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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/9401


    Title: 先進安全車輛的道路線及前車視覺偵測技術;Vision Detection of Lanes and Vehicles for Advanced Safety Vehicles
    Authors: 沈信良;Xin-Liang Shen
    Contributors: 資訊工程研究所
    Keywords: 相關性;前車偵測;道路線偵測;道路線分類;樣板;correlation;Lane detection;template;lane classification;Vehicle detection
    Date: 2007-06-29
    Issue Date: 2009-09-22 11:46:59 (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致死。有鑑於此,發展車 輛輔助安全駕駛系統,也就越顯得重要。在安全駕駛的研究上,我們架 設一部相機於車前,用來偵測及追蹤前車及道路線資訊,分析前車和已 車的行車狀況,以確保駕駛人於行車時的安全。 到目前為止我們的系統已有十二項偵測及分析功能 : 車道線偵 測、多車道估計、虛實道路線分類、黃白道路線分類、單雙道路線分類、 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。雨天的前車偵測與追蹤、黃白道 路線分類、單雙道路線分類、區分行駛於一般市區道路時遇到路口問題 和夜間駕駛時燈源發散現象是本論文所提供的。 在雨天前車的偵測中,我們以車子底部和輪胎顏色較深的特性,設 計樣板並且藉由樣板匹配來偵測前車的可能位置,去除樣板重疊的部份 來加快系統執行效率和增加執行效果。之後,再以一個小矩形框來代表 我們所找到的前車區塊。最後再利用車寬和車道寬的比例、對稱性、及 區塊色彩標準差來驗證是否真的是車輛。在道路線偵測方面,根據所得 到的道路線兩側位置,取出道路線中線的顏色資訊,藉此區分黃白及單 雙的道路線,再利用影像連續性的觀念,記錄每張影像中道路線的角度 及截距,並且計算其變化程度並修正,藉此判斷是否為相機震動或是遇 到路口。而利用道路線寬的觀念,來克服夜間駕駛時,因前車車燈或是 路燈產生的燈源發散現象。 我們以多種影像:晴天、陰天、多雲、雨天及夜晚在一般道路及高 速公路等影像,來測試我們的偵測效能。從實驗結果顯示,我們所提出 的系統可以在不同的天候及駕駛狀況下,即時且快速的偵測前車及道路 線。 Developing real-time automotive driver assistance systems to alert drivers about driving environments and possible collision with other vehicles has attracted much attention lately. In order to achieve such a system, we use a camera mounted on a vehicle to capture road scenes for lane and preceding vehicle detection. The proposed system consists of twelve functions: lane detection, multiple lane estimation, classification of solid/dashed lane marks, classification of yellow/white lane marks, classification of single/double lane marks, vehicle direction estimation, vehicle lateral offset estimation, lane departure warning, preceding vehicles detection, distance estimation for preceding vehicles, brake light detection, and turn signal detection. In this thesis, the lane marks are detected by searching the best-fitted parameters of a defined lane model on the image, and we verify the results by the width of lane marks and the difference of the slopes and the intercepts in consecutive image frames. Preceding vehicle detection is divided into two independent parts based on daytime and bad weather conditions. In the daytime condition, we utilize cast shadow, left/right borders of a vehicle to detect preceding vehicles and use ratio of the road width and vehicle width, symmetry, and variance to verify vehicle region. In bad weather condition such as rainy day, we generate several templates based on the tires and the bottom of vehicles. According to template matching, we can find out the vehicle candidates and then use ratio of the road width and vehicle width, symmetry, and variance to verify. In the experiments, the proposed methods are evaluated on several different weather conditions such as sunny day, misty day, dusky day, cloudy day, rainy day, and night. The average vehicle detected rate is 94.5 % and average processing time for executing all twelve functions is about 0.045 second run on a general PC with Intel® Pentium® D 3.0GHz CPU. From the experimental results, we find that the proposed approaches can stably detect or track the lanes and preceding vehicles in real time.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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