自動駕駛已然成為市場以及世界發展主軸及趨勢，使得百家爭鳴，如Google旗下的Waymo或是當前的自駕車主導車廠Tesla皆已逐步實現無須駕駛介入之安全自駕車系統，而國內如鴻海等產業龍頭也開始投入研發國產電動車，希望趕上這波自動駕駛所掀起的巨浪，而影響自動駕駛車業關鍵的因素之一必為與周圍環境互動之安全性，在都市場景中則常常需要面對行人，而如何營造行人與車輛的安全互動成為各車廠最需要解決以及面對的問題。
　　因此本論文提出一套建置於機場航廈內之自動駕駛車前行人偵測方案，藉由光學雷達(LiDAR)通過濾波、雜訊去除來追蹤車輛周圍環境之障礙物，同時憑藉攝影機的畫面來分析行人的位置及關節資訊萃取，接著藉由深度學習神經網路來學習並實現預測行人相對於車輛的未來移動路徑，更甚透過轉移學習方法將已訓練好之模型轉移至國立中山大學校園所建置之環境，最終參考Euro NCAP之AEB測試規章進行本論文提出之安全偵測方法驗證，表明此系統有安全、時間及不同車速下應用之有效性，最後透過設計之人機互動介面顯示車前影像偵測到之危險行人，給予駕駛人或自動駕駛車系統參考依據，使車輛能夠於準確的預測行人移動路徑來做出最可靠的決策。

Autonomous driving has become the primary axis and trend of the market and global development, giving rise to a hundred schools of thought. For example, Google's Waymo or the current leading self-driving car manufacturer, Tesla, have gradually realized a safe self-driving system that does not require driver intervention, and domestic companies such as Hon Hai have also begun to invest in research and development. Domestic electric vehicles hope to catch up with the huge wave of autonomous driving, and one of the key factors affecting the autonomous driving industry must be the safety of interacting with the surrounding environment, and the most that needs to be addressed in urban scenes is pedestrian safety. The most important problem that every car factory must solve and face is how to create a safe interaction between pedestrians and vehicles.
Therefore, this paper proposes a set of pedestrian detection solutions for self-driving vehicles built in the D area of the second terminal of Taoyuan International Airport. The LiDAR is used to track obstacles in the surrounding environment of the vehicle through filtering and noise removal. At the same time, the camera image is used to analyze the pedestrian's position and joint information extraction, and then use the deep learning neural network to learn and realize the prediction of the future trajectories of the pedestrian relative to the vehicle, and even transfer the trained model through the transfer learning method to the environment built on the campus of National Sun Yat-sen University. The safety detection method proposed in this paper was verified by referring to the AEB test regulations of Euro NCAP, which showed that the system has safety, time, and the application effectiveness at different car speeds. Finally, the human-computer interaction interface displays the dangerous pedestrians detected by the image from the camera, providing a solid reference for the driver or the automatic driving system so that the vehicle could accurately predict the pedestrian's movement path to make the most reliable decision.

論文審定書 i
致 謝 ii
摘 要 iii
Abstract iv
目 錄 v
圖 目 錄 viii
表 目 錄 xi
第 1 章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 3
1.2.1 骨架分析 3
1.2.2 多目標追蹤 3
1.2.3 行為辨識 5
1.2.4 動作預測 5
1.2.5 意向評估 6
1.2.6 路徑預測 6
1.2.7 風險評估 7
1.3 主要貢獻 8
1.4 章節介紹 9
第 2 章 研究方法 10
2.1 神經元與多層感知器 10
2.2 CNN 11
2.3 LSTM 13
2.4 GRU 15
2.5 Transformer 16
2.6 模型損失函數 19
2.6.1 Categorical Cross Entropy 19
2.6.2 RMSE 19
第 3 章 系統概述 20
3.1 系統架構 20
3.1.1 影像子系統 21
3.1.2 LiDAR子系統 22
3.1.3 決策子系統 22
3.2 實驗平台 23
3.2.1 硬體設備 24
3.2.2 軟體開發元件 29
第 4 章 系統方法與實現 31
4.1 物件偵測方法 31
4.1.1 體素濾波 31
4.1.2 雜點去除 32
4.1.3 點雲聚類 33
4.2 物件追蹤方法 34
4.2.1 多目標決策 35
4.2.2 卡爾曼濾波 38
4.3 影像座標轉換方法 40
4.4 間接預測行人未來路徑架構 44
4.4.1 資料來源 44
4.4.2 資料處理流程 48
4.4.3 特徵取得及前處理方法 50
4.4.4 姿態辨識神經網路架構 53
4.4.5 線性路徑生成方法 54
4.5 直接預測行人未來路徑架構 57
4.5.1 資料來源 57
4.5.2 資料處理流程 57
4.5.3 特徵取得及前處理方法 60
4.5.4 深度神經網路架構 61
4.5.5 直接路徑生成方法 64
4.5.6 轉移學習 66
4.6 行車路徑偵測方法 68
4.7 警示方法 69
第 5 章 實驗結果與討論 71
5.1 實驗場域 71
5.2 LiDAR點雲濾波及雜點去除效果 73
5.3 LiDAR點雲聚類效果 75
5.4 座標轉換結果比較 77
5.4.1 線性擬合比較 77
5.4.2 獨立運作及融合進直接路徑預測模型 79
5.4.3 轉移學習前後結果比較 79
5.5 姿態預測準確性評估 81
5.6 間接預測架構結果評估及誤差值 85
5.7 直接路徑預測架構測試及結果評估 86
5.7.1 航廈場景 86
5.7.2 校園場景 87
5.8 直接路徑預測架構各類模型評估 90
5.8.1 航廈場景 90
5.8.2 校園場景 91
5.9 直接路徑預測整合測試結果及人機介面設計 93
第 6 章 結論與未來展望 94
6.1 結論 94
6.2 未來展望 94
參考文獻 95

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