臺灣博碩士論文加值系統

隨著自動駕駛技術的發展，眾多車廠開始投入自駕技術的研發，並積極尋求更高等級的自動化。同時在自駕技術中車道變換系統以及同時定位與地圖建構(Simultaneous Localization and Mapping, SLAM)技術在自駕技術中也扮演著至關重要的角色。前者是使實驗車輛能夠準確獲取周圍環境訊息，並判斷是否適合進行車道變換，並確保車輛能夠準確地沿著變道路徑行駛，進而安全地執行該操作。而後者則是為實驗車輛提供了準確的定位和地圖構建的功能，使車輛能夠更好地感知周圍環境，從而更加可靠地執行車道變換等操作。
本論文實驗主要在已知場域進行實驗，強調了基於模糊邏輯車道變換決策系統(Fuzzy Lane Change Decision System, FLCDS)和基於模糊邏輯航點追蹤系統(Fuzzy Waypoints Tracking System, FWTS)的設計與功能，並使這些系統能夠提高航點追蹤的準確性和能夠安全的車道變換。動態換道軌跡生成技術也是本論文的重點，確保了車輛在多變的駕駛環境中能夠安全地進行車道變換，也探討了模糊邏輯控制方法在自動駕駛中的應用，特別針對車道變換決策系統和航點追蹤系統。FLCDS則整合了來自3D和2D LiDAR感測器的高精度距離數據以及Realsense D435i深度攝影機的深度數據。該系統透過模糊邏輯控制器處理這些輸入數據，以做出安全有效的車道變換系統。FWTS設計是使用3D LiDAR數據用來確保車輛能夠準確地跟隨航點，從而防止偏離預定路徑。

The experiments in this manuscript are primarily conducted in a known environment. With the continuous development of autonomous driving technology, many car manufacturers have begun investing in the research and development of self-driving technologies, actively seeking higher levels of automation. In autonomous driving, lane change systems and Simultaneous Localization and Mapping (SLAM) technology play crucial roles. The former enables experimental vehicles to accurately acquire surrounding environmental information, determine the suitability of lane changes, and ensure that the vehicle can precisely follow the changed lane path to safely execute the maneuver. The latter provides precise positioning and map construction capabilities, allowing the vehicle to better perceive the surrounding environment and thereby perform lane changes and other operations more reliably.
This manuscript emphasizes the design and functionality of the Fuzzy Lane Change Decision System (FLCDS) and the Fuzzy Waypoints System (FWTS), which enhance waypoint tracking accuracy and enable safe lane changes. Dynamic lane change trajectory generation technology is also a key focus of this paper, ensuring that vehicles can safely perform lane changes in variable driving environments. The paper explores the application of fuzzy logic control methods in autonomous driving, particularly for lane change decision systems and waypoint tracking systems. FLCDS integrates high-precision distance data from 3D and 2D LiDAR sensors and depth data from the Realsense D435i camera. This system processes these input data through a fuzzy logic controller to make safe and effective lane change decisions. FWTS is designed using 3D LiDAR data to ensure that the vehicle can accurately follow waypoints, thus preventing deviation from the planned route.

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
第二章 相關研究...4
2.1 自動駕駛技術...4
2.2 車道變換系統...4
2.3 SLAM技術...5
2.4 動態換道軌跡規劃...6
2.5 模糊規則...6
第三章 基於SLAM技術車道變換的軌跡生成...8
3.1 SLAM技術[19]...8
3.2 基於原軌跡動態換道軌跡規劃...10
3.3 變道後的車道軌跡生成...11
第四章 使用模糊控制於車道變換...14
4.1 模糊邏輯控制器之架構...16
4.2 基於模糊邏輯車道變換決策系統...17
4.2.1 模糊化(fuzzifier)...19
4.2.2 模糊規則庫(fuzzy rule base)...22
4.2.3 解模糊化(defuzzifier)...23
4.3 基於模糊邏輯航點追蹤系統...26
4.3.1 模糊化(fuzzifier)...27
4.3.2 模糊規則庫(fuzzy rule base)...29
4.3.3 解模糊化(defuzzifier)...29
第五章 實驗結果與分析...30
5.1 實驗環境與硬體配置...30
5.2 實驗結果與性能驗證...33
5.2.1 實驗一：遇前方慢車實現車道變換超車後並回到原車道...34
5.2.1.1 實驗一之基於模糊邏輯車道變換決策系統(FLCDS)...34
5.2.1.2 實驗一之基於模糊邏輯航點追蹤系統(FWTS)...36
5.2.2 實驗二：遇到前方慢車實現車道變換後等待變道安全距離...37
5.2.2.1 實驗二之基於模糊邏輯車道變換決策系統(FLCDS)...37
5.2.2.2 實驗二之基於模糊邏輯航點追蹤系統(FWTS)...39
5.2.3 實驗三：遇前方慢車與左後車實現車道變換後慢車加速...40
5.2.3.1 實驗三之基於模糊邏輯車道變換決策系統(FLCDS)...40
5.2.3.2 實驗三之基於模糊邏輯航點追蹤系統(FWTS)...42
5.2.4 實驗控制器比較...43
5.2.4.1 直線實驗之控制器行駛路線比較分析...43
5.2.4.2 實驗一之控制器行駛路線比較分析...44
第六章 結論與未來展望...45
6.1 結論...45
6.2 未來展望...46
Extended Abstract...50
Chapter 1 Introduction...51
Chapter 2 Related work...52
Chapter 3 Trajectory Generation for Lane Changes Based on SLAM Technology...53
Chapter 4 Application of Fuzzy Control Method in Autonomous Driving...53
Chapter 5 Experimental Results and Analysis...54
Chapter 6 Conclusion and Future Prospects...54

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