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研究生: 劉行皓
Liu, Hsing-Hao
論文名稱: 車輛偵測與避撞使用雙視覺系統
Vehicle Detection and Collision Avoidance using Dual Vision System
指導教授: 林清一
Lin, Chin E.
學位類別: 碩士
Master
系所名稱: 工學院 - 航空太空工程學系
Department of Aeronautics & Astronautics
論文出版年: 2014
畢業學年度: 102
語文別: 英文
論文頁數: 60
中文關鍵詞: 車輛偵測車輛避撞警示視覺影像雙鏡頭
外文關鍵詞: Vehicle detection, Vehicle avoidance alert, Visual image, Dual vision
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  • 駕駛不再只是操作車輛而已,還可以藉由電子輔助系統的幫忙提醒駕駛人。從最一開始的倒車雷達,到現在有很多為了安全著想的新技術被應用在車上。避撞系統能夠使駕駛保持與前方車輛一定的安全距離,這對避免碰撞危險來說是很重要的第一步。此避撞系統隨時監控前方的狀況,能夠在第一時間保障駕駛的安全,因此應用此系統的環境被設定為高速公路。此系統能夠使用很多感測器來完成,但低成本的視覺系統很值得搭配此避撞系統,更可以結合行車紀錄器,達到單一設備卻有多重功能的高使用率。本研究使用雙視覺來完成此系統,分成影像前處理、車輛搜尋、距離估測和避撞判斷等四個部分。當有危險發生,駕駛只會被警示,而方向盤的操作依然由駕駛來掌握,此系統並不會強制操作方向盤。駕駛自己來排除安全距離不足的狀況會更比系統強制操作還要來的安全。

    The vehicle is not only operated by driver but also assisted by electronic device to enhance drive awareness. Beginning with backward parking radar, the new technology is extended to safety assistance system. The collision avoidance system is important for driver to avoid the danger by keeping safety distance. The system inspects the situation on the road in any time to guarantee security of driving. The applied environment is set on the high way. It can be implemented by many sensors. Low cost vision device is worthy of applying on vehicle combining with driving recorder. This study uses dual vision acquisition to achieve the collision avoidance requirements. The proposed system contains image processing, vehicle searching, distance estimation and collision avoidance. The driver is just alerted on computer screen and still controlled the steering. It is safer than that the steering of vehicle is involved by the electronic device.

    Abstract I 摘要 II 誌謝 III List of Figures VI List of Tables VIII Chapter 1 Introduction 1 1-1 Motivation 1 1-2 Literature survey 2 1-3 Goals 4 1-4 Strategy 5 1-5 Thesis Outline 7 Chapter 2 Image process 8 2-1 Gray level and Blurring 8 2-2 Image segment 10 2-3 Threshold 11 2-4 Edge detection 12 2-5 Remark 13 Chapter 3 Vehicle searching 14 3-1 Vehicle generation 14 3-2 Vehicle verification 19 3-2.1 Feature extraction 19 3-2.2 Classification 25 3-2.3 Train data collection 27 3-3 Remark 28 Chapter 4 Distance estimation 30 4-1 Template match 30 4-2 Distance calculation 33 4-3 Remark 36 Chapter 5 Collision Avoidance 37 5-1 Car speed reading 37 5-2 Safety alert 39 5-3 Remark 40 Chapter 6 Results and Discussions 41 6-1 Experiment 41 6-2 Experiment results 48 Chapter 7 Conclusion 55 References 58

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