# 臺灣博碩士論文加值系統

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 隨著公路運輸的發展，使人們能夠自行駕車前往目的地。然而不幸的是，這卻同樣導致了交通事故數量的增加。高級輔助駕駛系統的設計宗旨是希望能夠警示駕駛人潛在的危險，許多的駕駛輔助系統是基於車輛未來路徑的預測為基礎所設計。當車輛行駛於道路上，道路上的情況，包含前方道路趨勢以及與前方車輛之間的距離，是能夠影響車輛未來路徑的重要因素。這些道路資訊能夠藉由裝置於車內的攝影機拍攝所得。在這篇論文中，我們提出了一種影像處理系統，用以擷取出拍攝影像中的道路資訊。這個系統由兩種演算法所組成，彎道偵測演算法，以及距離轉換演算法。首先，我們利用彎道偵測演算法進行車道線的偵測，並計算出消失點的位置，接著再通過消失點的位置判斷出道路的趨勢。而距離轉換演算法則是用於當影像中偵測到實驗車前方的車輛，該演算法將實驗車與前方車輛之間的距離，轉換成現實世界的距離。通過分析與實驗，結果顯示我們提出的系統相較於基準演算法有較高的準確率。
 The development of road transportation makes it easier for people to drive their vehicles. Unfortunately, it also causes the increasing number of traffic accidents. Advanced driver assistance systems, which in many ways is based on the future trajectory prediction of the vehicle, are developed to alert the driver for potential dangers. Road conditions, including road geometry and the distance between lead vehicle and host vehicle, are important factors in improving accuracy of future trajectory prediction. The road conditions can be obtained by the camera affixed in the vehicle. In this thesis, we propose a image processing system, which includes a curve detection algorithm (CDA) and a distance conversion (DC) algorithm, to obtain these road conditions from the image. First, CDA detects the lane stripes and calculates the vanishing point. The road trend can then be identified according to the location of the vanishing point. DC is used to convert the distance between the lead and host vehicles in the image to the real distance. Through analyses and experiments, it is shown that the proposed system achieves a higher precision than the baseline algorithms.
 1 Introduction 12 RelatedWork 42.1 Front Car Detection and Car Distance Calculation . . . . . . . . . . . . . . 42.2 Lane Detection and Vanishing Point Detection . . . . . . . . . . . . . . . . 52.3 Path Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Preliminary 103.1 Hough Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.2 Canny Edge Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.3 Gaussian Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.4 Haar-like Feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.5 Exponential Moving Average . . . . . . . . . . . . . . . . . . . . . . . . . . 164 System Design 184.1 The Basic Idea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.2 Curve Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204.2.1 Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204.2.2 Feature Extraction and Validation . . . . . . . . . . . . . . . . . . . 214.2.3 Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.3 Vehicle Detection and Distance Calculation . . . . . . . . . . . . . . . . . . 254.3.1 Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254.3.2 Feature Extraction and Validation . . . . . . . . . . . . . . . . . . . 264.3.3 Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 Performance 305.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305.1.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305.1.2 Simulation Conﬁguration . . . . . . . . . . . . . . . . . . . . . . . . 325.2 Simulation Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . 356 Conclusions 41