本論文目的為發展前車距離偵測系統，幫助駕駛保持與前車的安全距離，使肇事量降低。
本論文提出使用最小魏卡森(Least Wilcoxon, LW)迴歸分析方法取代原有的權重最小平方迴歸分析方法，最小魏卡森可以降低不精確消逝點與雜訊干擾的影響，使道路線的標記更精確且強健。距離對照表的建立與更新，利用道路虛線的規律建立距離對照表，使單純影像處理能在不需要太多硬體參數也能得到影像中距離的關係。
比較模擬實驗的結果，可以看出最小魏卡森迴歸分析比權重最小平方迴歸分析更為貼近實際道路線。本論文提出之距離對照表建立與更新方法建立的對照表，也能準確地獲得前車的距離資訊，並判斷避免碰撞前車的安全。

The aim of this thesis is to develop the forward vehicle distance detection system. This system can help the driver maintain a safe distance from the forward vehicle and reduce the accidents.
This study proposes a novel regression analysis method based on least Wilcoxon instead of traditional weighted least squares method. The least Wilcoxon could reduce interference of imprecise vanishing point and noise to make lane marking more precise and robust. The distance table is established and updated following the building rule of road dash line to measure the distance of forwarding vehicle in the captured image.
Comparing the results of the simulation experiments, it showed the regression analysis with least Wilcoxon is closer to the actual road line than that with weighted least squares. The distance table is built by the algorithm could also accurately measure the distance of the forward vehicle and judge the safety for collision avoidance.

第一章 緒論 1
1-1 前言 1
1-2 文獻回顧 3
1-3 動機與目的 4
1-4 研究方法與貢獻 6
1-5 論文架構 8
第二章 道路線及前車偵測 9
2-1 道路線偵測 9
2-2 道路線辨識 17
2-3 前車偵測 20
2-4 安全係數 23
2-5 自校正距離對照表 25
第三章 前車距離偵測系統 29
3-1 最小魏卡森迴歸分析應用於道路線偵測 29
3-1-1 最小魏卡森範數的架構 30
3-1-2 最小魏卡森應用於線性函數 31
3-1-3 最小魏卡森與權重最小平方分析與探討 34
3-2 距離對照表建立與更新演算法 36
3-2-1 對照表演算法架構 37
3-2-2 對照表格建立與更新 38
3-3 前車距離偵測系統 43
第四章 實驗模擬 45
4-1 兩種迴歸分析於各種環境比較 45
4-1-1 晴天 45
4-1-2 陰天 47
4-1-3 雨天 50
4-2 對照表建立與更新模擬 51
第五章 結論與未來展望 59
5-1 結論 59
5-2 未來方向 60
參考文獻 63
作者簡歷 67

[1] Transportation Society of America, http://www.itsa.org/
[2] W. S. Wijesoma, K. R. S. Kodagoda, and A. P. Balasuriya, “Road-boundary detection and tracking using ladar sensing,” IEEE Trans. on Robotics and Automation, vol. 20, no. 3, pp. 456-464, Jun. 2004.
[3] J. Sparbert, K. Dietmayer, and D. Streller, “Lane detection and street type classification using laser range images,” in Proc. IEEE Intelligent Transportation Systems, pp. 454 – 459, Oakland, California, USA, Aug. 2001.
[4] W. S. Wijesoma, K. R. S. Kodagoda, A. P. Balasuriya, and E. K. Teoh, “Laser and camera for road edge and mid-line detection,” in Proc. the Second Inter. Workshop on Robot Motion and Control, pp. 269-274, Bukowy Dworek, Poland, Oct. 2001.
[5] 曾俊元，以視覺感知的智慧型車輛防撞系統之研究，國立東華大學碩士論文，2003。
[6] 黃偉秩，適用於嵌入式系統之快速道路線追蹤演算法，國立東華大學碩士論文，2009。
[7] 許智誠，結合慣性道路線標記與車輛陰影之前車偵測演算法，國立東華大學碩士論文，2011。
[8] 黎俊宏，基於時間序列之道路線種類辨識，國立東華大學碩士論文，2013。
[9] 紀富翔，以電腦視覺為主的快速前車偵測與警示系統，國立東華大學碩士論文，2013。
[10] 顏妤樺，基於智慧型手機的自適應性道路偏移警示系統，國立東華大學碩士論文，2014。
[11] 鄭鈺傑，基於智慧型手機以車輛對稱性與貝氏分類為基礎的前車辨識演算法，國立東華大學碩士論文，2015。
[12] T. Y. Sun, S. J. Tsai, and V. Chan, “HSI color model based lane-marking detection,” in Proc. IEEE Intelligent Transportation Systems Conf., pp. 1168-1172, Toronto, Canada, Sept. 2006.
[13] C. L. Huo, Y. H. Yu, and T. Y Sun, “Lane departure warning system based on dynamic vanishing point adjustment,” in Proc. 2012 IEEE 1st Global Consumer Electronics, pp. 25-28, Tokyo, Japan. , Oct. 2-5, 2012
[14] Y. H. Yen, C. L. Huo, and T.Y. Sun, “Adaptive Lane Departure Warning System on Android Smartphone,” in Proc. IEEE. Consumer Electronics- Taiwan (ICCE-TW), pp. 67-68, Taipei, Taiwan, May. 26-28, 2014
[15] T. Y. Sun and W. C. Huang, “Embedded vehicle lane-marking tracking system,” in Proc. IEEE International Symposium on Consumer Electronics, pp. 627-631, Kyoto, Japan, May 2009.
[16] P. Y. Hsiao, C. W. Yeh, S. S. Huang, and L. C. Fu, “A portable vision-based real-time lane departure warning system: day and night,” IEEE Trans. on Vehicular Technology, Vol. 58, No. 4, pp. 2089 – 2094, May 2009.
[17] K. Y. Chiu and S. F. Lin, “Lane detection using color-based segmentation,” in Proc. IEEE Intelligent Vehicles Symposium, pp. 706 – 711, Las Vegas, Nevada, USA, June 2005.
[18] Y. Wanga, E. K. Teoha, and D. Shenb, “Lane detection and tracking using B-Snake,” Image and Vision Computing, Vol. 22, pp. 269-280, April 2004.
[19] B. Yu and A. K. Jain, “Lane boundary detection using a multiresolution Hough transform,” in Proc. International Conference on Image Processing, Vol. 2, pp. 748 – 751, Washington, DC, USA, Oct. 1997.
[20] C. R. Jung and C. R. Kelber, “A robust linear-parabolic model for lane following,” in Proc. 17th Brazilian Symposium on Computer Graphics and Image Processing, pp. 72 – 79, Curitiba, PR, Brazil, Oct. 2004.
[21] C. R. Jung and C. R. Kelber, “An improved linear-parabolic model for lane following and curve detection,” in Proc. the 18th Brazilian Symposium on Computer Graphics and Image Processing, pp.131 – 138, Natal, RN, Brazil, Oct. 2005.
[22] M. Boumediene, A. Ouamri, and N. Dahnoun, “Lane boundary detection and tracking using NNF and HMM approaches,” in Proc. IEEE Intelligent Vehicles Symposium, pp. 1107-1111, Istanbul, Turkey, June 2007.
[23] T. O. and N. Suganuma, “Development of Preceding Vehicle Recognition Algorithm for Lead Vehicle of Autonomous Platooning System Based on Multi Sensor Fusion and Digital Map,” in Proc. SICE Annual Conference, pp. 247-250, Tokyo, Japan, Sept. 2011.
[24] International commission on non-ionizing radiation protection: Guidelines for limiting exposure to time-varying electric and magnetic fields. Health Physic 74:494-522, 1998.
[25] A. Gern, U. Franke, and P. Levi, “Advanced Lane Recognition-fusing Vision and Radar,” in Proc. the 2000 IEEE Intelligent Vehicle Symposium, pp. 45 – 51, Oct. 2000.
[26] C. D. Wann and Y. M. Chen, “Position tracking and velocity estimation for mobile positioning systems,” IEEE Trans, On Wireless Personal Multimedia Communications, vol. 1, pp. 310 – 314, Oct. 2002.
[27] J.K Keamey, X Yang, and S. Zhang, “Camera calibration using geometric constraints,” in Proc. IEEE Computer Society Conference, On Computer Vision and Pattern Recognition, pp. 672 – 679, Jun. 1989.
[28] A. Petrovai, R. G. Danescu, and S. Nedevschi, “A stereovision based approach for detecting and tracking lane and forward obstacles on mobile devices,” in Proc. IEEE Intelligent Vehicles Symposium, pp. 634–641, Seoul, South Korea, June 28 - July 1 2015.
[29] M. Bertozzi, A. Broggi, M. Cellario, A. Fascioli, P. Lombardi, and M. Porta, “Artificial vision in road vehicles,” in Proc. the IEEE Special Issue on Technology and Tools for Visual Perception, Vol. 9, Issue: 7, pp. 1258-1271, Jul. 2002.
[30] Y. H. Yu, F. H. Chi, C. L. Huo, and T. Y. Sun, “A self-calibrating distance lookup method for camera-equipped vehicular system,” in Proc. 2012 IEEE 1st Global Conf. on Consumer Electronics, pp.171-174, Tokyo, Japan, Oct. 2-5, 2012.
[31] 內政部警政署 104年A1類道路交通事故肇事者與原因分析, https://www.npa.gov.tw/NPAGip/wSite/ct?xItem=79664&ctNode=12594&mp=1,27,Jul. 2018
[32] 內政部警政署 105年A1類道路交通事故肇事者與原因分析, https://www.npa.gov.tw/NPAGip/wSite/ct?xItem=83511&ctNode=12594&mp=1,27,Jul. 2018
[33] 內政部警政署 106年A1類道路交通事故肇事者與原因分析, https://www.npa.gov.tw/NPAGip/wSite/ct?xItem=86936&ctNode=12594&mp=1,27,Jul. 2018
[34] 蔡承翰，自構式最小魏卡森廣義的放射狀基函數類神經網路模糊推論系統之研究，國立東華大學碩士論文，2008。
[35] Y. K Yang, T. Y. Sun, C. L. Huo, Y. H. Yu, C. C. Liu and C. H. Tsai, “A novel self-constructing Radial Basis Function Neural-Fuzzy System,” Applied Soft Computing, vol. 13, no. 5, pp. 2390-2404, 2013.
[36] R. V. Hogg, J. W. McKean, and A. T. Craig, Introduction to Mathematical Statistics, 6th ed. Englewood Cliffs, NJ: Prentice-Hall, 2005.
[37] 交通部公路總局, http://www.thb.gov.tw/