適應亮度與天候變化的前車碰撞警示系統

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：12

、訪客IP：3.145.18.97

姓名

蔡昀昇(Yun-Sheng Cai) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

適應亮度與天候變化的前車碰撞警示系統
(Light-and-Weather-Adapted Forward Collision Warning System)

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)

先進駕駛輔助系統 (advanced driver assistance system, ADAS) 在近年來已經成為重要的研究議題；利用先進的科技輔佐駕駛者，讓駕駛者更加的安全並大幅下降交通事故的發生。目前的駕駛輔助系統有許多項目;例如車道偏離警示、行人偵測剎車系統、前方車輛偵測碰撞警示等。本研究分析了天候明暗變化，提出了可適應天候不同明暗變化的前車偵測技術並配合穩定偵測的判定準則，發展成一個可適應亮度與天候變化的前車碰撞警示系統。
首先我們提出一個判斷白天與夜晚的準則，針對不同的時間點使用不同的演算法。在判斷白天夜晚的準則中，我們在大小為320240影像裡取出固定數量和固定位置的像素來代表整張影像，求出像素的平均亮度和小於某個亮度門檻值的像素佔所有像素的比例，依照這個比例定義出門檻值來分別日夜間。
在日間偵測前方車輛的演算法中，我們會先依據偵測出來的車道線定義偵測範圍來避免非路面上的物體影響偵測，並對偵測範圍內做灰階第二差分找出不同強度的垂直邊和水平邊。而為了適應不同的天候環境，我們統計邊強度資訊與二值化門檻值的關係，求出動態門檻值並偵測影像邊界點。最後連結邊界點成平邊及垂直線；水平邊線分成為負水平邊和正水平邊。我們根據水平邊的兩端點位置找出是否有顯著的垂直邊或是是否具有左右對稱的垂直邊;若符合此條件的水平邊，再根據水平邊和車道寬比例判斷是否位在我們定義的道路寬比例中，若符合則產生候選車輛。當天候狀況不良導致影像模糊時，我們採用車尾燈來偵測車輛;例如，在大雨中，影像中的前車非常模糊，無法採用邊資訊來偵測車輛。而在此種天候情況下駕駛者通常會開啟車尾燈來提醒後方車輛，因此我們擷取車尾燈的紅色特徵來偵測車輛。在日間演算法中，一旦偵測到前方車輛我們會估計前方車輛的距離和計算碰撞時間 (time to collision, TTC) 判斷是否會對駕駛者產生危險並提供駕駛者警示。
在夜間前車偵測中，我們使用車尾燈在影像中所呈現的特徵來偵測車尾燈。再以車尾燈的垂直距離、水平距離、移動軌跡、與兩車尾燈相關性等四項特徵配對，來偵測可能的候選車輛。最後再依據車尾燈在不同時間點寬度的變化量，計算與前車的碰撞時間，提供駕駛者警示。夜間車輛偵測和天候不良的日間偵測都是利用車尾燈資訊，由於我們所使用的相機設備擁有自動曝光功能，而夜間的平均亮度較低，因此相機會將畫面亮度提高，使得車尾燈產生過曝情形，夜間主要是利用此過曝點來偵測車尾燈。
論文最後將介紹我們的實驗器材與開發環境，並統計日間車輛偵測在各種不同天候環境下的偵測率和偵測結果，根據數據顯示本系統在一般天候環境下，前方有車輛且正確偵測的偵測率皆可達到90%以上；並且分析系統效能，在Intel(R) Core(TM)2 Duo CPU 7400 @2.80GHz 2.80GHz 和4.0GB DDR2 RAM 的個人電腦上，整體系統執行速度每秒約為50張影像，在我們的實驗環境中相機每秒拍攝30張影像，因此本系統是可以達到即時性的車輛偵測。

摘要(英)

Advanced driver assistance systems (ADAS) has become an important research topic in recent years, the use of advanced technology assisted motorists to allow motorists more secure and substantial decline in traffic accidents. The standard driver assistance systems have many items, such as lane departure warning system, pedestrian detection system, forward collision warning system. This paper analyzed the weather brightness changes, and proposed changes in brightness can be adapted to different weather vehicle detection algorithm and stable detection criterion.we developed into an adaptive brightness and weather forward collision warning system.
First, we integrate the car detection in day and night time, using different algorithm for the different time. The beginning we defined a criterion for judging what it day or night is, and decided what the algorithm we choose. The criterion judging day or night, we fetch fixed number of frame from different video. We fetch fixed pixels at fixed position for representing the whole image which size is 320×240, then calculating the average intensity and the percentage of bellow the threshold of pixels we fetch. According to the criterion, judging what it day or night.
In daytime forwarding collision warning system, we define the detection area based on detected lane mark to avoid the impact of non-detection of objects on the road, and fetch vertical edge response image and horizontal edge response image by second difference mask. For adapting different weather, we use edge strength histogram and our binary threshold function to determine dynamically threshold value. we generate the bi-level gradient image with dynamically threshold. Then find the continuous pixels belong to the horizontal edge, the horizontal edge we divided into positive and negative for representing the bottom of car and body of car respectively. We find whether it has enough of vertical edge patency or symmetric vertical edge pair at the end point of horizontal edge. If the horizontal edge confirmed, we calculate the proportion of width of the horizontal edge with lane width. If the proportion confirmed, the candidate vehicles generate. In bad weather condition, the vehicles are detected by tail lights. For example, in the heavy rain, the vehicle is very vague and can not use edge information to detect. In this case, the driver usually turns taillights to warn vehicles behind, so we captured the red taillight features to detect vehicles. In daytime forwarding collision warning system, once the vehicle is detected we will estimate the distance and calculate the time to collision to (TTC) judge whether they would be dangerous drivers and provides the driver alert.
In nighttime forwarding collision warning system, we detect the tails of preceding vehicles. Then we pair the lights using the features of the horizontal distance, the vertical heights, the trajectory, and correlation of a pair of lights. Finally, we estimate the time to collision of the verified light pair and providing the warning for the driver. Vehicle detection at night and bad weather are the use of daytime detection tail lights, because of we are using the camera device has automatic exposure control, while at night the average brightness is low, so the camera will increase the brightness of the screen, it’s making the car’s taillights overexposure. the night is used to detect this overexposure taillights.
Finally, the paper introduces our experimental equipment and the development environment, and statistical daytime vehicle detection in various weather conditions and the detection results of the detection rate. According to the data indicate that the system accurate detection of vehicles in front measured detection rate can reach 90% in normal weather conditions, and analyze system performance. In our experiment, the system execution speed of approximately 50 frames per second and camera captured 30 frames per second, so the system can achieve real-time vehicle detection.

關鍵字(中)

★ 前車偵測
★ 碰撞警示
★ 電腦視覺
★ 車輛安全
★ 智慧型車輛

關鍵字(英)

★ Forward Collision Warning
★ vehicle detection

論文目次

摘要 ii
目錄 iv
圖目錄 vi
表目錄 xi
第一章緒論 1
1.1 研究動機 1
1.2 系統架構 4
1.3 論文架構 7
第二章相關研究 8
2.1 車輛偵測 8
2.1.1 日間車輛偵測 8
2.1.2 夜間車輛偵測 13
2.2 車輛追蹤 18
第三章日間車輛偵測系統 20
3.1 線段偵測 20
3.2 車道線偵測 21
3.3 適應天候的邊強度二值化 23
3.3.1定義ROI 22
3.3.2適應二值化門檻值 24
3.4 產生有意義水平線線段 25
3.5 刪除線段 26
3.5.1 刪除過短線段 27
3.5.2 刪除斜率過大的線段 27
3.6 產生候選車輛 28
3.6.1 確認負水平邊兩端點上方垂直邊顯著性 28
3.6.2 天候因素造成拉長的車底陰影 29
3.6.3 車體所產生的水平邊 31
3.7 刪除重疊的候選車輛 32
3.8 氣候不良下的前車偵測 33
3.8.1 確認車尾燈區塊 33
3.8.2估計車底位置 34
第四章計算前車距離與碰撞時間(TTC) 35
4.1 計算前車距離 35
4.2 碰撞時間 37
第五章實驗 38
5.1 實驗環境 38
5.2 實驗結果 39
第六章結論與未來展望 51
6.1 結論 51
6.2 未來展望 53
參考文獻 54

參考文獻

[1] Armingol, J. M., A. de la Escalera, C. Hilario, J. M. Collado, J. P. Carrasco, M. J. Flores, J. M. Pastor, and F. J. Rodríguez, "IVVI: Intelligent vehicle based on visual information," Robotics and Autonomous Systems, vol.55, no.12, pp.904-916, 2007.
[2] American National Standard Institution, Photographic Exposure Guide, American National Standard for Photography, Tech. Rep. ANSI PH2.7-1986, 1986.
[3] Baker, S. and I. Matthews, "Lucas-Kanade 20 years on: a unifying framework," Int. Journal of Computer Vision, vol.56, no.3, pp.221-255, 2004.
[4] Broggi , A., P. Cerri, and P. C. Antonello, "Multi-resolution vehicle detection using artificial vision," in Proc. Conf. Intelligent Vehicles Symp., Parma, Italy, June. 14-17, 2004, pp.310-314.
[5] Chu, J., L. Ji, L. Guo, Libibing, and R. Wang, "Study on method of detecting preceding vehicle based on monocular camera," in Proc. IEEE Intelligent Vehicles Symp., Parma, Italy, Jun.14-17, 2004, pp.750-755.
[6] Clady, X., F. Collange, F. Jurie, and P. Martinet, "Cars detection and tracking with a vision sensor," in Proc. IEEE Intelligent Vehicles Symp., Columbus, OH, Jun.9-11, 2003, pp.593-598.
[7] Collado, J. M., C. Hilario, A. De La Escalera, and J. M. Armingol, "Model based vehicle detection for intelligent vehicles," in Proc. of IEEE Intelligent Vehicles Symp., Parma, Italy, Jun.14-17, 2004, pp.572-577.
[8] Cui, J., F. Liu, Z. Li, and Z. Jia, "Vehicle localisation using a single camera," in Proc. IEEE Symp. Intelligent Vehicles, San Diego, CA, Jun. 21-24, 2010, pp.871-876.
[9] Du, Y. and N. P. Papanikolopoulos, "Real-time vehicle following through a novel symmetry-based approach," in Proc. of IEEE Int. Conf. on Robotics and Automation, Albuquerque, NM, Apr.20-25, 1997, pp.3160-3165.
[10] Gwenaëlle, T., S. Mousset, and A. Bensrhair, "Fast and accurate stereo
vision-based estimation of 3D position and axial motion of road obstacles, "Int. Journal of Image and Graphics, vol.4, no.1, pp. 99-126, 2004.
[11] Gwenaëlle, T., M. Bertozzi, and S. Mousset, and A. Bensrhair, and A. Broggi, "Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis" IEEE Trans. Image Processing, vol. 15, no. 8, pp.2364-2375, 2006.
[12] George, S.K. and N.H.C. Yung, and G.K.H. Pang, "Vehicle shape approximation from motion for visual traffic surveillance" in Proc. IEEE Conf. Intelligent Transportation Systems, Oakland, CA, 2001, pp.610-615.
[13] Hsieh, W.-C., L.-C. Fu, and S.-S. Huang, "Vision based obstacle warning system for on-road driving," in Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Las Vegas, NV, Oct.27-31, 2003, pp.3668-3673.
[14] Huang, S.-S., C.-J. Chen, P.-Y. Hsiao, and L.-C. Fu, "On-board vision system for lane recognition and front-vehicle detection to enhance driver’s awareness," in Proc. Conf. Robotics and Automation, New Orleans, LA, April.26-May.1, 2004, pp.2456-2461.
[15] Huang, Y.-C. and D.-C. Tseng, "A vision-based vehicle to vehicle detection and tracking system," in Proc. of 18th IPPR Conf. on Computer Vision, Graphics and Image Processing, Taipei, Taiwan, Aug.21-23, 2005, pp.866-873.
[16] Jazayeri, A., C. Hongyuan, Z. Jiang Yu, and M. Tuceryan, "Vehicle detection and tracking in car video based on motion model," IEEE Trans. Intelligent Transportation Systems, vol.12, no.2, pp.583-595, 2011.
[17] Jin, L.-S., B.-Y. Gu, R.-B. Wang, L. Guo, Y.-B. Zhao, and L.-H. Li, "Preceding vehicle detection based on multi-characteristics fusion," in Proc. IEEE INT. Conf. Vehicular Electronics and Safety, Shanghai, China, Dec. 13-15, 2006, pp.356-360.
[18] Kalman, R. E., "A new approach to linear filtering and prediction problems," Trans. of ASME, vol.82, pp.35-45, 1960.
[19] Lewis, J. P., Fast Normalized Cross-Correlation, Industrial Light and Magic, http://citeseerx.ist.psu.edu/viewdoc/download?doi=
10.1.1.21.6062&rep=rep1&type=pdf, 1995.
[20] Niknejad, H. T., S. Mita, D. McAllester, and T. Naito, "Vision-based vehicle detection for nighttime with discriminately trained mixture of weighted deformable part models," in Proc. 14st Int. Conf. Intelligent Transportation Systems, Washington, DC, Oct. 5-7, 2011, pp.1560-1565.
[21] O’Malley, R., E. Jones, and M. Glavin, "Rear-lamp vehicle detection and tracking in low-exposure color video for night conditions," IEEE Trans. on Intelligent Transportation Systems, vol.11, no.2, pp.453-462, 2010.
[22] Schamm, T., C.-V. Carlowitz, and J. M. Zollner, "On-road vehicle detection during dusk and at night," in Proc. Symp. Intelligent Vehicles, San Diego, CA, June. 21-24, 2010, pp.418-423.
[23] Schwarzinger, M., T. Zielke, D. Noll, M. Brauckmann, and W. Vonseelen, "Vision-based car-following detection tracking and identification," in Proc. of IEEE Intelligent Vehicles Symp., Detroit, MI, Jun.29-Jul.1, 1992, pp.24-29.
[24] Shyr, B.-Y., Daytime Detection of Leading and Neighboring Vehicles on Highway: A Major Capability for the Driver Assistant Vision System, Master Thesis, Electrical Engineering, National Chung Cheng Univ., Chia-yi, Taiwan, 2003.
[25] Sun, Z., R. Miller, G. Bebis, and D. DiMeo, "A real-time precrash vehicle detection system," in Proc. 6st IEEE. Conf. Applications of Computer Vision, Orlando, Florida, Dec. 3-4, 2002, pp.171-176.
[26] Tseng D.-C. and C.-W. Lin, "Versatile lane departure warning using 3D visual geometry," Int. Journal of Innovative Computing Information and Control, vol.9, no.5, pp.1899-1917, 2013.
[27] Tseng, D.-C., Monocular Computer Vision Aided Road Vehicle Driving for Safety, U.S. Patent No. 6765480, 2004.
[28] Viola, P. and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Proc. Conf. Computer Vision and Pattern Recognition, Kauai, HI, Dec. 8-14, 2001, pp.I-511-I-518.
[29] Tao X., and C. Debrunner, "Stochastic car tracking with line- and
color-based features," IEEE Trans. Intelligent Transportation Systems, vol. 5, no.4, pp. 324-328, 2004.
[30] Zielke, T., M. Brauckmann, and W. Von Seelen, "Intensity and edge-based symmetry detection with an application to car-following," CVGIP: Image Understanding, vol.56, no.2, pp.177-190, 1993.

指導教授

曾定章(Din-Chang Tseng)

審核日期

2013-7-25

推文