先進安全車輛的前方與盲點視覺偵測; Forward and Blind-spot Visual Detection for Advanced Safety Vehicles

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/9608

Title:	先進安全車輛的前方與盲點視覺偵測;Forward and Blind-spot Visual Detection for Advanced Safety Vehicles
Authors:	林奇叡;Chi-jui Lin
Contributors:	資訊工程研究所
Keywords:	車輛偵測;盲點偵測;道路線偵測;距離估計;blind-spot detection;distance estimation;lane detection;vehicle detection
Date:	2008-06-27
Issue Date:	2009-09-22 11:51:39 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	在台灣，每年有超過兩千人因為交通事故而死。有鑑於此，發展車輛輔助安全駕駛的議題，也就越顯得重要。在我們的安全駕駛的研究議題上，我們架設相機在車上，再將拍攝的影像傳到電腦上執行前車或側後方車輛的偵測及追蹤，並分析前車或側後方車與己車的行車狀況，確保駕駛人於行車時的安全。到目前為止我們的前車系統已有十項偵測及分析功能：車道線偵測、多車道估計、虛實車道線分類、己車方向估計、己車左右位置估計、偏離車道警示、前車偵測、前車距離估計、煞車燈偵測、及方向燈偵測。我們改進了其中部份功能，另外也新加入了側邊盲點的視覺偵測功能。到目前為止側邊盲點的視覺偵測已有四項偵測及分析功能：車道線偵測、虛實車道線分類、側方車輛偵測、及側方車輛距離估計。利用事先定義的車道線模組，我們尋找最符合該模組的近側車道線。並且以車底陰影及車輛左右垂直邊來偵測車體區塊，並且對車體區塊進行追蹤。我們以多種影像;例如，晴天、陰天、及多雲的高速公路等，來測試我們演算法的偵測效能。從實驗結果顯示，我們所提出的方法可以在不同的天候狀況下，即時且快速的偵測前方與側邊車輛。前車偵測在各種天候下約有95 % 的正確率;側邊車輛偵測約有92 % 的正確率。 Developing real-time automotive driver assistance systems to alert drivers for possible collision with other vehicles has attracted lots of attention lately. In this study, we use cameras mounted on a vehicle to capture road scenes for forward lane detection, preceding vehicle detection, and blind-spot visual detection. The forward visual system consists of ten functions: lane detection, multiple lane estimation, classification of solid/dashed lane marks, direction estimation, vehicle lateral offset estimation, lane departure warning, preceding vehicles detection, distance of preceding vehicle estimation, brake light detection, and turn signal detection. The blind-spot system contains four modules: near lane mark detection, classification of solid/dashed lane marks, side vehicle detection, and distance estimation. In the proposed system, the lane marks are detected by searching the optimal parameters of a defined lane model on the images. Preceding vehicle is detected by underneath shadow and left/right borders; then use ratio of the road width and vehicle width, symmetry, and variance to verify vehicles. The proposed vehicle detector uses template matching, symmetry, and appearance times to detect brake light, and uses template matching, position check, cluster, and frequency to detect turn signals. Side vehicle is detected by underneath shadow and left/right borders. In the experiments, the proposed detectors were evaluated on several different weather conditions such as sunny day, misty day, dusky day, cloudy day, and dark night. From the experiment results, we find that the proposed approach can stably detect or track the lanes and vehicles in real time.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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