臺灣博碩士論文加值系統

本文提出一種基於初始點位置和自適應卡爾曼預測路徑融合基因演算法(Genetic algorithm) 組成的車道路線追蹤演算法。為了降低計算成本，採用影像特徵擷取並選擇待處理區域的感興趣區域 (Region of Interest) 來分割車輛前進路徑周圍的左側和右側道路線和路面，接著預測初始位置以及後續的座標點位置。大多數情況下，道路線是直線或彎曲的，因此可利用卡爾曼濾波器來預測下一刻道路線的位置，使車輛能夠平穩前進。
由於一般卡爾曼濾波器在一開始無法確定參數值，導致系統的不確定性，如狀態雜訊 (Process noise) 和量測雜訊 (Measurement noise) 必須設定得足夠準確，否則將導致卡爾曼濾波器對系統狀態的初始估計出現重大誤差，無法實現最佳效能。因此本文採用自適應卡爾曼濾波器來動態調整狀態雜訊和量測雜訊，以提高卡爾曼濾波器參數的精確度，並增強卡爾曼濾波器在預測過程中的性能。除了狀態雜訊和量測雜訊之外，卡爾曼增益 (Kalman filter gain) 也對動態狀態的初始估計有明顯的誤差影響，因此本文採用基因演算法針對預測更新座標與實際座標計算誤差，將計算出來符合當前狀態估計的結果作為卡爾曼增益的初始值來改善卡爾曼濾波器的效能。
從實驗測試結果得知，本文提出的方法能夠在道路線缺失或座標點判斷錯誤的情況下預測車道道路線且呈現優秀的性能，且結果數據表明使用插值基因演算法的自適應卡爾曼濾波器方法相較於只使用自適應卡爾曼濾波器方法在左側道路線上的均方誤差降低54.5276倍；在右側道路線上的均方誤差降低了72.7803倍。

This thesis proposes a lane-tracking method based on a Genetic algorithm and Kalman filter. To reduce the computational cost, this thesis uses image feature extraction and selects the ROI of the area to be processed to segment the left and right side road lines and surfaces around the vehicle's forward path. Then, the initial position is predicted. In most cases, road lines are straight or curved, so the Kalman filter, a linear system, is used to predict the position of the road line at the next moment, allowing the vehicle to advance steadily.
However, the process noise and measurement noise must be set accurately enough; otherwise, they will lead to significant errors in the Kalman filter's initial estimation of dynamic states, thereby failing to achieve optimal performance. Therefore, this thesis utilizes an adaptive Kalman filter to dynamically adjust the process noise and measurement noise to improve the Kalman filter parameters' precision and enhance the Kalman filter's performance during the prediction process. In addition to the process noise and measurement noise, the Kalman filter gain also significantly affects the Kalman filter's initial estimation of dynamic states. Thus, this thesis employs a genetic algorithm to calculate the error between the predicted updated coordinates and actual coordinates. The optima results that match the current state estimation are used as the initial value of the Kalman gain to improve the Kalman filter's performance.
The experimental results show that the proposed method can predict lane road lines with excellent performance, even in cases of missing road lines or incorrect coordinate point judgment. The results indicate that the proposed adaptive Kalman filter method using the interpolated genetic algorithm reduced the mean square error on the left road line by 54.5276 times and on the right road line by 72.7803 times compared to using only the adaptive Kalman filter method.

摘 要 i
ABSTRACT iii
誌 謝 v
目 錄 vi
圖 目 錄 viii
表 目 錄 x
符號說明 xi
一、緒論 1
1.1 研究背景 1
1.2 相關文獻與研究動機 2
1.3 論文架構 5
二、相關研究 6
2.1 影像處理 7
2.1.1 Region of Interest 7
2.1.2 亮度資訊 7
2.1.3 高通濾波器 8
2.1.4 連通元件標記 9
2.2 卡爾曼濾波器 10
2.2.1 卡爾曼濾波器 10
2.2.2 自適應卡爾曼濾波器 12
2.2.3 擴展卡爾曼濾波器 14
2.2.4 無跡卡爾曼濾波器 15
2.3 基因演算法 15
2.4 插值方法 17
2.4.1 線性插值 17
2.4.2 二次多項式插值 18
2.5 系統評估指標 19
2.5.1 平均均方誤差 19
三、研究方法與步驟 21
3.1 測試及應用資料收集 27
3.2 硬體相關配置資訊 27
3.3 TCP傳輸 28
3.4 初始點位置 28
3.4.1 ROI 28
3.4.2 道路線亮度資訊強化 29
3.5 自適應卡爾曼濾波器 30
3.6 基因演算法計算卡爾曼增益 31
3.7 評估模型效能 32
四、研究成果 33
五、結論與未來展望 42
5.1 結論 42
5.2 未來展望 43
參考文獻 44
附錄A 48
附錄B 54

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