臺灣博碩士論文加值系統

本論文提出一個駕駛狀況分析系統，系統架構是利用兩台固定位置的攝影機，其中一台擷取駕駛者影像，以分析駕駛者視線，另一台則用於擷取前方道路影像，進行行車型態的分析。在駕駛者影像方面，先利用AdaBoost演算法偵測出人臉，然後取得眼睛、鼻子、嘴唇之位置，決定轉頭角度與視線方向。在道路影像方面，使用了邊緣偵測、反透視轉換、霍夫轉換等偵測道路線，繼而預測車道中線位置，然後應用適應性網路模糊推論系統分析車道中線，判斷行車型態。最後結合駕駛者視線與行車型態兩項資訊，判斷駕駛狀況是否安全。

This thesis proposes an intelligent system which analyzes driving status. The system infrastructure is utilized two fixed cameras on the host vehicle. One is used to capture driver’s image in order to analyze driver’s sight line, and the other is used to capture image of road ahead for analyzing driving pattern. In the section of driver’s image, it’s necessary to utilize AdaBoost algorism to recognize face and then get the positions of eyes, nose and lips to diagnose the angles of driver’s head and driver’s sight line. In the section of road image, it’s used edge detection, inverse perspective transform and Hough transform to get lane line. Next is to analyze the mid-line of lane by adaptive network-based fuzzy inference system (ANFIS) for driving pattern recognition. The last step is to combine both driver’s sight line and driving pattern to judge if it’s safe driving.

指導教授推薦書
口試委員會審定書
授權書………………………………………………………………… iii
誌謝…………………………………………………………………… iv
中文摘要……………………………………………………………… v
英文摘要……………………………………………………………… vi
目錄…………………………………………………………………… vii
圖目錄………………………………………………………………… x
表目錄………………………………………………………………… xiv
第一章　緒論 ………………………………………………………… 1
　　1.1　研究動機…………………………………………………… 1
　　1.2　文獻回顧…………………………………………………… 1
　　1.3　論文架構…………………………………………………… 3
第二章　影像分析 …………………………………………………… 4
　　2.1　駕駛者視線偵測…………………………………………… 4
　　　　2.1.1　人臉偵測…………………………………………… 4
　　　　2.1.2　五官定位…………………………………………… 6
　　　　2.1.3　轉頭角度與視線方向……………………………… 8
　　2.2　道路線偵測………………………………………………… 10
　　　　2.2.1　邊緣偵測…………………………………………… 10
　　　　2.2.2　反透視轉換………………………………………… 11
　　　　2.2.3　型態學影像處理…………………………………… 12
　　　　2.2.4　霍夫轉換…………………………………………… 13
　　　　2.2.5　車道中線預測……………………………………… 14
第三章　適應性網路模糊推論系統簡介 …………………………… 16
　　3.1　模糊系統…………………………………………………… 16
　　　　3.1.1　模糊化機構………………………………………… 17
　　　　3.1.2　模糊規則庫………………………………………… 19
　　　　3.1.3　模糊推論引擎……………………………………… 20
　　　　3.1.4　解模糊化機構……………………………………… 21
　　3.2　類神經網路………………………………………………… 22
　　　　3.2.1　感知機……………………………………………… 22
　　　　3.2.2　倒傳遞類神經網路………………………………… 23
　　3.3　適應性網路模糊推論系統………………………………… 24
　　　　3.3.1　ANFIS架構………………………………………… 24
　　　　3.3.2　ANFIS參數學習…………………………………… 26
第四章　行車型態辨識與駕駛安全性判斷 ………………………… 27
　　4.1　行車型態辨識……………………………………………… 27
　　　　4.1.1　常見的行車型態…………………………………… 28
　　　　4.1.2　以ANFIS訓練之結構……………………………… 31
　　　　4.1.3　行車型態分類方式………………………………… 34
　　4.2　駕駛安全性判斷…………………………………………… 39
　　　　4.2.1　利用彈性形狀脈絡相似度量測…………………… 39
　　　　4.2.2　相似度量測結合模糊系統………………………… 41
　　　　4.2.3　結合駕駛者視線與行車型態……………………… 47
第五章　實驗結果及分析 …………………………………………… 49
　　5.1　系統設置…………………………………………………… 49
　　5.2　駕駛者視線偵測結果……………………………………… 50
　　　　5.2.1　五官位置偵測結果………………………………… 50
　　　　5.2.2　視線偵測結果……………………………………… 51
　　5.3　道路線偵測結果…………………………………………… 53
　　5.4　行車型態辨識結果………………………………………… 58
　　5.5　駕駛安全性判斷結果……………………………………… 62
　　　　5.5.1　利用彈性形狀脈絡相似度量測之結果…………… 62
　　　　5.5.2　相似度量測結合模糊系統之結果………………… 63
　　　　5.5.3　結合駕駛者視線與行車型態之結果……………… 65
第六章　結論與未來展望 …………………………………………… 67
　　6.1　結論………………………………………………………… 67
　　6.2　未來展望…………………………………………………… 67
參考文獻 ……………………………………………………………… 68

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