根據統計，大部分的交通事故都是和其他車輛發生碰撞，因此先進駕駛輔助系統 (advanced driver assistance systems , ADAS) 已經變成近年熱門的研究議題，透過此系統可以輔助駕駛者，在駕駛者可能會發生危險的情況下提出警示，避免意外發生，目前先進駕駛輔助系統包含車道偏離警示 (LDW) 、前車碰撞警示 (FCW) 、盲點範圍車輛偵測 (BSD) 、行人碰撞警示 (PCW) 等應用。
在本論文中，我們提出一個可以適應天候的前車碰撞警示系統，此系統可以幫助駕駛者偵測前方車輛，避免駕駛者因為分心或者昏睡沒注意到和前方車輛的距離而發生碰撞。
首先，我們會先利用車道線定義偵測範圍，接下來是偵測車輛，在我們車輛偵測的演算法中包含候選車輛產生和車輛驗證兩個部分，分別使用車子的局部特徵和整體特徵。在車輛候選產生階段我們在偵測範圍內找出水平邊和垂直邊，接著根據車輛的幾何特性進而產生候選車輛。而在車輛驗證階段我們使用一個基於主成份分析 (principal component analysis, PCA) 的方法，從大量的車輛影像中擷取出重要的特徵，根據擷取出來的特徵，一個候選車輛區域可以被分解和重建，原始影像和重建影像的相似度則被計算出來用以作為判斷是否為車輛的可能性。理論上，PCA方法是用來排除非車輛的候選影像以減少錯誤警報。找到車輛後我們會進一步估算前方車輛距離並計算碰撞時間判斷是否會對駕駛者產生危險並提供駕駛者警示。
論文最後將介紹我們的實驗器材與開發環境，並統計車輛偵測在各種不同天候環境下的正確率和偵測結果，根據數據顯示本系統在一般天候環境下，正確偵測的正確率平均可達到90%以上；並且在Intel® Core(TM) i5-2400 CPU @3.10GHz 和8.0GB DDR3 RAM 的個人電腦上，整體系統執行速度每秒約為80張影像，在我們的實驗環境中相機每秒拍攝30張影像，因此本系統可以達到即時的車輛偵測。

There were lots of deaths caused by traffic accidents of rear-end collision, advanced driver assistance systems (ADASs) has become an important research topic in recent years. To prevent these fatalities, forward collision warning (FCW) systems have been proposed to protect drivers from the danger due to paying no attention to forward traffic situations. Not only FCW systems but also many other ADASs such as lane departure warning (LDW), blind spot detection (BSD), pedestrian collision warning (PCW), etc. have been developed to assist drivers.
In this thesis, we present a weather-adaptive forward collision warning system, which would help drivers to avoid collisions to the preceding vehicles or obstacles.
In the proposed FCW system, the local features such as horizontal and vertical edges are first calculated. Then edge maps are bi-leveled using a learning thresholding method to adapt the intensity variation of captured images, so that the extraction of edge points is less influenced by bad weather conditions. Third, the preserved edge points are used to generate possible objects . Fourth, the objects are selected based on edge response, location, and symmetry of object candidates to generate vehicle candidates. Three candidate generation schemes are hierarchically designed to extract vehicle candidates in various weather conditions. At last, a method based on principal component analysis (PCA) is proposed to verify the vehicle candidates. PCA is a technique used to extract the important features of a set of vehicle images. Each extracted feature describes a characteristic of vehicle appearance which is defined as a global feature. Depending on the extracted features, a candidate region can be decomposed and reconstructed. The similarity between the original regions and the reconstructed regions are measured to verify the vehicle candidates. Theoretically, PCA method is used to remove the non-vehicle candidates to reduce the false alarm.
The proposed FCW system has been test and evaluated on various weather conditions. The average accuracies of the proposed FCW system in clear and bad weather conditions are 98.5% and 71.8%, respectively. 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.

[1] Arenado, M. I., J.P. Oria, and C. Torre-Ferrero, "Monovision-based vehicle detection, distance and relative speed measurement in urban traffic," IET Intelligent Transport Systems, vol.8, no.8, pp.655-664, 2014.
[2] 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.
[3] Arrospide, J. and L. Salgado, “Log-Gabor filters for image-basedvehicle verification", IEEE. Trans. Image Processing, vol.22, no.6, pp.2286 -2295, 2013.
[4] 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.
[5] Broggi, A., P. Cerri, and P. C. Antonello, "Multi-resolution vehicle detection using artificial vision," in Proc. Conf. Intelligent Vehicles Symp., Parma, Italy, Jun.14-17, 2004, pp.310-314.
[6] Chan, Y.M. , S.S. Huang , L.C. Fu , P.Y. Hsiao , and M.F. Lo , "Vehicle detection and tracking under various lighting conditions using a particle filter," IET Intelligent Transport Systems, vol.6, no.1, pp.1-8, 2012.
[7] Chen, D. Y., G. R. Chen, and Y. W. Wang, "Real-time dynamic vehicle detection on resource-limited mobile platform," IET Computer Vision, vol.7, no.2, pp.81-89, 2013.
 
[8] 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.
[9] 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.
[10] El Ansari, M., S. Mousset, and A. Bensrhair, "Temporal consistent real-time stereo for intelligent vehicles," Pattern Recognition Letters, vol.31, no.11, pp.1226-1238, 2010.
[11] Ferryman, J.M., A.D. Worrall, G.D. Sullivan, and K.D. Baker, " A generic deformable model for vehicle recognition," in Proc. Conf. on British Machine Vision, Birmingham, Sep.11-14, 1995, pp.127-136.
[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] 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.
[14] 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.
 
[15] Huang, C,-C, Vision-based Forward Collision Warning and Parking guiding Techniques for Advanced Safety Vehicles, Ph.D. Dissertation, Dept. of Computer Science and Information Engineering, National Central University, Chung-li, Taiwan,Jul. 2014.
[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] Ling, H. and K. Okada, "Diffusion distance for histogram comparison," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, New York, NY, Jun.17-22, 2006, vol.1, pp.246-253.
[18] Liu, P., W. Li, Y. Wang, and H. Ni , "On-road multi-vehicle tracking algorithm based on an improved particle filter," IET Intelligent Transport Systems, vol.9, no.4, pp.429-441, 2015.
[19] Milanés, V., D. F. Llorca, J. Villagrá, J. Pérez, C. Fernández, I. Parra, C. González, and M. A. Sotelo, "Intelligent automatic overtaking system using vision for vehicle detection," Expert Systems with Applications, vol.39, no.3, pp.3362-3373, 2012.
[20] Rubner, Y., C. Tomasi, and L. J. Guibas, "The earth mover's distance as a metric for image retrieval," Int. Journal of Computer Vision, vol.40, no.2, pp.99-121, Nov. 2000.
[21] 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.
[22] Sivaraman, S. and M. M. Trivedi, "Combining monocular and stereo-vision for real-time vehicle ranging and tracking on multilane highways," in Proc. IEEE Conf. on Intelligent Transportation Systems, Washington DC, Oct.5-7, 2011, pp.1249-1254.
[23] Sun, Z., R. Miller, G. Bebis, and D. DiMeo, "A real-time precrash vehicle detection system," in Proc. 6th IEEE. Conf. Applications of Computer Vision, Orlando, Florida, Dec. 3-4, 2002, pp.171-176.
[24] Surgailis, T., A. Valinevicius, V. Markevicius, D. Navikas, and D. Andriukaitis, "Avoiding forward car collision using stereo vision system," Elektronika ir Elektrotechnika, vol.18, no.8, pp.37-40, 2012.
[25] Teoh, S. S. and T. Bräunl, "Symmetry-based monocular vehicle detection system," Machine Vision and Applications, vol.23, no.5, pp.831-842, 2012.
[26] Toulminet, G., M. Bertozzi, S. Mousset, 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, Aug. 2006.
[27] 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.
[28] Tseng, D.-C., Monocular Computer Vision Aided Road Vehicle Driving for Safety, U.S. Patent No. 6765480, 2004.
[29] 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.
[30] Wu, J. and X. Zhang , "A PCA classifier and its application in vehicle detection," in Proc. IEEE Int. Joint Conf. on Neural Networks, Washington, DC, Jul.15-19, 2001, pp.600-604.
 
[31] 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.