近年來，駕駛安全輔助系統中的各類偵測子系統已有具體的開發成果，對於駕駛者的行車安全提供了不少保障。但是這些偵測子系統通常採取獨立作業的方式，除了它們的偵測結果可能互相衝突與矛盾外，子系統個別發出的頻繁的警告訊息對駕駛者更是容易造成困擾，延遲行車決策時間。若能將偵測子系統偵測到的結果加以分析，輸出經過整合後的結論供駕駛者參考，對駕駛者的行車安全會更有幫助。因此，本研究希望能建立危險行車事件分析子系統，目的在分析偵測子系統的偵測結果，並建立完善的整合機制，輸出較適當與穩定的警告訊息供駕駛者參考。
本研究在開發危險行車事件分析子系統之前，先建立了高速公路行車事件模擬子系統，主要是因為要完整的蒐集高速公路上的行車事件資料並不容易，而且在高速公路上模擬真實的危險行車事件對實驗者的生命安全也會造成莫大的威脅，所以我們開發了高速公路行車事件模擬子系統，並將模擬結果做為危險行車事件分析子系統的資料輸入。
危險行車事件分析子系統是採用cascaded fuzzy reasoning Petri net(CFRPN)模組來進行事件的分析、推理，首先將模擬子系統的模擬結果當作資料輸入，再經由CFRPN的多階段推導，整合出行車狀況的結論。若系統認為目前車輛處在危險的行車狀態，則輸出警告訊息提醒駕駛者注意。最後，我們以實驗來驗證危險行車事件分析子系統的適用性，並希望未來能將此系統嵌入駕駛安全輔助系統中，不僅幫助駕駛者更輕鬆地開車，同時也提升駕駛的安全性。

To help provide safety for drivers, driver assistance systems have been an area of active research in recent years. An important component in such systems are detection subsystems. Many have been developed based on various technologies. These detection subsystems always work independently of each other and, as a result, there are often conflicts regarding the detection of objects by these diverse systems. Also, these subsystems often make needless warnings to drivers, causing the drivers to be distracted from their primary task of driving. With this in mind, this paper proposes a system to integrate the results of detection subsystems and provide a better organized and filtered set of commands or suggestions to drivers.
Collecting realistic hazardous driving events on freeways is difficult and dangerous, so we set up a freeway driving simulation system provide usable data (both common and dangerous events encountered while driving) for use with our detection subsystem.
The dangerous driving event analysis subsystem analyzes and infers driving events using a CFRPN (cascaded fuzzy reasoning Petri net) module, and then determines if it is a danger. If so, then the driver is warned. The experimental results show that this proposed approach is feasible. If the dangerous driving event analysis subsystem is incorporated into a driver assistance system, a driver can drive more easily and safety will be improved.

[Bad03] E. Badouel, M. Llorens, and J. Oliver, “Modeling Concurrent Systems: Reconfigurable Nets,” Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, Nevada, USA, Vol. 4, pp. 1568-1574, 2003.

[Che03] Y. Chen, “Highway Overhead Structure Detection Using Video Image Sequences,” IEEE Transactions on Intelligent Transportation Systems, Vol. 4, pp. 67-77, 2003.

[Che02] S. M. Chen, “Weighted Fuzzy Reasoning Using Weighted Fuzzy Petri Nets,” IEEE Transactions on Knowledge and Data Engineering, Vol. 14, Issue 2, pp. 386-397, 2002.

[Che96] S. M. Chen, “A Fuzzy Reasoning Approach for Rule-Based Systems Based on Fuzzy Logics,” IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 26, Issue 5, pp. 769-778, 1996.

[Dua01] J. C. Duan, F. L. Chung, “Cascaded Fuzzy Neural Network Model Based on Syllogistic Fuzzy Reasoning,” IEEE Transactions on Fuzzy Systems, Vol. 9, pp. 293-306, 2001.

[Fan04] C. Y. Fang, C. S. Fuh, P. S. Yen, S. Cherng, and S. W. Chen, “An Automatic Road Sign Recognition System Based on a Computational Model of Human Recognition Processing,” Computer Vision and Image Understanding, Vol. 96, pp. 237-268, 2004.

[Fan03-1] C. Y. Fang, S. W. Chen, and C. S. Fuh, “Road Sign Detection and Tracking,” IEEE Transactions on Vehicular Technology, Vol. 52, pp. 1329-1341, 2003.

[Fan03-2] C. Y. Fang, S. W. Chen, and C. S. Fuh, “Automatic Change Detection of Driving Environments in a Vision-Based Driver Assistance System,” IEEE Transactions on Neural Networks, Vol. 14, pp. 646-657, 2003.

[Gao04] M. Gao, M. Zhou, and Y. Tang, “Intelligent Decision Making in Disassembly Process Based on Fuzzy Reasoning Petri Nets,” IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 34, Issue 5, pp. 2029-2034, 2004.

[Gao03] M. Gao, M. Zhou, “Fuzzy Reasoning Petri Nets,” IEEE Transactions on Systems, Man and Cybernetics, Part A, Vol. 33, pp. 314-324, 2003.
[Hat03] C. Hatipoglu, U. Ozguner, and K. A. Redmill, “Automated Lane Change Controller Design,” IEEE Transactions on Intelligent Transportation Systems, Vol. 4, pp. 13-22, 2003.

[He03] S. He, R. Song, and Q. Zhao, “Design of Uncertain Group Decision Support System and Its Application in Intelligent Transportation Management,” Proceedings of IEEE International Conference on Intelligent Transportation Systems, Nagoya, Japan, Vol. 2, pp. 1724-1729, 2003.

[Lis04] G. F. List, M. Cetin, “Modeling Traffic Signal Control Using Petri Nets,” IEEE Transactions on Intelligent Transportation Systems, Vol. 5, pp. 177-187, 2004.

[Llo04] M. Llorens, J. Oliver, “Structural and Dynamic Changes in Concurrent Systems: Reconfigurable Petri Nets,” IEEE Transactions on Computers, Vol. 53, pp.1147-1158, 2004.

[Mu03] X. Mu, L. Li, N. Tang, and X. Jiang, “Laser-based System for Highway Pavement Texture Measurement,” Proceedings of IEEE International Conference on Intelligent Transportation Systems, Nagoya, Japan, Vol. 2, pp. 1559-1562, 2003.

[Pap03] M. Papageorgiou, C. Diakaki, V. Dinopoulou, A. Kotsialos, and Y. Wang, “Review of Road Traffic Control Strategies,” Proceedings of the IEEE, Vol. 91, pp. 2043-2067, 2003.

[Pet81] J. L. Peterson, Petri Net Theory and the Modeling of Systems, Central Book Company, Taipei, Taiwan, 1981.

[Rui03] Y. Rui, W. Liu, X. Li, and Z. Zhang, “Research on Vehicle Intelligent Braking Control Based on Neural Networks,” Proceedings of IEEE International Conference on Intelligent Transportation Systems, Nagoya, Japan, Vol. 2, pp. 1384-1386, 2003.

[Sal98] G. Salgian, D. H. Ballard, “Visual Routines for Autonomous Driving,” Proceedings of IEEE 6th International Conference on Computer Vision, Bombay, India, pp. 876-882, 1998.

[Sha04] T. Shamir, “How Should an Autonomous Vehicle Overtake A Slower Moving Vehicle: Design and Analysis of an Optimal Trajectory,” IEEE Transactions on Automatic Control, Vol. 49, pp. 607-610, 2004.

[Sri04] D. Srinivasan, J. Xin, and R. L. Cheu, “Evaluation of Adaptive Neural Network Models for Freeway Incident Detection,” IEEE Transactions on Intelligent Transportation Systems, Vol. 5, pp. 1-11, 2004.

[Sug77] M. Sugeno, “Fuzzy Measures and Fuzzy Integrals: A Survey,” Fuzzy automatic and Decision Processes, Amsterdam, North Holland, pp. 89-102, 1977.

[Wan03] J. Wang, B. Cai, “A Low-cost Integrated GPS/INS Navigation System for the Land Vehicle,” Proceedings of IEEE International Conference on Intelligent Transportation Systems, Nagoya, Japan, Vol. 2, pp. 1022-1026, 2003.

[Wij04] W. S. Wijesoma, K. R. S. Kodagoda, and A. P. Balasuriya, “Road-Boundary Detection and Tracking Using Ladar Sensing,” IEEE Transactions on Robotics and Automation, Vol. 20, pp. 456-464, 2004.

[1] Toyota web site: http://www.toyota.co.jp/en/tech/index.html

[2] Honda web site: http://www.honda.co.jp/factbook/auto/safety/20000510/003.html

[3] http://www.mercedes-benz.com.tw/amw/emb/tw/0,,0-340-170702-86-171599-1-0-0-0-0-0-2645-155369-0-0-0-0-0-0-0,00.html

[4] http://www.audi.com.tw/experience/exp_tech_quattro.htm

[5] http://www.audi.com.tw/experience/exp_tech_ESP.htm