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研究生:郭容平
研究生(外文):Kuo, Jung-Ping
論文名稱:結合車道偏移與前車防撞警示之強健車行輔助系統
論文名稱(外文):A Robust Driving Assistance System with Combined Lane Departure and Front Collision Warning Technologies
指導教授:楊家輝楊家輝引用關係
指導教授(外文):Yang, Jar-Ferr
口試委員:胡敏君吳宗憲邱瀞德張嘉熒
口試委員(外文):Min-Chun HuChung-Hsien WuChing-Te ChiuChia-Ying Chang
口試日期:2017-07-17
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:49
中文關鍵詞:先進輔助駕駛系統車道偵測前車偵測條件隨機域偵測方法車輛追蹤
外文關鍵詞:ADASLane DetectionVehicle DetectionCRF detectionTracking System
相關次數:
  • 被引用被引用:0
  • 點閱點閱:27
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
為防止駕駛分心以致於在高速公路上發生事故,先進輔助駕駛系統開發已經被廣泛地研究。本論文結合車道偏移系統與前車防碰撞系統,我們提出了一套車行輔助系統。相較於霍夫轉換法,條件隨機域車道偵測方法能用於彎道與直線車道的偵測,但會產生很多誤報。因此,我們首先提出了一套車道線追蹤的方式使得偵測的結果更加穩定並且保有偵測彎道車道的特性。另外,本論文亦提出強健車輛偵測,我們利用方向梯度直方圖特徵截取與支持向量機分類器做為車輛偵測的方法,並加入追蹤系統使得偵測結果更加穩定。最後,結合車道偵測與車輛追蹤的結果,我們可以過濾掉不屬於駕駛車道的車輛,並計算與前車相距的距離。當系統發現到車輛過於靠近前車或是偏離駕駛中的車道時,它會提醒駕駛人。首先是比較所提出的車道偵測方法與其他兩種方法比較,實驗結果證明其有較高的辨識率與較低的誤報率。對於過濾掉不屬於行駛車道並整合橋影偵測,實驗證明有車輛追蹤的偵測結果可有效地提升辨識率。本論文提出的輔助駕駛系統於在高速公路將可有效提升汽車駕駛之安全係數。
To avoid driver distraction, which might cause accidents in the highway, the development of advanced driver assistance system (ADAS) has been widely studied. In this thesis, we proposed a driver assistance system, which combines lane departure warming system (LDWS) and forward collision warming system (FCWS). Comparing to Hough transform, the conditional random field (CRF) lane detection method can detect both straight and curve lanes but with a lot of false alarms. As a result, we proposed an effective lane tracking system to make the detection more stable while preserving the ability of curve lane detection. For front vehicle detection, we use histogram of oriented gradient (HOG) feature extraction and support vector machine (SVM) classifier to detect the vehicle while the tracking system makes the detection more robust. Finally, we combine the tracked vehicle and lane detection results, we can further filter out the off-lane vehicles and estimate the distance between the in-lane front vehicle and driving car. When the system detects the distance is too close to the front in-lane vehicle or finds departure of the driving lane, the system will notify the driver. The proposed lane detection is first compared to two existed methods. The experimental results show that the proposed system has higher detection rate and lower false alarm rate. With filtering out off-lane vehicles and bridge shadow detection, the proposed system can effectively increase the detection rate by vehicle tracking. In this thesis, the proposed driver assistance can effectively improve the safety of car driving in the highway.
摘 要 I
Abstract II
誌 謝 III
Contents IV
List of Tables VI
List of Figures VII
Chapter 1: Introduction 1
1.1 Research Background 1
1.2 Motivations 2
1.3 Literature Reviews 3
1.4 Organization of Thesis 5
Chapter 2: Related Work 7
2.1 Conditional Random Field for Lane Detection 7
2.2 Histogram of Oriented Gradients 10
2.3 Support Vector Machine 12
2.4 Kalman Filter 14
Chapter 3: The Proposed Robust Driving Assistant System 16
3.1 Overview Whole System 16
3.2 Lane Departure Warning System 17
3.2.1 CRF Model for Lane Detection 18
3.2.3 Lane Tracking 19
3.2.4 Curve Lane Detection 22
3.3 Forward Collision Warning System 24
3.3.1 ROI Extraction 25
3.3.2 Vehicle Detection and Tracking 26
3.3.3 Driving Lane Filter 29
3.3.4 Distance Estimation 30

Chapter 4: Experimental Results 33
4.1 The Environment of Experiment 33
4.2 Comparisons of Different Lane Detection Methods 34
4.3 Bridge Detection Testing 39
4.4 Vehicle Tracking Testing 40
4.5 Fusion Version Testing 41
Chapter 5: Conclusions 43
Chapter 6: Future Work 44
References 45
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