請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/33489
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 丁肇隆 | |
dc.contributor.author | Mo-Kai Huang | en |
dc.contributor.author | 黃莫凱 | zh_TW |
dc.date.accessioned | 2021-06-13T04:43:23Z | - |
dc.date.available | 2006-07-25 | |
dc.date.copyright | 2006-07-25 | |
dc.date.issued | 2006 | |
dc.date.submitted | 2006-07-17 | |
dc.identifier.citation | 1. Mei Chen and Todd Jochem, 1995. “AURORA: A Vision-Based Roadway Departure Warning System,” in Intelligent Robots and Systems 95. 'Human Robot Interaction and Cooperative Robots', Proceedings, pp. 243-248.
2. Tom Pilutti and A. Galip Ulsoy, 1999. “Identification of Driver State for Lane-Keeping Tasks,” in IEEE Transactions on Systems, Man, and Cybernetics—PART A: Systems and Humans, pp. 486-502. 3. Joon Woong Lee, 2002. “A Machine Vision System for Lane Departure Detection,” in Computer Vision and Image Understanding, pp. 52-78. 4. A.M. L¨utzeler and E.D. Dickmanns, 1998. ”Road recognition with MarVEye”, in Proceedings of the IEEE Intelligent Vehicles Symposium 98, Stuttgart, Germany, pp. 341-346. 5. Bertozzi, M., and A. Broggi, 1998. “GOLD: A Parallel Real-Time Stereo Vision System gor Generic Obstacle and Lane Detection,” in IEEE Transactions on Image Processing, vol. 7, No. 1, pp. 62-81. 6. Bertozzi, M., A. Broggi, and A. Fascioli, 2000. “Vision-based intelligent vehicles: State of the art and perspectives,” in Robotics and Autonomous Systems, 32:1-16. 7. Jin Wang and Stefan Schroedl, 2005. “Lane Keeping Based on Location Technology,” in IEEE Transactions On Intelligent Transportation Systems, pp. 351-356. 8. Cumani, A., and A. Guiducci, 1995. “Geometric camera calibration: the virtual camera approach,” in Mach. Vision Appl. 8, pp. 375-384. 9. Faugeras, O. D., Q. T. Luong, and S. J. Maybank, 1992. “Camera self-calibration: Theory and experiments,” in Computer Vision ECCV 92, Santa Margherita, Italy, pp. 321-334. 10. D.A.Pomerleau, 1995. “RALPH:Rapidly Adapting Lateral Position Handler”, in Proceedings of the Intelligent Vehicle '95, pp. 506-511. 11. Chun-Che Wang, and Shih-Shinh Huang, 2005. “Driver Assistance System for Lane Detection and Vehicle Recognition with Night Vision,” in Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference, pp. 3530 - 3535 12. L. Andreone, and P. C. Antonello, 2002. “Vehicle Detection and Localization in Infra-Red Images,” in IEEE 5th International Conference on Intelligent Transportation Systems, pp. 141-146. 13. J. Canny, 1986. “A Computational Approach to Edge Detection,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, pp. 679-698. 14. Bing Ma, Sridhar Lakshmanan, and Alfred O, 2000. “Simultaneous Detection of Lane and Pavement Boundaries Using Model-Based Multisensor Fusion,” in IEEE Transactions ON Intelligent Transportation Systems, pp. 135-147. 15. M. Beauvais and S. Lakshmanan, 2000. “CLARK: A heterogeneous sensor fusion method for finding lanes and obstacles,” in Image Vis. Comput., vol. 18, no. 5, pp. 397-413. 16. E. Mizutani, T. Kozek, and L. O. Chua, 1998. “Road lane marker extraction by motion-detector CNNs,” in Proc. IEEE Int. Joint Conf. Neural Networks, pp. 503-508. 17. Young Uk Yim and Se-Young Oh, 2003. “Three-Feature Based Automatic Lane Detection Algorithm (TFALDA) for Autonomous Driving,” in IEEE Transactions On Intelligent Transportation Systems, pp. 219-225. 18. S. G. Jeong, C. S. Kim, K. S. Yoon, J. N. Lee, and J. I. Bae, 2001. “Real-time lane detection for autonomous vehicle,” in Proc. IEEE Intelligent Transportation Systems, pp. 508–513. 19. Weng, J., P. Cohen, and M. Herniou, 1992. “Camera calibration with distortion models and accuracy evaluation,” in IEEE Trams. Pattern Anal. Mach. Intelligence 14, pp. 965-980. 20. Wang, Y., D. Shen, and E. K. Teoh, 1998. “Lane detection using Catmull-Rom spline,” in IEEE International Conference on Intelligent Vehicle ’98, pp. 51-57. 21. Takahashi, A. and Ninomiya, Y, 1999. “A robust lane detection using real-time voting processor,” in Intelligent Transportation Systems, 1999. Proceedings. 1999 IEEE/IEEJ/JSAI International Conference on, pp. 577-580. 22. Pomerlau, D., and T. Jochem, 1996. “Rapidly Adapting Machine Vision for Automated Vehicle Steering,” IEEE Expert, 11(2). 23. Antonio Guiducci, 1998. “3D Road Reconstruction from a Single View,” in Computer Vision AND Image Understanding, pp. 212-226. 24. Antonio Guiducci, 1999. “Parametric Model of the Perspective Projection of a Road with Applications to Lane Keeping and 3D Road Reconstruction,” in Computer Vision AND Image Understanding, pp. 414-427. 25. Peng Chang and Camus, T., 2004. “Stereo-Based Vision System for Automotive Imminent Collision Detection,” in Intelligent Vehicles Symposium, 2004 IEEE 14-17 June, pp. 274-279. 26. Yue Wang and Eam Khwang Teoh, 2004. “Lane detection and tracking using B-Snake,” in Image AND Vision Computing, pp. 269-280. 27. Broggi, A., 1995b. “Robust Real-Time Lane and Road Detection in Critical Shadow Conditions,” in Proceedings IEEE International Symposium on Computer Vision, Coral Gables, Florida, pp. 19-21. IEEE Computer Society. 28. Alberto Broggi, 2000. “Visual Perception of Obstacles and Vehicles for Platooning,” in IEEE Transactions ON Intelligent Transportation Systems, vol. 1, pp. 164-176. 29. Erez Dagan, 2004. “Forward Collision Warning with a Single Camera,” in Intelligent Vehicles Symposium, 2004 IEEE 14-17 June, pp. 37-42. 30. C. Colombo, 1999. “Generalized Bounds for Time to Collision from First-Order Image Motion,” in Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, vol. 1, pp. 220-226. 31. Meyer, 1992. “Estimation of Time-to-Collision Maps from First Order Motion Models and Normal Flow,” in Pattern Recognition, 1992 . Vol.1. Conference A: Computer Vision and Applications, Proceedings., 11th IAPR International Conference on, pp. 78-82. 32. Eidehall, 2004. “Combined road prediction and target tracking in collision avoidance,” in Intelligent Vehicles Symposium, 2004 IEEE 14-17 June, pp. 619-624. 33. Tsogas, 2004. “Dynamic situation and threat assessment for collision warning systems: the EUCLIDE approach,” in IEEE Intelligent Vehicles Symposium, 2004 IEEE 14-17 June, pp. 636-641. 34. Song, 2004. “Design and Experimental Study of an Ultrasonic Sensor System for Lateral Collision Avoidance at Low Speeds,” in IEEE Intelligent Vehicles Symposium, 2004 IEEE 14-17 June, pp. 647-652. 35. Paul, 1996. “VEHICLE COLLISION WARNING AND AVOIDANCE SYSTEM USING REAL-TIME FFT,” in Vehicular Technology Conference, 1996. 'Mobile Technology for the Human Race'., IEEE 46th Volume 3, pp. 1820-1824. 36. Rudy, 2003. “Flat World Homography for Non-Flat World On-Road Obstacle Detection,” in IEEE Intelligent Vehicles Symposium, 2003 IEEE 9-11 June, pp. 310-315. 37. Axel, 2001. “Robust vehicle tracking fusing radar and vision,” in Multisensor Fusion and Integration for Intelligent Systems, 2001. MFI 2001. International Conference on 20-22 Aug, pp. 323-328. 38. Narayan, 2002. “Vision-based Vehicle Detection and Tracking Method for Forward Collision Warning in Automobiles,” in Intelligent Vehicle Symposium, 2002. IEEE Volume 2, 17-21 June, pp. 626-631. 39. SamYong, 2005. “Front and Rear Vehicle Detection and Tracking in the Day and Night Times Using Vision and Sonar Sensor Fusion,” in Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on 2-6 Aug, pp. 2173-2178. 40. Kate, 2004. “Mid range and distant vehicle detection with a mobile camera,” in IEEE Intelligent Vehicle Symposium, 2004 IEEE 14-17 June, pp. 72-77. 41. Yuji, 2004. “Real-time Lane Line and Forward Vehicle Detection by Smart Image Sensor,” in International Symposium on communications and information Technologies 2004 ( ISCIT 2004 ) Sapporo, Japan, October 26- 29, pp. 957-962. 42. Lee C. Yang, 2004. “Multiple Model Estimation for Improving Conflict Detection Algorithms,” in Systems, Man and Cybernetics, 2004 IEEE International Conference on, Volume 1, 10-13 Oct, pp. 242-249. 43. An-Ping Wang, 2004. “Intelligent CAN-based Automotive Collision Avoidance Warning System,” in Proceedings of the 2004 IEEE International Conference on Networking. Sensing & Control Taipei, Taiwan. March 21-23, pp. 146-151. 44. R. E. KALMAN, 1960. “A New Approach to Linear Filtering and Prediction Problems,” in Transactions of the ASME–Journal of Basic Engineering, 82, pp. 35-45. 45. Gene F. Franklin, J. David Powell, 1980. “Digital Control Of Dynamic Systems”. 46. Kyongsu Yi, 1999. “An Experimental Investigation of a CWKA System for Automobiles Using Hardware-in-the-Loop Simulations,” in Proceedings of the American Control Conference San Diego, California June, pp. 724-728. 47. Makoto Hirano, 1993. “Development of Vehicle-Following Distance Warning System for Trucks and Buses,” in IEEE - IEE Vehicle Navigation & Information System Conference, Ottawa – VNlS, pp. 513-516. 48. J. M. Collado, 2004. “Model Based Vehicle Detection for Intelligent Vehicles,” in IEEE Intelligent Vehicles Symposium University of Parma Parma, Italy June 14-17, pp. 572-577. 49. Andreas Eidehall, 2004. “Combined road prediction and target tracking in collision avoidance,” in IEEE Intelligent Vehicles Symposium University of Parma Parma, Italy June 14-17, pp. 619-624. 50. Nico Kaempchen, 2004. ” IMM Object Tracking for High Dynamic Driving Maneuvers,” in IEEE Intelligent Vehicles Symposium University of Parma Parma, Italy June 14-17, pp. 825-830. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/33489 | - |
dc.description.abstract | 車輛主動式安全系統是屬於政府重點發展計畫ITS(Intelligent Transport System智慧型運輸系統)中極為重要之一環,利用感測器協助駕駛者感官功能之不足,提高自動控制之程度,以彌補駕駛者因判斷錯誤或技術不足所造成的疏失,減少危險及意外事故之發生。本系統係以影像視覺為基礎,從影像中辨識出車道標線位置及正前方車輛位置等資訊以提供駕駛行車安全資訊。首先路面影像經由車內照後鏡下方之攝影機紀錄,將影像中欲處理的區域取出後,利用Sobel邊緣偵測,將物體邊緣分辨出來,再以車道線向量法找出最可能之車道線位置。結果顯示,本研究所提之方法,在大部分之路況下能準確地辨識出車道標線。其次偵測正前方車輛距離,由於白天和夜晚亮度不同,分別有不同的處理方法,白天以Sobel邊緣偵測,找尋車輛底部陰影位置;晚上則利用車輛尾燈特徵,以估計車輛之位置。最後將所得之資訊提供車道偏離警示及前車追撞警示系統,作為判斷車輛是否行使於車道之安全範圍內。 | zh_TW |
dc.description.abstract | Vehicle active safety system is very important and intelligent transport system is an important developing subject of our government. Different sensing systems have been developed to assist human driving and to avoid the occurrences of dangers or accidents. Our system is vision-based. The images are acquired by a video camera and processed to recognize the position of the lane markers and the relative distance to the front vehicle. A video camera was mounted on a vehicle to catch the sequence of the roadway. From each recorded image, a region of interest was decided. In the region of interest, the edges of objects were detected using Sobel edge detection method. Then a lane-vector method was used to find the most possible positions of the lane markers. Results showed that our method can recognize the positions of lane markers correctly at different road conditions. In front-vehicle detection two methods were used in daytime and at night. In the daytime, we detect the shadow of the front vehicle on the ground to calculate the relative distance; however, during the night the paired tail lights of cars were used to estimate the position of the front vehicle. Those information from the past image processing were used to provide lane-departure warning and collision-warning systems to judge if the car is driving safely. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T04:43:23Z (GMT). No. of bitstreams: 1 ntu-95-R93525016-1.pdf: 2665327 bytes, checksum: d9ec5b9d9297fba996b62b17f9fa754a (MD5) Previous issue date: 2006 | en |
dc.description.tableofcontents | 中文摘要 Ⅰ
英文摘要 Ⅱ 目錄 Ⅲ 表目錄 Ⅴ 圖目錄 Ⅵ 符號說明 Ⅸ 第一章 前言 1 1.1 研究動機與目的...........................................1 1.2 相關文獻探討………………………………………4 1.3 論文架構…………………………………………..10 第二章 系統架構 11 2.1 設備與裝置................................................11 2.2 基本假設…………………………………………..12 2.3 相機校正…………………………………………..12 2.4 影像處理流程…………………………………….14 2.4.1 影像擷取…………………………………….15 2.4.2 影像處理…………………………………….16 2.4.3 車道線及前車距離偵測…………………..17 2.4.4 逆透視轉換………………………………….18 2.4.5 警示發佈系統………………………………21 第三章 道路及前車偵測 23 3.1 邊界偵測…………………………………………23 3.1.1 車道標線之特徵……………………………23 3.1.2 邊界偵測介紹………………………………24 3.1.3 道路標線識別………………………………32 3.2 前方車輛的偵測………………………………….38 3.2.1 車輛在影像上之特徵……………………..38 3.2.2 前車偵測之方法介紹……………………..39 3.3 速度計算…………………………………………..55 3.3.1 側向速度計算之方法介紹………………..55 3.3.2 相對車速計算之方法介紹………………..57 3.4 警示系統…………………………………………..58 3.4.1 車道偏離警示系統…………………………58 3.4.2 前車追撞警示系統…………………………59 3.5 即時處理…………………………………………..60 第四章 實驗結果 61 4.1 相機校正…………………………………………..61 4.2 影像擷取及處理………………………………….62 4.3 車道標線偵測結果……………………………….66 4.4 車道偏離警示系統……………………………….70 4.5 前方車輛偵測結果……………………………….72 4.6 前方車輛追撞警示系統…………………………75 4.7 IPM逆透視轉換之驗證………………………….78 第五章 結論與未來目標 82 參考文獻 85 | |
dc.language.iso | zh-TW | |
dc.title | 全方位智慧型車輛--前車追撞及車道偏離警示系統 | zh_TW |
dc.title | Intelligent Vehicles-- A Study On A Vision-based Roadway Departure Warning And Collision Warning System | en |
dc.type | Thesis | |
dc.date.schoolyear | 94-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林銘崇,陳柏全,蔡進發 | |
dc.subject.keyword | 車道線偵測,前方車輛偵測, | zh_TW |
dc.subject.keyword | lane detection,vehicle detection, | en |
dc.relation.page | 89 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2006-07-18 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-95-1.pdf 目前未授權公開取用 | 2.6 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。