臺灣博碩士論文加值系統

台灣車禍每年都在發生，其中人為的案發比率相當的高，多半是危險駕駛和疲勞駕駛，要降低其發生的機率，就必須遵守『酒後不開車』或『疲勞不上路』的準則，此外可以倚賴電子設備系統輔助，以達到安全駕駛的成效，例如:車道偏移警示系統。此系統作用以後就會自動開始偵測兩側車道線的位置，當駕駛者處於精神不集中的狀態時，會造成車子靠近或欲跨越車道線至另一車道時，系統便會發出警示，使失去注意力之駕駛者回神，避免發生事故。國內已有應用至汽車的產品，如LUXGEN　7　SUV的行車偏移偵測警示系統和車輛測試研究中心（ARTC）的車道偏移警示晶片。
本研究利用嵌入式平台來開發演算法以實現車道線偏移警示系統之研究，演算法利用偵測影像中的邊緣點，經過過濾策略進行雜訊的去除，搭配使用霍夫轉換運算偵測出車道線，相較於直線的車道線偵測、彎道的車道線偵測，有不同的運算與標示方法。配合車道線偵測功能與交通規則，演算法就可以制定出良好的偏移警訊機制或變換車道能力。

關鍵字:車道線偵測、偏移警示系統、駕駛輔助系統

Car accidents happen in Taiwan every year. The ratio of human factors is very high, most of human factors are drowsy driving. If People to reduce car accidents, they must keep the guidelines “do not drink and drive” and “do not drive when drowsing”. In addition, we can rely on the electronic equipment system such as Lane Departure Warning System to achieve the effectiveness of safe driving. After operating the system, it automatically begins to detect the position of the two lane lines. When the driver is an inattentive state, the car is close to or across the side of lane. The system issues a warning to remind the driver focusing on driving. The technique is applied to automotive products in Taiwan, like LUXGEN 7 With Lane offset detection warning system and ARTC（Automotive Research and Testing Center）developed on Lane Departure Warning chips.
In this study, the embedded platform develops an algorithm to finish the lane departure warning system. Firstly, the algorithm detects feature points of a lane, secondly, using the Hough Transform operator marks the lane line after removing noise with the filtering policy. Compared to the straight lane or curve lane detection, the proposed algorithm has advantages for computation speed and lane-mark detection methods. Together with lane detection and traffic rules, the propose algorithm can develops a good offset warning mechanism, and has the ability to change lanes.
Key words：Lane detection, departure -warning -system, driver assistance systems

中文摘要
Abstract
目錄
表目錄
圖目錄
1. 緒論
1.1 研究背景
1.2 研究動機
1.3 研究目的
1.4 論文架構
2. 文獻探討
3. 研究方法
3.1 車道線偵測演算法
3.1.1 初始與追蹤
3.1.2 霍夫轉換
3.1.3 感興趣區域的定義
3.1.4 最小平方法
3.2 車道偏移警示
4. 平台介紹與實驗結果
4.1 平台介紹
4.2 實驗結果
5. 結論與未來展望
參考文獻

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