在台灣，每年有將近兩千五百人死於交通事故中，其中，肇事時間發生在晚上的比例為53%，反映出夜間駕駛的危險性。此外，最主要的肇事原因為”不適當的駕駛行為”，疲勞駕車或是手持行動電話等行為都屬於此範疇之內。有鑒於此，本論文係發展一套以夜間電腦視覺技術為基礎之駕駛輔助系統，當汽車偏離車道或是未與前車保持安全距離，則系統將給予警告。而本系統藉由車道偵測與汽車辨識來避免這兩種主要危害，以確保駕駛員於夜間行車的安全。
車道偵側方面，利用道路標線在影像中的特性來偵測出駕駛員兩旁的車道線。汽車辨識方面，藉由擷取影像中汽車車尾燈的位置，進一步地配對成前車候選者，加上先前所偵側的車道邊界，便可篩選出主要車道上前導車。最後，於危害判斷方面，引入時間軸的觀念，計算出車道偏移或是與前車碰撞的時間，即可推算出駕駛員的危害程度，以阻止危害的發生，減少不必要的意外。
除此之外，本論文提出以消失點偵測為基礎之相機校正，透過影像處理的技術來估測相機內部的校正參數。本系統於夜間正常天候下汽車辨識率為91%，而車道偵測率更可高達99%，證明了本系統的可靠性。此外，每秒25張的處理速度滿足及時系統的需求，也提高了本系統的實用性。In Taiwan, more than 2,500 people die in the fatal traffic accidents per year, of which 53% traffic accidents happen in the nighttime. Besides, the major cause of traffic accidents is “Improper Driving” due to driver’s inattention or fatigue. For this reason, we develop a vision based driver assistance system which has capabilities of lane departure prevention and collision avoidance at night.
The objectives of this paper are to detect the lane boundaries and vehicles by use of computer vision techniques. In lane recognition, three procedures including Gaussian filter, Peak-Finding Algorithm, and Line-Segment Grouping, based on three properties, brightness, slenderness, and continuity, are used to detect land markers successfully and effectively. In vehicle recognition, taillight features are first stood out and the proposed taillight pairing algorithm is used to search vehicle candidates effectively. Besides, in this paper, we also provide an automatic method to calculate the tilt and the pan of the camera according to the position of vanishing point in the image.
The proposed system is shown to work well on highway in the nighttime. The detection rate in lane detection is nearly 95%, and vehicle recognition is higher than 87%. Besides, the computation cost of our approach is low and our system can process the image in almost real time.Chapter 1 Introduction 1
1.1 Motivation 1
1.1.1 Statistics on Traffic Accidents 1
1.1.2 Sensing Device Selection 3
1.2 Related Works 5
1.3 Objectives 7
1.4 System Overview 8
1.5 Thesis Organization 9
Chapter 2 Camera Calibration 10
2.1 Location Problem 10
2.1.1 Overview 10
2.1.2 Camera Configuration 11
2.1.3 Transformation Formulation 14
2.2 Vanishing Point 17
2.2.1 Applications of the Vanishing Point 17
2.2.2 Relation between Vanishing Point and Camera Calibration 18
2.3 Vanishing Point Detection Algorithm 20
2.3.1 Overview 20
2.3.2 Canny Edge Detection 22
2.3.3 Vanishing Point Localization 23
2.4 Summary 29
Chapter 3 Lane Detection 31
3.1 Overview 31
3.2 Lane Detection Procedure 33
3.3 Lane Marker Detection 34
3.3.1 Lane Marker Properties 35
3.3.2 Noise Removal – Gaussian Filter 36
3.3.3 Feature Extraction – Peak Finding Algorithm 37
3.3.4 Feature Point Grouping Algorithm 40
3.4 Lane Boundaries Construction 42
3.4.1 Line Segment Combination Algorithm 42
3.4.2 Lane Boundary Selection Algorithm 46
3.5 Summary 48
Chapter 4 Vehicle Recognition 49
4.1 Overview 49
4.2 Vehicle Recognition Procedure 50
4.3 Taillight Extraction 51
4.3.1 Taillight Properties at Night 52
4.3.2 Taillight Pixel Standing-Out 52
4.3.3 Taillight Pixel Filtering 54
4.4 Vehicle Candidate Localization 56
4.4.1 Taillight Centroid Detection 56
4.4.2 Constraints of Front-Vehicle 57
4.4.3 Taillight Paring Algorithm 59
4.5 Summary 61
Chapter 5 Hazard Identification 63
5.1 Overview 63
5.2 Preceding Vehicle Verification 64
5.3 Lane Departure Prevention Mechanism 65
5.4 Collision Avoidance Mechanism 67
Chapter 6 Experiment 71
6.1 Environment Description 71
6.2 Camera Calibration 73
6.3 Lane Detection 76
6.4 Vehicle Recognition 78
6.5 System Performance 80
6.5.1 Detection Rate of System 80
6.5.2 Processing Time of System 82
Chapter 7 Conclusion 84
Reference 8
