汽車自動化駕駛已經是未來的趨勢，近年來，經由世界各國及產、經、學界的努力，在自動駕駛領域上投入大量資源已逐漸看到成果，加上全球定位系統(Global Positioning System)等技術的成熟，使得自動化駕駛更具未來性。
過去自動化駕駛的研究著重在避免碰撞及最佳路徑規劃上，對於車隊中其他車輛互動較少群體式移動的研究，車輛群體移動可應用在遊覽車的群體移動、家族式旅遊及災難物資運送車輛等方面。
本研究之目的在於模擬車輛全自動化駕駛，著眼於車輛移動過程中做有效的安全決策避免碰撞，此外，我們修改生物群體式移動行為演算法，讓群體車輛能自動加入車隊呈現群體式移動，並且不會因群體移動導致塞車的現象。

With enormous help and funding from the governments, industries and institutes around the world, autopilot has become more mature, and soon it will become the future. It can be foreseen that autopilot can be more realistic with the advance techniques, such as Global Positioning System (GPS).
Previous researches on autopilot focused on anti-collision and best driving path search, they had few studies on grouped autopilot system. This grouped system can be applied to tourist coaches, family tour, or transporting of aids.
This paper aimed to simulate the full unmanned autopilot system, focus on the autonomous safety decision of vehicles behavior. Moreover, we modify the flock algorithm of animal behavior, to attract the group vehicles together without block the others.

致謝 i
摘要 ii
Abstract iii
目錄 iv
表目錄 vi
圖目錄 vii
1. 緒論 1
1.1. 研究動機 1
1.2. 研究目的 2
1.3. 論文架構 2
2. 背景知識與文獻探討 4
2.1. 車輛自動化駕駛研究 4
2.2. 生物群體自主行為模擬 5
3. 研究方法 8
3.1. 模型假設 8
3.2. 符號、定義及定理 9
3.3. 安全距離 11
3.4. 模擬系統策略 12
3.4.1. 演算法 12
3.4.2. 安全加速步驟 13
3.4.3. 安全變換車道步驟 15
3.4.4. 安全煞車步驟 17
3.4.5. 群體車輛移動策略 18
3.4.6. 車輛自主行為運算流程 20
3.5. 道路追隨虛擬力(Path Following) 21
3.6. 開發工具Processing 22
4. 實驗結果 24
5. 結論及未來展望 29
參考文獻 30
附錄 33
A.實驗數據 33
B.車輛行駛情境 42

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