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研究生: 江進豐
Jinn-Feng Jiang
論文名稱: 擋風玻璃顯示行車障礙物智慧即時警示系統
Road Obstacles Intelligent On-Windshield Real-Time Warning System
指導教授: 林其禹
Chyi-Yeu Lin
口試委員: 林秋豐
none
黃文星
none
梁卓中
none
邱士軒
none
學位類別: 博士
Doctor
系所名稱: 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年: 2012
畢業學年度: 100
語文別: 中文
論文頁數: 53
中文關鍵詞: 駕駛輔助警示系統智慧車輛擋風玻璃
外文關鍵詞: driver-assistance, warning system, intelligent vehicle, windshield
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本文提出一新型汽車擋風玻璃警示系統,此系統能將前方具有潛在危險的障礙物的警示資訊直接顯示在車輛擋風玻璃上,駕駛者不需將視線從前方車道移走即可藉注視該警示點看到前方障礙物;相較於傳統裝置,需轉移駕駛者視線至LCD顯示器才能獲得資訊之作法,本文所提出之系統有較佳之安全性與效率。藉由攝影機與影像辨識軟體偵測到之車道前方障礙物之位置資訊,透過系統數學模型求得駕駛者與障礙物之視線方程式,並利用代數法求得視線方程式與擋風玻璃之交點解析式;再同步驅動雷射投射裝置將警告資訊投射於此位置,讓駕駛者得以直接藉由該雷射投影點看到前方車道障礙物,以達到有效警示目的。本文藉由電腦模擬程式驗證系統的數學方程式之正確性;並建立虛擬實境實驗平台與實車驗證平台證實擋風玻璃警示系統之可行性;最後並實際進行實車測試,實驗證實擋風玻璃警示系統之確實效用,有助於降低車禍發生率且增進行車安全。


The present study proposes a novel mark-on-windshield warning system for vehicles, by which the driver can see the potential hazardous object in front by looking at the direction through the mark on the windshield, without the need of moving his sight to the equipped liquid crystal display (LCD) panel for identifying the position of the obstacle as in traditional arts. The target to be warmed after identified by the camera and software system will be used to construct the line-of-sight equation based on the coordinate system on the moving vehicle. The explicit equation of the intersection point of the line-of-sight and the windshield surface is derived using the algebraic method. A warning mark is then projected at the intersection point on the windshield by a two-dimensional robotic laser projector, allowing the driver to easily identify the obstacle. A computer simulation program of the system is developed to validate the mathematical model of the system. A virtual-reality experimental platform and an actual vehicle experimental platform are constructed to demonstrate the feasibility of the proposed system. Finally, the experiments with the warning system installed on a car driving on the road proved the effectiveness in real time.

摘要 I 英文摘要 II 誌謝 III 目錄 IV 表目錄 VI 圖目錄 VII 第一章 前言 1 1-1 研究動機 1 1-2 文獻回顧 2 1-3 研究目的與方法 4 1-4 本文架構 6 第二章 擋風玻璃障礙物警示系統之數學模型 7 2-1 擋風玻璃警示交點數學模型 7 2-2 雷射投射機構之馬達旋轉角數學模型 12 2-3 路徑預測與系統時間補償 13 2-4 本章結論 15 第三章 擋風玻璃障礙物警示系統之模擬測試 16 3-1 視窗程式模擬測試 16 3-1-1 視窗程式介面 16 3-1-2 模擬測試結果 18 3-2 虛擬實境實驗測試 19 3-2-1 系統實驗流程與軟硬體裝置 19 3-2-2 虛擬實境實驗測試 32 3-3 本章結論 37 第四章 擋風玻璃障礙物警示系統之實車驗證 38 4-1 實車實驗設備 38 4-2 實驗結果與討論 42 4-3 本章結論 44 第五章 結論與建議 45 5-1 結論 45 5-2 建議 46 參考文獻 48 自述 53

01.Betke, M., Haritaoglu, E., and Davis, L. S., 1996, “Multiple Vehicle Detection and Tracking in Hard Real-Time,” Proceedings of the 1996 IEEE Intelligent Vehicles Symposium, pp. 351-356.
02.Bensrhair, A., and Bertozzi, M., 1998, “GOLD: A Parallel Real-Time Stereo Vision System for Generic Obstacle and Lane Detection,” IEEE Transaction on Image Processing, Vol. 7, No. 1, pp. 62-81.
03.Curio, C., Edelbrunner, J., Kalinke T., Tzomakas, C., and Seelen, W. V., 1999, “Walking Pedestrian Recognition,” IEEE Transactions on intelligent transportation system, Vol. 1, pp. 87-93.
04.Kwon, W., Lee, J. W., Shin D., Roh, K., Kim, D. Y., and Lee, S., 1999, “Experiments on Decision Making Strategies for a Lane Departure Warning System,” IEEE International Conference on Robotics and Automation, Vol. 4, pp. 2596-2601.
05.Stiller, C., Hipp, J., Rössig, C., and Ewald, A., 2000, “Multisensor Obstacle Detection and Tracking,” Journal of Image and Vision Computing, vol. 18, pp. 389-396.
06.Bertozzi, M., Broggi, A., and Fascioli, A., 2000, “Vision-Based Intelligent Vehicles: State of the Art and Perspectives,” Journal of Robotics and Autonomous Systems, Vol. 32, pp. 1-16.
07.Bensrhair, A., Bertozzi, M., Broggi, A., Miché, P., Mousset, S., and Toulminet, G., 2001, “A Cooperative Approach to Vision-Based Vehicle Detection,” Proceedings of the 2001 IEEE Intelligent Transportation System, pp. 207-212.
08.Cheng, B., Hashimoto, M., and Suetomi, T., 2001, “Analysis of Driver Response to Collision Warning During Car Following,” SAE Paper, No. 2001-08-0132.
09.Sun, Z., Miller, R., Bebis, G., and DiMeo, D., 2002, “A Real-Time Precrash Vehicle Detection System,” Proceedings of the 6th IEEE Workshop on Applications of computer Vision, pp. 171-176.
10.Miller, R. and Huang, Q., 2002, “An Adaptive Peer-to-Peer Collision Warning System,” IEEE 55th Vehicular Technology Conference, Vol. 1, pp. 317-321.
11.Hsieh, W. C., Fu, L. C., and Huang, S. S., 2003, “Vision Based Obstacle Warning System for On-Road Driving,” Proceedings of the 2003 IEEE International Conference on Intelligent Robots and Systems, pp. 3668-3673.
12.Kastrinaki, V., Zervakis, M., and Kalaitzakis, K., 2003, “A Survey of Video Processing Techniques for Traffic Applications,” Journal of Image and Vision Computing, pp. 359-381.
13.Kim, S. Y., and Oh, S. Y., 2003, “A Driver Adaptive Lane Departure Warning System Based on Image Processing and a Fuzzy Evolutionary Technique,” Proceedings of IEEE Intelligent Vehicles Symposium, pp. 361-365.
14.Huang, S. S., Chen, C. J., Hsiao, P. Y., and Fu, L. C., 2004, “On-Board Vision System for Lane Recognition and Front-Vehicle Detection to Enhance Driver’s Awareness,” Proceedings of the 2004 IEEE International Conference on Robotics & Automation, pp. 2456-2461.
15.Polychronopoulos, Scheunert, U., and Tango, F., 2004, “Gentralized Data Fusion for Obstacle and Road Borders Tracking in a Collision Warning System,” Proceedings of the ISIF 7th International Conference on Information Fusion, Stockholm, Sweden, pp. 760-767.
16.Li, X., Yao, X., Murphey, Y. L., Karlsen, R., and Gerhart, G., 2004, “A Real-Time Vehicle Detection and Tracking System in Outdoor Traffic Scenes,” Proceedings of the 17th International Conference on Pattern Recognition, Vol. 2, pp. 761-764.
17.Gat, I., Benady, M., and Shashua, A., 2005, “A Monocular Vision Advance Warning System for the Automotive Aftermarket,” SAE Transactions, Vol. 114, No. 7, pp. 403-410.
18.McCall, J. C. and Trivedi, M. M., 2006, “Video-Based Lane Estimation and Tracking for Driver Assistance: Survey, System, and Evaluation,” IEEE Transactions on Intelligent transportation systems, Vol. 7, No. 1, pp. 20-37.
19.Ho, A. W. L., Cummings, M. L., Wang, E., Tijerina, L., and Kochhar, D. S., 2006, “Integrating Intelligent Driver Warning Systems: Effects of Multiple Alarms and Distraction on Driver Performance,” TRB 2006 Annual Meeting, pp. 1-13.
20.Wu, B. F., Chen, C. J., Hsu, Y. P., and Chung, M. W., 2006, “A DSP-Based Lane Departure Warning System,” Proceedings of the 8th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering, pp. 240-245.
21.Ararat, Ö., Kural, E., and Güvenc, B. A., 2006, “Development of a Collision Warning System for Adaptive Cruise Control Vehicles Using a Comparison Analysis of Recent Algorithms,” Intelligent Vehicles Symposium, pp. 194-199.
22.Wei, Y., Meng, H., Zhang, H., and Wang, X., 2007, “Vehicle Frontal Collision Warning System Based on Improved Target Tracking and Threat Asseaament,” Proceedings of the 2007 IEEE Intelligent Transportation System, pp. 167-172.
23.Parsai, R. and Bajaj, P., “Intelligent Monitoring System for Driver’s Alertness (A Vision Based Approach),” KES’07/WIRN’07 Proceedings of the 11th International Conference, 2007.
24.Kim, S. Y., Kang, J. K., Oh, S. Y., Ryu, Y. W., Kim, K., Park, S. C., and Kim J., 2008, “An Intelligent and Integrated Driver Assistance System for Increased Safety and Convenience Based on All-Around Sensing,” Journal of intelligent Robot Systems, pp. 261-287.
25.Lin, C. C., Lin, C. W., Huang, D. C., and Chen, Y. H., 2008, “Design a Support Vector Machine-Based Intelligent System for Vehicle Driving Safety Warning,” Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China, pp. 938-943.
26.Jamson, A. H., Lai, F. C. H., and Carsten, O. M. J., 2008, “Potential Benefits of an Adaptive Forward Collision Warning System,” Transportation Research Part C, 16, pp. 471-484.
27.Gao, D., Li, W., Duan, J., and Zheng, B., 2009, “A Practical Method of Road Detection for Intelligent Vehicle,” Proceedings of the 2009 IEEE International Conference on Automation and Logistics, pp. 980-985.
28.Hsiao, P. Y., Yeh, C. W., Huang, S. S., and Fu, L. C., 2009, “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.
29.Lin, C. W., Wang, H. Y., and Tseng, D. C., 2009, “A Robust Lane Detection and Verification Method for Intelligent Vehicles,” Third International Symposium on Intelligent Information Technology Application, Vol. 1, pp. 521-524.
30.Matsukawa, N., 2010, “Image Displaying In-Vehicle System, Image Displaying Control In-Vehicle Apparatus and Computer Readable Medium Comprising Program For The Same,” U. S. Patent, 2010/0066516.
31.Matsumoto, M., Harada, T., and Nakayama, K., 2010, “Field Watch Apparatus,” U. S. Patent, 2010/0141414.
32.Lin, Y. R., and Li, Y. H., 2010, “FPGA Implementation of a Vision-Based Blind Spot Warning System,” World Academy of Science, Engineering and Technology, pp. 896-900.
33.Tseng, S. P., Liao, Y. S., Lin, C. W., Wang, Y. L., and Huang L. K., 2010, “A DSP-Based Lane Recognition Method for the Lane Departure Warning System of Smart Vehicles,” International Journal of Innovative Computing, Information and Control, Vol. 6, No. 7, pp. 2985-2995.
34.Tasi, L. W., 1999, Robot Analysis, John Wiley & Sons, New York.
35.Gellert, W., Gottwald, S., Hellwich, M., Kästner, H., and Künstner, H., 1989, Plane, In: VNR Concise Encyclopedia of Mathematics, pp. 539-543. New York.
36.Algebraic plane. http://mathworld.wolfram.com/Plane.html
37.Quest 3D software. http://www.quest3d.com/
38.Futaba S3102 servo motor. http://www.espritmodel.com/browseproducts/Futaba-S3102-Servo.HTML
39.SSC-32 Servo Controller. http://www.lynxmotion.com/p-395-ssc-32-servo-controller.aspx
40.Luxgen 7 MPV. http://www.luxgen-motor.com.tw/cars/7-MPV/
41.EITX-3002 embedded device: http://www.viatech.com.tw/

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