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研究生(外文):Ting-Wei Mei
論文名稱(外文):Intelligent Vision Based Vehicle Side Collision Warning System on Highway
指導教授(外文):Chin-Teng Lin
外文關鍵詞:blind spotcomputer visionITS
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近年來,由於車輛數目的快速成長而帶來愈來愈嚴重的交通問題,因此,世界各地皆投入了龐大的人力及物力在智慧型運輸系統( ITS )的研究上;而智慧型運輸系統最主要的研究目的就是為了發展更先進的科技來改善及提昇交通運輸系統的安全和效率。
智慧型運輸系統所涵蓋的範圍很廣泛,而先進車輛的控制和安全系統( AVCSS )是其中非常重要的一個環節;先進車輛控制和安全系統發展的主要目的,就是在避免駕駛者避免於駕駛時分心而導致車禍的發生。為了達到這個目的,車輛本身就必須具備智慧型感測的能力,因此,在先進車輛控制和安全系統的研究中,研究人員通常利用了各種的感測器來實現感測的功能。
通常駕駛者要變換車道時,常利用的是以車側後視鏡中的影像資訊做為判斷的依據,但由於駕駛者自身的疏忽和車側視覺死角區域的存在,側撞及擦撞是最容易發生的車禍類型之一,為了要避免這類型的意外發生,我們便想要發展出一套能自動偵測車側視覺死角區域是否有車輛存在的系統;在此系統中,我們利用攝影機做為偵測用的感測器,首先,會利用攝影機中的資訊去尋找路面邊線的特徵,以此定義出車側視覺死角的偵測範圍,之後便是對偵測範圍判斷是否有其它車輛存在的可能性和可能的位置,找出位置後,便利用演算法判斷出車輛與雜訊( 如:影子、道路間隙…等 ),若系統偵測出有其它車輛的存在,系統便會發出警示以提示駕駛者注意車側視覺死角區域。
In recent years, Intelligent Transportation System ( ITS ) has been researched all over the world. Because of the high growth of popularization of vehicles, ITS has become an important scope research and industrial development in order to apply advanced technologies to improve the efficiency and safety of the transportation system.
ITS consists of a lot of sections, and AVCSS (Advanced Vehicle Control and Safety System) is a part of these sections. The purpose of this system is to assist to prevent the traffic accidents resulted from the negligence of drivers. In order to achieve this goal, researchers make use many kinds of sensors.
Rearview mirror is usually used to assist the driver to determine if it is safe for lane-change maneuvers. But side collision accident might still happen because of the existence of the blind spot and the negligence of the driver. In order to prevent the occurrence of side collision accident resulted from the above situation, we develop a algorithm to detect objects at a moving vehicle. In our system, we use the camera as the sensor to detect if there is any other vehicle in the blind spot. At first, we make use of the feature of road to define the region of interesting (ROI ). Then, we find the area where other vehicles may exist in ROI. Furthermore, our algorithm will judge that the object is a vehicle or a noise (such as: shadow, gap, etc). Finally, if our system detects other vehicles in the ROI, the system will give some alarm to warn the driver that lane-changing is not allowed.
中文摘要 ii
英文摘要 iii
中文誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
1 第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 3
1.3 論文架構 4
2 第二章 相關研究 5
2.1 道路線偵測 6
2.2 車輛偵測 6
3 第三章 車側視覺死角定義 8
3.1 車側視覺死角所造成的問題 8
3.2 造成車側視覺死角的原因 9
3.2.1 人類視覺的限制 9
3.2.2 車側後照鏡的限制 10
3.3 系統中車側視覺死角的定義 12
3.3.1 道路線偵測及追蹤模組流程 13
3.3.2 彩色空間轉換 14
3.3.3 中位數濾波器 15
3.3.4 天候狀態決定 15
3.3.5 道路線參考點 21
3.3.6 Hough Transform 24
3.3.7 道路線追蹤 25
4 第四章 車側視覺死角中的車輛偵測 26
4.1 車輛偵測模組流程 26
4.2 影像前處理 27
4.3 分離出偵測區域中的物體 31
4.3.1 Lane Based Transform 31
4.3.2 修補斷邊 33
4.3.3 Connected Component 33
4.3.4 尺寸濾波器與障礙物列表 34
4.4 確認物體 35
4.4.1 物體的寬度確認 36
4.4.2 物體的定位 36
4.4.3 物體的高度確認 38
4.4.4 物體的存在時間確認 38
4.5 追蹤偵測區域中的物體存在性 39
5 第五章 實驗結果 40
5.1 實驗設備及設計 40
5.2 道路線偵測及追蹤之結果 41
�P 道路線偵測結果 41
�P 道路線追蹤結果 43
5.3 車輛偵測及追蹤的結果 46
�P 車輛偵測的結果 46
�P 車輛追蹤的結果 49
5.4 系統的處理時間 54
6 第六章 結論與未來展望 55
6.1 結論 55
6.2 未來展望 56
參考文獻 57
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