輔助變換車道的盲點範圍視覺偵測

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：21

、訪客IP：3.22.27.22

姓名

林彥翔(Yen-Hsiang Lin) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

輔助變換車道的盲點範圍視覺偵測
(Visual Blind-spot Detection for Lane Change Assistance)

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)

近年來為了減少交通事故而發展的車輛輔助安全駕駛議題越來越受重視。對駕駛人而言，在車的兩側都有一些無法觀察到的盲點視線範圍。我們可以在左右兩側後照鏡的下方架設相機拍攝側後方的影像，利用電腦視覺方法偵測是否有可能造成威脅的來車，以輔助駕駛人變換車道。
我們的盲點範圍視覺偵測系統包含：近側車道線偵測、虛實線判斷、遠側車道線位置估計、側方車輛偵測、側方車輛距離估計、側方車輛追蹤及車輛相對運動關係分析等模組。
以定義好的車道線模式，尋找最符合該模式的直線作為近側車道線。由已知的道路寬度和相機參數以反投影法估計出遠側車道線的位置，並藉由近側及遠側車道線所劃分出的範圍，作為偵測車輛的搜尋範圍。我們以車底陰影和車輛左右垂直邊作為偵測車體區塊的特徵，並藉由一些條件來驗證偵測區塊。我們會追蹤各個車體區塊以獲得同一台車在連續影像中的相對位置，並記錄該車輛的運動向量。如果車輛是朝己車逐漸逼近，則系統會對駕駛人提出警示，以避免切換車道造成碰撞。
我們以各種不同天候狀況和不同道路的影像測試偵測效能。由實驗結果顯示，在良好天候狀況下的側邊車輛偵測率約為92%，但在惡劣天候狀況下的偵測率並不理想。因此，研究新的偵測方法或是融合其他的感測器來幫助惡劣天候下的偵測是未來主要的研究目標。

摘要(英)

Developing a real-time automotive driver assistant system for safety has emerged wide attention in recent years. When driving on the road, the fields of view beside the host vehicle for drivers are limited. Therefore, we utilize cameras mounted under side-view mirrors of a vehicle to monitor the circumstance in the blind-spot areas for drivers to avoid possible collision when changing lane.
The proposed visual blind-spot detection system includes near lane mark detection, classification of solid/dashed lane mark, far lane mark estimation, side vehicle detection, distance estimation of side vehicles, vehicle tracking, and object-based motion analysis.
In the proposed system, the lane mark at the near side of the host vehicle is detected by searching the optimal parameters of a defined lane model on the images, and the lane mark at the far side is estimated from the relative position of near lane mark by inverse perspective transform. Thus the proposed system is able to extract the region of the adjacent lane and detect the approaching vehicles. Side vehicles are detected by underneath shadow and left/right borders, and verified by the ratio of vehicle width and road width, symmetry, and gray-level variance of the vehicle region. We track the detected vehicles in consecutive images to acquire their relative positions between frames and compute their motion vectors. We analyze the motion vectors to judge if the vehicle is approaching the host vehicle, and the system will warn the driver if there is a vehicle approaching during the driver changes lane.
In the experiments, we evaluate the proposed system in different weather conditions, such as cloudy day, sunny day, dusky day, and rainy day, and in different driving environments, such as highway, expressway, and urban roads. The average detection rate of vehicles in sunny day and cloudy day is about 92%, while the detection rate in rainy day is about 75%. The performance of the vehicle detection is not robust enough in bad weather condition, so finding other vehicle detecting method or fusing different sensor data is our future work.

關鍵字(中)

★ 運動分析
★ 盲點偵測
★ 先進安全車輛
★ 車輛偵測

關鍵字(英)

★ advanced safety vehicle
★ vehicle detection
★ blind-spot detection
★ motion analysis

論文目次

Abstract ii
Contents iv
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 System overview 2
1.3 Thesis organization 3
Chapter 2 Related Works 5
Chapter 3 Lane Detection 21
3.1 Near lane detection 22
3.1.1 Prerequisites for lane mark detection 22
3.1.2 Accumulating edge pixels 23
3.1.3 Improvements in efficiency and correctness 25
3.2 Near lane verification 26
3.3 Solid/dashed lane classification 28
3.4 Far lane estimation 30
Chapter 4 Vehicle Detection 34
4.1 Vehicle hypothesis detection 34
4.1.1 Shadow 35
4.1.2 Vertical borders 37
4.2 Vehicle hypothesis verification 38
4.3 Distance estimation 39
4.4 Vehicle tracking and relative motion analysis 40
Chapter 5 Experiments 45
5.1 Experimental environments 45
5.2 Experimental results 47
5.3 Discussion 51
Chapter 6 Conclusions and Future Works 54
6.1 Conclusions 54
6.2 Future works 55
References 56

參考文獻

[1] Achler, O. and M. M. Trivedi, "Vehicle wheel detector using 2D filter banks," in Proc. IEEE Intelligent Vehicles Symp., Parma, Italy, Jun.14-17, 2004, pp.25-30.
[2] Alonso, J. D., Multimodal Bio-inspired Vision System, Ph.D Dissertation, University of Granada, Spain, 2006.
[3] Alonso, J. D., E. R. Vidal, A. Rotter, and M. Muhlenberg, "Lane-change decision aid system based on motion-driven vehicle tracking," IEEE Trans. Vehicular Technology, vol.57, no.5, pp.2736-2746, Sep. 2008.
[4] Batavia, P. H., D. A. Pomerleau, and C. E. Thorpe, "Overtaking vehicle detection using implicit optical flow," in Proc. IEEE Conf. on Intelligent Transportation System, Pittsburgh, PA, Nov.9-12, 1997, pp.729-734.
[5] Becker, L. P., A. Debski, D. Degenhardt, M. Hillenkamp, and I. Hoffmann, "Development of a camera-based blind spot information system," in Advanced Microsystems for Automotive Applications, J. Valldorf and W. Gessner, eds., Springer-Verlag, Berlin, 2005, Ch.6, pp.71-84.
[6] Blanc, N., B. Steux, and T. Hinz, "LaRASideCam - a fast and robust vision-based blindspot detection system," in Proc. IEEE Intelligent Vehicles Symp., Istanbul, Turkey, Jun.13-15, 2007, pp.480-485.
[7] Chen, K.-W., Monocular Computer Vision Technigues for Road and Situation Detection, Master thesis, Computer Science and Information Engineering Dept., National Central Univ., Chung-li, Taoyuan, Taiwan, 2005.
[8] Chung, E. Y., H. C. Jung, E. Chang, and I. S. Lee, "Vision based for lane change decision aid system," in Proc. of The 1st Int. Forum on Strategic Technology, Ulsan, Korea, Oct.18-20, 2006, pp.10-13.
[9] Díaz, J., E. Ros, S. Mota, G. Botella, A. Canas, and S. Sabatini, "Optical flow for cars overtaking monitor: the rear mirror blind spot problem," in Proc. of 10th Int. Conference on Vision in Vehicles, Granada, Spain, 2003.
[10] Gandhi, T. and M. M. Trivedi, "Vehicle surround capture: Survey of techniques and a novel omni video based approach for dynamic panoramic surround maps," IEEE Trans. Intelligent Transportation Systems, vol.7, no.3, pp.293-308, Sep. 2006.
[11] Huang, Y.-C., A Vision-based Vehicle to Vehicle Detection and Tracking System, Master thesis, Computer Science and Information Engineering Dept., National Central Univ., Chung-li, Taoyuan, Taiwan, 2005.
[12] Krips, M., J. Valten, and A. Kummert, "AdTM tracking for blind spot collision avoidance," in Proc. IEEE Intelligent Vehicles Symp., Parma, Italy, Jun.14-17, 2004, pp.544-548.
[13] Lin, C.-J., Forward and Blind-spot Visual Detection for Advanced Safety Vehicles, Master thesis, Computer Science and Information Engineering Dept., National Central Univ., Chung-li, Taoyuan, Taiwan, June 2008.
[14] Mota, S., E. Ros, J. Diaz, G. Botella, F. Vargas-Martin, and A. Prieto, "Motion driven segmentation scheme for car overtaking sequences," in Proc. of 10th Int. Conf. on Vision in Vehicles, Granada, Spain, Sept.7-10, 2003.
[15] Mota, S., E. Ros, E. M. Ortigosa, and F. J. Pelayo, "Bio-inspired motion detection for blind spot overtaking monitor," Int. Journal of Robotics and Automation, vol.19, no.4, pp.190-196, 2004.
[16] Mota, S., E. Ros, J. Díaz, E. M. Ortigosa, and A. Prieto, "Motion-driven segmentation by competitive neural processing," Neural Processing Letters, vol.22, no.2, pp.125-147, Oct. 2005.
[17] Mota, S., E. Ros, and F. D. Toro, "A computing architecture for correcting perspective distortion in motion-detection based visual systems," Scalable Computing: Practice and Experience, vol.8, no.4, pp.387-394, Dec. 2007.
[18] Rudar, M., W. Enkelmann, and R. Garnitz, "Highway lane change assistant," in Proc. IEEE Intelligent Vehicles Symp., Dresden, Germany, Jun.17-21, 2002, pp.204-244.
[19] Sun, Z., G. Bebis, and R. Miller, "On-road vehicle detection: a review," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.28, no.5, pp.694-711, May 2006.
[20] Techmer, A., "Real-time motion analysis for monitoring the rear and lateral road," in Proc. IEEE Intelligent Vehicles Symp., Parma, Italy, Jun.14-17, 2004, pp.704-709.
[21] Wang, J., G. Bebis, and R. Miller, "Overtaking vehicle detection using dynamic and quasi-Static background modeling," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Diego, CA, Jun.20-26, 2005, pp.64-71.
[22] Wu, B.-F., W.-H. Chen, C.-W. Chang, C.-J. Chen, and M.-W. Chung, "A new vehicle detection with distance estimation for lane change warning systems," in Proc. IEEE Intelligent Vehicles Symp., Istanbul, Turkey, Jun.13-15, 2007, pp.698-703.

指導教授

曾定章(Din-Chang Tseng)

審核日期

2009-7-23

推文