博碩士論文 92522035 詳細資訊




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姓名 林君威(Chun-Wei Lin)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 以3D幾何為基礎的車道偏離警示系統與車道偏離誤差分析
(3D Visual Geometry-based Lane Departure Warning System and Error Analysis on Lateral Offsets)
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摘要(中) 根據世界各國交通運輸部門的交通事故分析,有許多的死亡事故是肇因於車道偏離。為了避免這些死傷,車道偏離警示 (LDW) 系統被提出來以防止車道偏離的意外發生;且該系統也被美國及歐盟政府建議為車輛安全的基本配備。一個標準的車道偏離警示系統主要包含了兩個部份:車道線偵測與偏離距離估測。車道線偵測方法擷取出車道線的資訊,交由偏離距離估測方法估算出車輛的偏離距離,由偏離的距離資訊適時的提出警示以防止車道偏離的意外發生。
本論文描述了一個以3D 幾何為基礎的車道偏離警示系統防止駕駛因不自主的偏離車道而造成傷亡。首先,使用一個結合周邊抑制效果(lateral inhibition) 與遠近調適效應 (far-near adaptation) 的邊偵測方法計算出水平邊資訊,這兩個技術可以避免不良的天候對於邊偵測結果的影響。我們所提出的車道線偵測方法利用共軛高斯模型 (conjugate Gaussian model) 在沒有門檻值篩選的邊資訊影像中搜尋車道線特徵,共軛高斯模型同時參考了車道線的左右邊資訊,可避免不清晰的車道線、雨刷、及部分車道線被前方車輛遮蔽的影響。被偵測到的車道線會經由路面上的平行線在 3D 幾何上的限制來驗證偵測的正確性,可避免將非車道線的物件誤判為車道線;而此一幾何限制也可在因為車道線品質不佳,車道線偵測只有找到單邊車道線時,用來估算出另外一邊的車道線資訊,也就是說,即便只有單邊的車道線,此系統也能提供駕駛者車道的資訊。最後,此系統是透過 3D 幾何精確的計算出車道偏離的距離,而非粗略的從影像中的車道線關係估算,提供駕駛更為準確的車道偏離資訊。
為了執行效率,我們以直線車道模式 (straight-lane model) 偵測直線或曲線車道線。然而,直線車道模式應用在彎曲車道上的偵測結果可能會造成偏離距離估算的誤差;為了了解在不同道路彎曲的狀況下,所造成的可能誤差是否還足以提供駕駛可靠的車道偏離資訊,我們還分析了在不同道路寬度與彎曲狀況下實際的車道偏離距離與偵測的車道偏離距離誤差,這些不同狀況包含: (i) 估計車道寬的正確性, (ii) 不同水平曲度的車道, (iii) 不同垂直曲度的車道。
根據我們大量實驗影像的分析與測試,面對不同的天候狀況與路面狀況,本研究的偵測正確率可達 97.1% 以上,展示出我們所提出的方法相對於目前相關研究或產品所具有獨特性與穩定性。除此之外,根據以直線為基礎的車道線偵測技術應用於不同幾何形狀的人造曲線道路及真實影像的分析,我們發現在正常行車環境下的偵測誤差不甚顯著;在高速公路環境中,最大水平曲度的車道上最大誤差達 7.1 公分、最大垂直曲度的車道上最大誤差達 13.7 公分。然而,此最大誤差發生於跨越車道時的外側車道線,此時所跨越的內側車道線方是駕駛者所依靠的車道線資訊,此內側車道線與一般行駛環境下的車道線在水平曲度車道上最大誤差僅有 3.8 公分、在垂直曲度車道上最大誤差僅有 2.2 公分。也就是說,以直線為基礎的車道偵測技術應用於曲線道路除了可以增加計算效率,所偵測到的車道線資訊也足以提供駕駛可靠的車道偏離資訊。
摘要(英) In recent years, there were lots of deaths caused by lane departure of traffic accidents. To prevent these fatalities, lane departure warning (LDW) systems have been proposed to protect drivers from the departure accidents. A standard LDW system should include two major parts: lane-mark detection and departure measurement. The lane-mark detection extracts lane marks, and then the measurement module acquires the lateral offset of the vehicle to adaptively prevent the lane departure.
In this dissertation, we present a versatile lane departure warning system based on three-dimensional (3D) visual geometry that would help drivers avoid unintended departure from their lane during land vehicle driving. A horizontal gradient map was first calculated by an edge operator combined with the properties of lateral inhibition and far-near adaptation so that the operation would be less influenced by bad weather conditions. The lane marks were then detected with the proposed conjugate Gaussian model on the non-thresholded gradient map to make the detection more stable and less influenced by shadow boundary, windshield wipers, and the partial occlusion of other vehicles. The detected lane marks were then verified by the 3D geometric constraints of the parallel lines on the road surface to avoid the wrong detection of non-lane marks; moreover, the constraints were also used to find the other-side lane mark from the extracted one if only one lane mark was extracted on low-quality road surfaces. This means that the lane can be detected even if only one lane mark is detected. Lastly, the lane offset distance was accurately calculated from the 3D geometry of the lane marks rather than roughly estimated from the two-dimensional relationship of the lane marks on images.
For efficiency, straight-line-based lane detections were generally utilized to detect straight or curved lane marks. However, using straight line to approach curved line may results in error and the error is influenced by variant situations. Here we want to know whether the straight-line-based LDW system is sufficient to work for various road situations. In this dissertation, we analyze the errors of the estimated lateral offsets which were generated by a straight lane model in three different kinds of road situations: (i) the accuracy of the estimated lane width, (ii) the horizontal curvature of the lane marks, and (iii) the vertical curvature of the lane marks.
Based on our analyses, the properties of the proposed system are special and unique and are not present in the existing systems. Based on various images of weather conditions and road surface situations, the detection rate reaches over 97.1%. This study demonstrates the effect and efficiency of the proposed system and compares it with other existing systems. Besides, according to the analysis on various geometric-shaped artificial curved lanes and the experiments on real images based on our experimental platforms, we find that most errors of estimated lateral offset are insignificant in general driving situation; on highway, the maximum errors reach 7.1 cm of horizontal curved lane and 13.7 cm of vertical curved lanes on highway. However, those maximum errors occur on the far lane mark when crossing lane. In that moment, the lateral offset is estimated by the lane mark which driver crossing. The maximum errors in general driving situation, includes the lane mark which driver crossing when changing lane, are only 3.8 cm of horizontal curved lane and only 2.2 cm of vertical curved lanes. That is, the straight-line-based lane departure warning systems are reliable to provide drivers the lane departure warning information whenever driving on straight or curved lanes.
關鍵字(中) ★ 車道線偵測
★ 道路偏離警示
★ 駕駛輔助
★ 智慧型車輛
★ 電腦視覺
關鍵字(英) ★ Lane-mark detection
★ Lane departure warning
★ Driving assistance
★ Advanced safety vehicle
★ Computer vision
論文目次 中文摘要 ........................................................................................................ i
Abstract ....................................................................................................... iii
誌謝 ............................................................................................................... v
Contents ....................................................................................................... vi
List of Figures ........................................................................................... viii
List of Tables .............................................................................................. xii
Chapter 1 Introduction ............................................................................... 1
1.1 Motivation .......................................................................................... 1
1.2 Overview of the study ........................................................................ 2
1.3 Organization of this dissertation ........................................................ 3
Chapter 2 The Related Works .................................................................... 4
Chapter 3 Lane-mark Detection ................................................................ 8
3.1 The lateral inhibition property ........................................................... 8
3.2 Adjustment of far-near edge weights ................................................. 9
3.3 Lane detection based on the conjugate Gaussian model ................. 10
Chapter 4 Lane Verification ..................................................................... 16
4.1 Three-dimensional geometric verification ....................................... 16
4.1.1 Judgment on the relation between intercepts and slope angles
.................................................................................................... 17
4.1.2 Judgment on the lane width ................................................... 17
4.2 The rectification of wrong detection ................................................ 20
Chapter 5 Lane-departure Warning ....................................................... 23
5.1 The lateral offset estimation ............................................................ 23
5.2 The estimation of time to lane crossing ........................................... 25
5.3 The warning by combining lateral offset and TLC .......................... 26
Chapter 6 Error Analysis for Lateral Offsets ......................................... 27
6.1 Error from the non-accurate estimated lane width .......................... 27
6.2 Error from horizontal curved lanes .................................................. 31
6.3 Error from vertical curved lanes ...................................................... 37
6.4 Error from simultaneously horizontal and vertical curved lanes .... 42
Chapter 7 Experiments ............................................................................. 44
7.1 Effect of lateral inhibition ............................................................... 45
7.2 Effect of the conjugate Gaussian model .......................................... 49
7.3 Comparison of the lane-mark detection between using two
independent and a single standard derivations in the conjugate
Gaussian models .............................................................................. 51
7.4 Effect of the three-dimensional geometric constraints for lane
verification ...................................................................................... 53
7.5 The performance of lane verification and rectification ................... 53
7.6 Comparison of methods ................................................................... 55
7.7 Verification of error analysis on error from the non-accurate
estimated lane width ........................................................................ 58
7.8 Verification of the built artificial driving environments ................. 59
Chapter 8 Conclusions .............................................................................. 62
References .................................................................................................. 64
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指導教授 曾定章 審核日期 2013-7-25
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