隨著家用汽車的普及，各大車廠開始研發和探索自動駕駛功能。目前市場上的大多數自動駕駛輔助系統(ADAS, Advanced driver-assistance system)仍需要人為輔助操作，無法實現完全自動駕駛。因此，如何開發實現完全自動駕駛的技術成為當前熱門的議題。在完全自動駕駛環境中，主動車距調節系統和車道維持系統尤其重要。前者能夠與前方車輛保持適當的距離，在前方無障礙物時實現定速行駛；後者能夠使車輛保持在車道中心，避免偏離車道引發事故。
本研究提出了基於模糊邏輯的主動車距調節系統（Fuzzy Adaptive Cruise Control, FACC）和基於模糊邏輯的車道維持系統（Fuzzy Lane Keeping Assist System, FLKAS），以實現上述兩個目標。主動車距調節系統利用車距、速度誤差和加速度作為控制器的輸入，通過比例控制方式調整油門，以實現舒適和安全的駕駛。車道維持系統則利用影像辨識技術檢測左右車道線，並與車道中心的距離進行比較，通過模糊控制器控制方向盤轉向，實現車道維持。實驗結果表明，該控制系統能夠有效地保持車輛與前車的距離，在前方無障礙物時實現定速行駛，並能夠將車輛有效地保持在車道中心。同時，該控制系統能夠即時對突發狀況進行修正，避免危險事件的發生。

With the widespread use of household cars, major car manufacturers have started to develop and explore autonomous driving capabilities. Currently, most of the Advanced Driver-Assistance Systems (ADAS) in the market still require human intervention and cannot achieve fully autonomous driving. Therefore, the development of technology to achieve complete autonomous driving has become a hot topic. In a fully autonomous driving environment, the Adaptive Cruise Control (ACC) system and Lane Keeping Assist System (LKAS) are particularly important. The former allows for maintaining an appropriate distance from the preceding vehicle and achieving a constant speed when there are no obstacles ahead, while the latter enables the vehicle to stay in the center of the lane and avoid accidents caused by lane departure.

This study proposes a Fuzzy Adaptive Cruise Control (FACC) system and a Fuzzy Lane Keeping Assist System (FLKAS) based on fuzzy logic to achieve the aforementioned goals. The FACC system utilizes parameters such as vehicle distance, speed error, and acceleration as inputs to the controller. By using proportional control, the throttle is adjusted to achieve comfortable and safe driving. The FLKAS system, on the other hand, utilizes image recognition technology to detect the left and right lane markings and compares the distance to the center of the lane. Through a fuzzy controller, it controls the steering wheel to maintain the lane. Experimental results demonstrate that this control system can effectively maintain the distance between vehicles, achieve constant speed when there are no obstacles ahead, and keep the vehicle effectively in the center of the lane. Additionally, the control system can make real-time adjustments to unexpected situations, preventing the occurrence of dangerous events.

摘要...i
Abstract...ii
誌謝...iii
目錄...iv
表目錄...vii
圖目錄...viii
第一章 緒論...1
1.1 研究背景...1
1.2 研究動機與目的...1
1.3 文獻回顧...2
1.3.1 模糊控制...2
1.3.2 自動駕駛汽車...3
1.3.3 車距調節...3
1.3.4 車道維持...4
1.4 論文架構...4
第二章 無人自駕高爾夫球車架構...6
2.1 設備架構...6
2.1.1 整車控制系統...7
2.1.2 油門控制模組...7
2.1.3 方向盤電動助力轉向模組...8
2.1.4 車速監測模組...9
2.1.5 車距感測模組...10
2.1.6 攝影鏡頭...11
2.2 機器人作業系統...12
第三章 模糊邏輯控制器架構設計...14
3.1...基於模糊邏輯的主動式車距調節巡航系統(Fuzzy Adaptive Cruise Control, FACC)...14
3.1.1 模糊控制器結構設計...15
3.1.2 模糊化(Fuzzifier)...17
3.1.3 模糊規則庫(Fuzzy Rule Base)...20
3.1.4 解模糊化(Defuzzifier)...20
3.2 基於模糊邏輯的車道維持輔助系統(Fuzzy Lane Keeping Assist System, FLKAS)...24
3.2.1 模糊控制器結構設計...25
3.2.2 模糊化(Fuzzifier)...30
3.2.3 模糊規則庫(Fuzzy Rule Base)...32
3.2.4 解模糊化(Defuzzifier)...33
第四章 基於模糊邏輯控制的車道維持與主動式車距調節巡航系統的實現...35
4.1 系統架構與硬件設置...35
4.2 模糊控制器應用於ROS作業系統系統...36
4.3 實驗環境...37
4.4 基於模糊邏輯的主動式車距調節巡航系統的性能驗證...37
4.4.1 情境一:使車輛在定速的情況下行駛...37
4.4.2 情境二:使車輛在定速前進時突然加入雜訊以驗證模糊控制器的強韌性...39
4.4.3 情境三:使車輛跟隨前車行駛...40
4.5 基於模糊邏輯的車道維持系統的性能驗證...42
4.5.1 情境一:使車輛在偏移中心點的位置開始行駛...42
4.5.2 情境二: 使車輛再偏移中心點的位置開始行駛，並在車輛維持在中心時輸入一段雜訊觀察模糊控制器之控制結果...43
第五章 結論與未來展望...46
5.1 結論...46
5.2 未來展望...46
參考文獻...47
Extended Abstract...50

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