即時的模式化道路線及前車偵測

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：28

、訪客IP：18.221.27.56

姓名

劉旭仁(Hsu-Jen Liu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

即時的模式化道路線及前車偵測
(Real-time Model-based lane and vehicle detection)

相關論文

★ 適用於大面積及場景轉換的視訊錯誤隱藏法	★ 虛擬觸覺系統中的力回饋修正與展現
★ 多頻譜衛星影像融合與紅外線影像合成	★ 腹腔鏡膽囊切除手術模擬系統
★ 飛行模擬系統中的動態載入式多重解析度地形模塑	★ 以凌波為基礎的多重解析度地形模塑與貼圖
★ 多重解析度光流分析與深度計算	★ 體積守恆的變形模塑應用於腹腔鏡手術模擬
★ 互動式多重解析度模型編輯技術	★ 以小波轉換為基礎的多重解析度邊線追蹤技術(Wavelet-based multiresolution edge tracking for edge detection)
★ 基於二次式誤差及屬性準則的多重解析度模塑	★ 以整數小波轉換及灰色理論為基礎的漸進式影像壓縮
★ 建立在動態載入多重解析度地形模塑的戰術模擬	★ 以多階分割的空間關係做人臉偵測與特徵擷取
★ 以小波轉換為基礎的影像浮水印與壓縮	★ 外觀守恆及視點相關的多重解析度模塑

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

摘要
日益繁忙的社會，交通運輸事業越顯複雜，人們對於交通安全及便利的需求也越來越重視。智慧型運輸系統(ITS, Intelligent Transportation Systems)便是各國積極發展的專案。最近幾年，研究發展更是迅速；其中，與人的生命安全息息相關的安全駕駛，特別受到重視。本論文即是發展用於輔助安全駕駛上的即時模式化道路線及前車偵測。
本論文的主要研究在於如何快速並有效地偵測出道路影像中的道路線、前車所在的位置、及判斷車輛是否偏離車道。針對道路線偵測，我們利用人類視覺的特性，加強所搜尋的資訊，並提出了一個減少搜尋空間的方法，不但更正確也更快速的偵測出道路線，我們也提出多車道線偵測的方法。針對前車偵測，我們提出一個適應性的門檻值，來偵測前車位置。最後我們也利用透視幾何模型，求得目前車輛相對於車道中的橫向位置，判別目前是否偏離車道，以警示駕駛人偏離車道，避免發生危險。我們的方法能夠有效的克服天候變化及其他車輛對影像所造成的影響。
在實驗方面，我們在Win2000平台、P4 1.8GHz CPU、540MB RAM、影像解析度為320×240的環境下，測試6千多張影像，影像包含多種不同天氣及不同環境。在大部份情況下均能正確並即時的偵測出道路線、前車、及車輛有無偏離車道；執行速度高達每秒30張影像，平均一張影像只需花0.033秒，單張影像處理正確率超過98%。

摘要(英)

Abstract
People pay attention on the safe driving more and more. The research on intelligent transportation systems (ITS) is quickly developed in recent years. The safe driving is one of the important subjects in the ITS. In this thesis, we propose a real-time model-based method for lane and vehicle detection for safe driving system.
Our goal is to detect lane markings and front vehicle, and then provide lane departure warning based on the road images efficiently and effectively. In the lane detection, we exploit the property of human vision to enhance the difference map’s information such that the result of the lane detection is more effectively, and then propose a method for reduction of searching space in order to improve the detection efficiency. Moreover, we propose a multi-lane detection method. In the front vehicle detection, we exploit lane’s location as a searching region and define two adaptive threshold values to detect the front vehicle. Finally, we also exploit lane’s location and camera optical direction to estimate lateral offset of the vehicle with respect to the detected lane markers. Then the lane departure alarm is triggered by the decision of the estimation algorithm.
In experiments, six-thousand images were processed to evaluate the system performance. The images were captured in variant weather conditions and with various driving situations. The rate of lane detection is over 98% and the processing time is about 0.033 seconds on average.

關鍵字(中)

★ 道路偏離警示
★ 智慧型運輸系統
★ 安全駕駛
★ 模式化
★ 道路線偵測
★ 多車道偵測
★ 前車偵測
★ 適應性
★ 門檻值
★ 透視幾何模型
★ 橫向位置

關鍵字(英)

★ geometry perspective model
★ lane departure warning
★ threshold value
★ adaptive
★ vehicle detection
★ multiple lane detection
★ lane detection
★ lateral position
★ safe driving
★ intelligent transportation systems
★ model-based

論文目次

Contents
Abstract ii
Contents iii
List of Figures v
List of Tables xi
List of Tables xi
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 System overview 2
1.3 Thesis organization 3
Chapter 2 Related Work 5
2.1 Lane detection 5
2.2 Obstacle detection 11
2.3 Lane departure warning 15
Chapter 3 Lane Detection 19
3.1 Introduction to Mach band effect 20
3.2 Lane model 24
3.3 Lane detection in a single frame 26
3.4 Comparison of variant combined methods 29
3.4.1 Building difference map 29
3.4.2 Enhancing difference map by using Mach band effect 34
3.4.3 Tuning far/near effect in the difference map 35
3.5 The reduction of searching space 39
3.6 Lane detection in image sequences 41
3.7 Multiple lane detection in a single frame 45
3.8 Multiple lane detection in image sequences 46
Chapter 4 Vehicle detection 50
4.1 Building the horizontal difference map 50
4.2 Adaptive thresholding 51
4.3 Vehicle detection in a single image 55
4.4 Warning criterion 56
4.5 Vehicle detection in image sequences 58
Chapter 5 Lane Departure Warning 60
5.1 Vehicle lateral offset estimation 60
5.2 Warning criterion 63
5.3 Warning modality 64
Chapter 6 Experiments 66
6.1 Developing environment 66
6.2 Lane detection 67
6.3 Vehicle detection 84
6.4 Lane departure warning 88
Chapter 7 Conclusions and Future Work 93
7.1 Conclusions 93
7.2 Future work 94

參考文獻

References
[1] Aufrère, R., R. Chapuis, and F. Chausse, “A model-driven approach for real-time road recognition,” Machine Vision and Applications, vol.13, no.2, pp.95-107, 2001.
[2] Behringer, R. and N. Muller, “Autonomous road vehicle guidance from autobahnen to narrow curves,” IEEE Trans. on Robotics and Automation, vol.14, issue 5, pp.810-815, Oct.1998.
[3] Bertozzi, M. and A. Broggi, “Real-time lane and obstacle detection on the GOLD system,” in Proc. IEEE Intelligent Vehicles'96, Tokyo, Japan, Sep.18-20, 1996, pp.213-218.
[4] Bertozzi, M., A. Broggi, and S. Castelluccio, “A real-time oriented system for vehicle detection,” Journal of Systems Architecture, vol.43, pp.317-325, 1997.
[5] Bertozzi, M., A. Broggi, G. Conte, and A. Fascioli, “Obstacle and lane detection on ARGO on autonomous vehicle,” in Proc. IEEE Intelligent Transportation Systems Conference'97, Boston, Nov.10-13, 1997, pp.1010-1015.
[6] Bertozzi, M. and A. Broggi, “GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection,” IEEE Trans. on Image Processing, vol.7, no.1, pp.62-81, Jan. 1998.
[7] Bertozzi, M., A. Broggi, and A. Fascioli, “Stereo inverse perspective mapping: theory and applications,” Image and Vision Computing, vol.16, pp.585-590, 1998.
[8] Bertozzi, M., A. Broggi, G. Conte, and A. Fascioli, “Vision-based automated vehicle guidance: the experience of the ARGO vehicle,” in Tecniche di Intelligenza Artificiale e Pattern Recognition per la Visione Artificiale, Apr.6-7, 1998, pp.35-40.
[9] Bertozzi, M., A. Broggi, G. Conte, and A. Fascioli, “The experience of the ARGO autonomous vehicle,” in Proc. SPIE’98-Enhanced and Synthetic Vision, Orlando, FL, Apr.13-17, 1998.
[10] Bertozzi, M., A. Broggi, and A. Fascioli, “Experiments using MMX-based processors for real-time image processing on the ARGO vehicle,” in Proc. IEEE Intelligent Vehicles Sym., Stuttgart, Germany, Oct.28-30, 1998, pp.505-510.
[11] Bertozzi, M., A. Broggi, M. Cellario, A. Fascioli, P. Lombardi, and M. Porta, “Artificial vision in road vehicles,” Proceedings of the IEEE, Jul. 2002, pp.1258-1271.
[12] Bertozzi, M., A. Broggi, A. Fasciolo, and A. Tibaldi, “An evolutionary approach to lane markings detection in road environments,” in Atti del 6 convegno dell’Associazione Italiana per I’Intelligenza Artificiale, Siena, Italy, Sep.2002, pp.627-736.
[13] Betke, M., E. Haritaoglu, and L. S. Davis, “Real-time multiple vehicle detection and tracking from a moving vehicle,” Machine Vision and Applications, vol.12, no.2, pp.69-83, Sep. 2000.
[14] Bishop, R., “A survey of intelligent vehicle applications worldwide,” in Proc. IEEE Intelligent Vehicle Sym. 2000, Dearborn, MI, Oct.3-5, 2000, pp.25-30.
[15] Broggi, A., “Parallel and local feature extraction: a real-time approach to road boundary detection,” IEEE Trans. on Image Processing, vol.4, no.2, pp.217-223, 1995.
[16] Broggi, A., “Robust real-time lane and road detection in critical shadow conditions,” in Proc. IEEE Int’l Sym. on Computer Vision, Coral Gables, Florida, Nov.19-21, 1995, pp.353-358.
[17] Broggi, A. and S. Bertè, “Vision-based road detection in automotive systems: a real-time expectation-driven approach,” Journal of Artificial Intelligence Research, vol.3, pp.325-348, Dec.1995.
[18] Broggi, A., M. Bertozzi, A. Fascioli, C. G. L. Bianco, and A. Piazzi, “The ARGO autonomous vehicle’s vision and control systems,” Int. Journal of Intelligent Control and Systems, vol.3, no.4, pp.409-441, 1999.
[19] Broggi, A., M. Bertozzi, and A. Fascioli, “Architectural issues on vision-based automatic vehicle guidance: the experience of the ARGO project,” Real-Time Imaging Journal, vol.6, issue 4, pp.313-324, Aug. 2000.
[20] Bücher, T., C. Curio, J. Edelbrunner, C. Igel, D. Kastrup, I. Leefken, G. Lorenz, A. Steinhage, and W. Seelen, “Image processing and behavior planning for intelligent vehicles,” IEEE Trans. Industrial Electronics, vol.50, no.1, pp.62-75, Feb. 2003.
[21] Bücher, T., “Measurement of distance and height in images based on easy attainable calibration parameters,” in Proc. IEEE Intelligent Vehicles Symp., Oct. 2000, pp.314-319.
[22] Canny, J., “A computational approach to edge detection,” IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. PAMI-8, no.6, pp.679-698, Nov. 1986.
[23] Carlsson, S. and J. O. Eklundh, “Object detection using model-based prediction and motion parallax,” in Proc. Europ. Conf. Computer vision, Antibes, France, vol.427, Apr. 1990, pp.297-306.
[24] Chapuis, R., F. Marmoiton, R. Aufrère, “Road detection and vehicles tracking by vision for an on-board ACC system in the VELAC vehicle,” in Proc. IEEE 3rd Int. Conf. Information Fusion, Jul.10-13, 2000, pp.11-18.
[25] Chapuis, R., R. Aufrère, and F. Chausse, “Accurate road following and reconstruction by computer vision,” IEEE Trans. Intelligent Transportation systems, vol.3, no.4, pp.261-270, Dec. 2001.
[26] Chen, M., T. Jochem, and D. Pomerleau, “AURORA: a vision-based roadway departure warning system,” in Proc. IEEE Int. Conf. on Intelligent Robots and Systems 95, Pittsburgh, PA, Aug.5-9, 1995, pp.243-248.
[27] Choi, S. Y. and J. M. Lee, “Optimal moving windows for real-time road image processing,” in IEEE Int’l Conf. on Robotics and Automation, Seoul, Korea, May 21-26, 2001, pp.1220-1225.
[28] Dickmanns, E. D. and B. D. Mysliwetz, “Recursive 3-D road and relative ego-state recognition,” IEEE Trans. Pattern Anal. Machine Intel, vol.14, pp.199-213, Feb. 1992.
[29] Enkelmann, W., “Obstacle detection by evaluation of optical flow field from image sequences,” in Proc. Europ. Conf. Computer Vision, Antibes, France, vol.427, Apr. 1990, pp.134-138.
[30] Foda, S. G. and A. K. Dawoud, “Highway lane boundary determination for autonomous navigation,” in Proc. of IEEE Pacific Rim Conf. Communications, Computers and Signal Processing, Victoria, BC, Canada, Aug.26-28, 2001, pp.698-702.
[31] Gehrig, S. K., A. Gern, S. Heinrich, and B. Woltermann, “Lane recognition on poorly structured roads: the bots dot problem in California,” in Proc. IEEE Int’l Conf. on Intelligent Transportation Systems, Singapore, Sep.3-6, 2002, pp.67-71.
[32] Gern, A., U. Franke, and P. Levi, “Advanced lane recognition - fusing vision and radar,” in Proc. IEEE Intelligent Vehicles Sym. 2000, Dearborn, MI, Oct.3-5, 2000, pp.45-51.
[33] Giachetti, A., M. Campani, R. Sanni, and A. Succi, “The recovery of optical flow for intelligent cruise control,” in Proc. IEEE Intelligent Vehicles ’94, Paris, France, pp.91-95.
[34] Goldbeck, J. and B. Huertgen, “Lane detection and tracking by video sensors,” in Proc. IEEE Int’l Conf. on Intelligent Transportation Systems, Tokyo, Japan, Oct.5-8, 1999, pp.929-932.
[35] Gonzalez, R. C. and R. E. Woods, Digital Image Processing, 2nd ed., Prentice Hall, Upper Saddle River, NJ, 2002.
[36] Grimmer, D., S. Lakshmanan, “A deformable template approach to detecting straight edges in radar images,” IEEE Trans. Pattern Anal. Machine Intel, vol.18, issue 4, pp.438-443, Apr. 1996.
[37] Guilloux, Y. Le., J. Lonnoy, R. Moreira, M. P. Bruyas, and A. Chapon, “PAROTO Project: the benefit of Infrared Imagery for Obstacle Avoidance,” in Proc. IEEE Intelligent Vehicles Sym. 2002, Jun.17-21, 2002, pp.81-86.
[38] Handmann, U., T. Kalinke, C. Tzomakas, M.Werner, and W. von Seelen, “An image processing system for driver assistance,” in Proc. IEEE Conf. Intelligent Vehicles, 1998, pp.481–486.
[39] Hattori, H., “Stereo for 2D visual navigation,” in Proc. IEEE Intelligent Vehicles Sym. 2000, Dearborn, MI, Oct.3-5, 2000, pp.31-38.
[40] Ioannou, P. A., H. Jula, and E. B. Kosmatopoulos, “Collision avoidance analysis for lane changing and merging,” IEEE Trans. on Vehicular Technology, vol.49, issue 6, pp.2295-2308, Nov. 2000.
[41] Ito, T. and K. Yamada, “Preceding vehicle and road lanes recognition methods for RCAS using vision system,” in Proc. IEEE Intelligent Vehicles Sym. 1994, Oct.24-26, 1994, pp.85-90.
[42] Jeong, S. G., C. S. Kim, D. Y. Lee, S. K. Ha, D. H. Lee, M. H. Lee, and H. Hashimoto, “Real-time lane detection for autonomous vehicle,” in Proc. ISIE’01- Industrial Electronics 2001, Pusan, South Korea , Jun.12-16, 2001, pp.1466-1471.
[43] Jiang, G. Y., T. Y. Choi, S. K. Hong, J. W. Bae, and B. S. Song, “Lane and obstacle detection based on fast inverse perspective mapping algorithm,” in Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics 2000, Nashville, TN, Oct. 8-11, 2000, pp.2969-2974.
[44] Kaliyaperumal, K., K. Kluge, and S. Lakshmanan, “An algorithm for detecting roads and obstacles in radar images,” IEEE Trans. on Vehicular Technology, vol.50, issue 1, pp.170-182, Jan. 2001.
[45] Kaske, A., R. Husson, and D. Wolf, “Chi-square fitting of deformable templates for lane boundary detection,” IAR Annual Meeting ’95, Grenoble France, Nov. 1995.
[46] Kaske, A., D. Wolf, and R. Husson, “Lane boundary detection using statistical criteria,” in Int’l Conf. QCAV97- Quality by Artificial Vision, Le Creusot, France, 1997, pp.28-30.
[47] Kass, M., A. Witikin, and D. Terzopoulos, “SNAKES: Active Contour Models,” International Journal of Computer vision, vol.1, issue 4, pp.321-331, 1987.
[48] Kluge, K., “Extracting road curvature and orientation from image edge points without perceptual grouping into features,” in Proc. IEEE Intelligent Vehicles Sym. 1994, Oct.24-26, 1994, pp.109-114.
[49] Kluge, K., S. Lakshmanan, “A deformable-template approach to lane detection,” in Proc. IEEE Intelligent Vehicles Sym. 1995, Detroit, MI, Sep.25-26, 1995, pp.54-59.
[50] Koller, D., J. Malik, Q.-T. Luong, and J. Weber, “An integrated stereo-based approach to automatic vehicle guidance,” in Proc. Fifth ICCV, Boston, MA, 1995, pp.12-20.
[51] Kreucher, C. and S. Lakshmanan, “LANA: a lane extraction algorithm that uses frequency domain features,” IEEE Trans. on Robotics and Automation, vol.15, issue 2, pp.343-350, Apr. 1999.
[52] Kreucher, C. and S. Lakshmanan, “A frequency domain approach to lane detection in roadway images,” in Proc. Int’l Conf. on Image Processing, Kobe, Japan, Oct.25-28, 1999, pp.31-35.
[53] Kruger, W., W. Enkelmann, and S. Rossle, “Real-time estimation and tracking of optical flow vectors for obstacle detection,” in Proc. IEEE Intelligent Vehicles ’95, Detroit, MI, pp.304-309.
[54] LeBlanc, D.J., G.E. Johnson, P.J.T. Venhovens, G. Gerber, R. DeSonia, R.D. Ervin, C.F. Lin, A.G. Ulsoy, and T.E. Pilutti, “CAPC: an Implementation of a road -departure warning system,” in Proc. IEEE Int’l Conf. on Control Applications 1996, Dearborn, MI, Sep.15-18, 1996, pp.590-595.
[55] Lee, S., W. kwon, and J. W. Lee, “A vision based lane departure warning system,” in Proc. IEEE Int’l Conf. on Intelligent Robots and Systems 1999, Kyongju, South Korea, Oct.17-21, 1999, pp.160-165.
[56] McLauchlan, P. F. and J. Malik, “Vision for longitudinal vehicle control,” in Proc. Intelligent Transportation Systems Conf., Boston, MA, Nov.9-12, 1997, pp.918-923.
[57] Park, J. W., J. W. Lee, and K. Y. Jhang, “A lane-curve detection based on an LCF,” Journal of Pattern Recongnition Letters, vol.24, issue 14, pp.2301-2313, Oct. 2003.
[58] Pomerleau, D., “RALPH: rapidly adapting lateral position handler,” in IEEE Int’l Conf. on Intelligent Vehicles, Detroit, Michigan, Sep.1995, pp.506-511.
[59] Ross, B., “A practical stereo vision system,” in Proc. Int. conf. Computer Vision and Pattern Recognition, Seattle, WA, Jun.21-23, 1993, pp.148-153.
[60] Takezaki, J., N. Ueki, T. Minowa, and H. Kondoh, “Support system for safe driving,” Hitachi Review, vol.49, no.3, pp.107-114, 2000.
[61] Tan, T. N., G. D. Sullivan, and K. D. Baker, “Model-based localization and recognition of road vehicles,” Int. J. Comput. Vis., vol.27, no.1, pp.5-25, Mar. 1998.
[62] Terakubo, S., “Development of an AHS safe driving system,” SEI Technical Review, no.45, pp.71-77, 1998.
[63] Thomanek, F., E. D. Dickmanns, and D. Dickmanns, “Multiple object recognition and scene interpretation for autonomous road vehicle guidance,” in Proc. Intelligent Vehicles Symp., Paris, France, Oct.24-26, 1994, pp.231-236.
[64] Tseng, D. C., “Monocular Computer vision Aided Road Vehicle Driving for safety,” US Patent, 2004.
[65] Tsugawa, S., H. Mori, and S. Kato, “A lateral control algorithm for vision-based vehicles with a moving target in the field of view,” in IEEE Int. Conf. Intelligent Vehicles, vol. 1, Stuttgart, Germany, 1998, pp.41-45.
[66] Turk, M. A., D. G. Morgenthaler, K. D. Gremban, and M. Marra, “VITS - A vision system for autonomous land vehicle navigation,” IEEE Trans. on Vehicular Technology, vol.10, no.3, pp.342-361, May. 1988.
[67] Wang, Y., D. Shen, and E. K. Teoh, “Lane detection using Catmull-Rom spline,” in IEEE Int’l Conf. Intelligent Vehicles, Stuttgart, Germany, Oct.28-30, 1998, pp.51-57.
[68] Wang, Y., E. K. Teoh, and D. Shen, “Lane detection using B-snake,” in Proc. IEEE Information Intelligence and Systems Sym. 2000, Bethesda, MD, Oct.31-Nov.3, 1999, pp.438-443.
[69] Wang, Y., D. Shen, and E. K. Teoh, “Lane detection using spline model,” Journal of Pattern Recognition Letters, vol.21, issue 8, pp.677-689, Jul. 2000.
[70] Wang, Y., E. K. Teoh, and D. Shen, “Lane detection and tracking using B-snake,” Journal of Image and Vision Computer, vol.22, issue 4, pp.269-280, Apr.1, 2004.
[71] Yamada, K., T. Ito, and K. Nishioka, “Road lane recognition system for RCAS,” in Proc. IEEE Intelligent Vehicles Sym. 1996, Tokyo Japan, Sep.19-20, 1996, pp.177-182.
[72] Yamada, M., K. Ueda, I. Horiba, and N. Sugie, “Discrimination of the road condition toward understanding of vehicle driving environments,” IEEE Trans. on Intelligent Transportation Systems, vol.2, no.1, pp.26-31, 2001.
[73] Yim, Y. and S. Y. Oh, “Three-feature based automatic lane detection algorithm for autonomous driving,” in Proc. IEEE Int’l Conf. on Intelligent Transportation Systems, Tokyo, Japan, Oct.5-8, 1999, pp.929-932.
[74] Zielke, T., M. Brauckmann, and W. vonSeelen, “Intensity and edge-based symmetry detection with an application to car-following,” CVGIP: Image Understand., vol.58, Sep. 1993, pp.177-190.

指導教授

曾定章(Din-Chang Tseng)

審核日期

2004-7-5

推文