合成一個駕駛習性圖。而不同的駕駛習性圖可以表示不同類型的駕駛習性。
　　本系統使用交通部運輸研究所的駕駛模擬儀進行實驗。主要因為本研究規劃的實驗具有某種程度的危險性，因此使用駕駛模擬儀實驗可具數據的真實性，也可減低實車實驗對受試人員的風險。實驗結果足以驗證，不同行為表現會產生不同行車關係圖，而不同類型的駕駛也會有不同的駕駛習性圖。上述結論在未來可以給予安全輔助系統更多的客製化空間。

This thesis presents a system to learn the different kinds of driving habits. Recently, no driving assistance system has been considered the characters of the drivers. We develop a driving habit graph which can model the characters of the drivers and hope it can be embedded into the driving assistance system to increase the driving safety.
The driving habit graph is constructed by many driving relational maps which contains nodes and links. Each node represents a significant change of a driving factor, and the links between nodes indicate the ordering of the occurrence time of the nodes. The raw data obtained from the driving simulator are regarded as the driving factors, and a significant change of a driving factor will trigger node creation process which can transform the continuous raw data into the discrete data. A driving relational map constructed by the nodes and links represents a driving behavior. We believe that a sequence of driving behaviors, which can be regarded as a path of the driving habit graph, can model a driving habit. All the paths created from the same driver can integrate to form the driving habit graph of the driver. Different kinds of drivers can construct different types of driving habit graphs.
The experimental data are obtained from a driving simulator, since implementing the experimental scenario on the urban road is dangerous. Moreover, the subjects think that the scenario supplied by the driving simulator is realistic. The experimental results show that different kinds of driving behaviors will correspond to different driving relational maps, and different types of drivers will be represented by different driving habit graphs. In the future, we hope the driving habit graph can make the driving assistance systems more flexible according to the characters of drivers.

[Aud 10] Audi(奧迪)官方網站，取自http://www.audi.com.tw/tw/brand/zh.html，2010年。
[Lex 10] Lexus(凌志)官方網站，取自http://www.lexus.com.tw/，2010年。
[Vol 10] Volvo(富豪)官方網站，取自http://www.volvocars.com/tw/Pages/default.aspx，2010年。
[Bmw 10] BMW(寶馬)官方網站，取自http://www.bmw.com.tw/home/index.htm，2010年。
[Lux 10] Luxgen(納智捷)官方網站，取自http://www.luxgen-motor.com.tw/，2010年。
[Oli 00] N. Oliver and A. P. Pentland, “Graphical models for driver behavior recognition in a smart car,” IEEE Proceedings of Symposium on Intelligent Vehicles, Dearborn, Michigan, USA, pp. 7-12. 2000.
[Ger 09] B. D. Gerardo and J. Lee, “A framework for discovering relevant patterns using aggregation and intelligent data mining agents in telematics systems,” Journal of Telematics and Informatics, Vol. 26, no. 4, pp. 343-352, November 2009.
[Pau 10] J. Pauwelussen and P. J. Feenstra, “Driver behavior analysis during ACC activation and deactivation in a real traffic environment,” IEEE Transactions on Intelligent Transportation Systems, Vol. 11, no. 2, pp. 329-338, June 2010.
[Cha 09] C. Y. Chang and Y.R. Chou, “Development of fuzzy-based bus rear-end collision warning thresholds using a driving simulator,” IEEE Transactions on Intelligent Transportation Systems, Vol. 10, no. 2, pp. 360-365, June 2009.
[Lin 09] A. Lindgren, A. Angelelli, P.A. Mendoza, and F. Chen, “Driver behaviour when using an integrated advisory warning display for advanced driver assistance systems,”IET on Intelligent Transport Systems, Vol. 3 , no. 4, pp. 390-399, December 2009.
[Kau 08] C. Kaufmann, R. Risser, A. Geven, R. Sefelin, and M. Tscheligi , “LIVES (LenkerInnenInteraktion mit VErkehrstelematischen Systemen) — driver interaction with transport-telematic systems,” IET on Intelligent Transport Systems, Vol. 2, no.4, pp. 294-305, December 2008.
[Wan 10] J. Wang, S. Zhu, and Y. Gong, “Driving safety monitoring using semisupervised learning on time series data,” IEEE Transactions on Intelligent Transportation Systems, Vol. 11, no. 3, pp. 728-737, September 2010.
[Zha 10] Y. Zhang, W. C. Lin, and Y. K. S. Chin, “A pattern-recognition approach for driving skill characterization,” IEEE Transactions on Intelligent Transportation Systems, Vol. 11, no. 4, pp. 905-916, December 2010.
[Dag 03] I. Dagli, M. Broast, and G. Breuel, “Action recognition and prediction for driver assistance systems using dynamic belief networks,” Agent Technologies, Infrastructures, Tools, and Applications for E-Services, Springer, Heidelberg, Berlin, Germany, Vol. 2592, pp. 179-194. 2003.
[Fan 08] C.Y. Fang, P. Y. Wu, S. L. Chang, and S. W. Chen, “Danger prediction by case-based approach on expressways,” Proceedings of the 11th International IEEE Conference on Intelligent Transportation System, Beijing, China, pp. 1118-1123, October 2008.
[Fan 10] C.Y. Fang, P. Y. Wu, J. M. Wang, and S. W. Chen, “Dangerous driving event prediction on expressways using fuzzy attributed map matching,” International Conference on Machine Learning and Cybernetics (ICMLC), Qingdao, China, pp. 2718-2723, July 2010.
[Hsi 09] P. Y. Hsiao, C.W. Yeh, S. S. Huang, and L. C. Fu, “A portable vision-based real-time lane departure warning system: day and night,” IEEE Transactions on Vehicular Technology, Vol. 58, no. 4, pp. 2089-2094, May 2009.
[Kim 07] D. J. Kim, K. H. Park, and Z. Bien, “Hierarchical longitudinal controller for rear-end collision avoidance,” IEEE Transactions on Industrial Electronics, Vol. 54, no. 2, pp. 805-817, April 2007.
[Bra 96] M. Brand, “Coupled hidden Markov models for modeling interacting processes,” Perceptual Computing / Learning and Common Sense Technical report, MIT Media Lab, November 1996.
[李 05] 李建霖，“駕駛模擬器技術開發及其在駕駛行為研究之應用”，國立中央大學，機械工程研究所碩士論文，2005年7月。
[吳 05] 吳純如，“道路環境誘發性疲倦對駕駛者駕駛行為與認知工作績效的比較性研究”，國立雲林科技大學，工業工程與管理研究所碩士論文，2005年6月。
[別 10] 別宇文，“應用機車駕駛模擬器探討機車駕駛行為”，龍華科技大學，工程技術研究所碩士論文，2010年6月。
[林 08] 林永基，“車用衛星導航系統之視覺資訊內容接受度及績效研究”，大同大學，工業設計研究所碩士論文，2008年7月。
[交 08] 交通部運輸研究所，“應用駕駛模擬器開發智慧型運輸系統實驗平臺之軟硬體規劃設計(III)－智慧型運輸系統相關設施對駕駛人行為反應之影響評估程序之建立”，取自http://www.iot.gov.tw/ct.asp?xItem=212118&ctNode=1448&mp=1，2008年。
[交10-1] 交通部，“機動車輛登記數”，取自http://www.motc.gov.tw/mocwebGIP/wSite/lp?ctNode=550&xq_xCat=15，2010年。
[交 10-2] 交通部，“公路類重要參考指標”，取自http://www.motc.gov.tw/motchy page/reference/g004.xls，2010年。
[交 10-3] 交通部運輸研究所，“駕駛模擬儀虛擬場景標準整合介面開發以及設備維護與功能改善”，取自http://www.iot.gov.tw/ct.asp?xItem=525005&ctNode=1448&mp=1，2010年。