駕駛人操控介入意圖推論之研究｜國立臺灣科技大學博碩士論文系統

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研究生：	蘇威丞 Wei-Cheng Su
論文名稱：	駕駛人操控介入意圖推論之研究 The Investigation of a Vehicle Driver Override Inference System
指導教授：	陳亮光 Liang-kuang Chen
口試委員:	徐繼聖 Gee-Sern Jison Hsu 洪博雄 Boe-Shong Hong
學位類別：	碩士 Master
系所名稱：	工程學院 - 機械工程系 Department of Mechanical Engineering
論文出版年：	2010
畢業學年度：	98
語文別：	中文
論文頁數：	89
中文關鍵詞：	隱馬可夫模型、動態貝氏網路、駕駛意圖
外文關鍵詞：	Hidden Markov Model, Dynamic Bayesian Network, Driver Intention
相關次數：	點閱：295 下載：3
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上一筆

本研究透過駕駛模擬器進行實驗收集所需要的數據，並將數據整理成訓練以及驗證所需要的資料庫，然後利用此資料庫進行隱馬可夫模型與動態貝氏網路的模型訓練，並探討不同資訊源以及有無疲勞資訊對識別駕駛人是否意圖介入主動轉向輔助系統之影響與優劣性，最後將訓練好的機率模型實現於線上駕駛模擬器中，使系統能夠識別出正確的駕駛人意圖發生點，以及可適應駕駛狀態的切換時間。最後結合控制權重調節器，當駕駛意圖改變時，調節器可藉由識別出的駕駛行為資訊，調節駕駛人與安全系統間的控制權重來避免衝突行為，以確保駕駛人的行車安全

In this research a vehicle driver override inference algorithm is developed. The data is collected from a driving simulator several subject human drivers, and divided into training data set and validation data set. Two classification models, namely the hidden Markov model and the dynamic Bayesian network, are trained. Several different choices of information sources are investigated to compare their effects on the classification performance. The trained classification model is implemented on the driving simulator to illustrate the on-line inference capability, and the possible integration with a decision making rule for the control authority of a lane keeping assist controller. The driving simulator experiments indicate that the inferred driver override intention can effectively assist the control authority determination and consequently successfully prevent the conflict between the driver and the lane keeping assist controller during a lane change maneuver.

摘要............................................................................................................................... I
ABSTRACT .................................................................................................................. II
目錄.............................................................................................................................. III
圖目錄........................................................................................................................... V
表目錄....................................................................................................................... VIII
第一章 緒論.................................................................................................................. 1
1研究背景與動機.............................................................................................. 1
2文獻探討.......................................................................................................... 2
2.1 使用HMM判別駕駛意圖之文獻探討.............................................. 2
2.2使用BN或DBN應用於駕駛意圖相關文獻探討............................. 4
2.3使用其它方式判別駕駛意圖相關文獻探討....................................... 5
2.4 文獻結果討論...................................................................................... 6
3研究架構與工作項目...................................................................................... 7
1-4預期貢獻 ......................................................................................................... 8
第二章 基礎理論.......................................................................................................... 9
1隱馬可夫模型.................................................................................................. 9
1.1隱馬可夫模型基本組成元素............................................................. 10
1.2 評估HMM模型................................................................................ 11
1.3推論最佳狀態序列 (Inference) ......................................................... 14
1.4學習 (Learning) ................................................................................. 16
2 動態貝氏網路 (Dynamic Bayesian Network, DBN) .................................. 18
2.1 貝氏網路 (Bayesian Network, BN) .................................................. 18
2.2動態貝氏網路基礎與其應用文獻探討............................................. 19
2.3動態貝氏網路基本組成元素............................................................. 20
2.4動態貝氏網路參數學習..................................................................... 21
2.5 DBN推論 ........................................................................................... 21
2.6 HMM 與 DBN 之比較 [27] ........................................................... 22
IV
3駕駛人狀態判別............................................................................................ 22
3.1 兩種疲勞狀態判別機制.................................................................... 22
3.2 卡爾曼濾波器.................................................................................... 23
5 控制權重調節器........................................................................................... 23
第三章 實驗設計與資料處理.................................................................................... 25
1 線上駕駛模擬器硬體架構........................................................................... 25
2 實驗設計與資料取得................................................................................... 28
3 實驗數據資料處理....................................................................................... 30
3.1 建立狀態序列.................................................................................... 30
3.2 資料截取與合併................................................................................ 31
3.3 資料分群............................................................................................ 33
4 HMM及DBN網路結構圖 .......................................................................... 39
4.1 HMM之網路結構圖.......................................................................... 39
4.2 DBN之網路結構圖 ........................................................................... 42
第四章 實驗結果分析與討論.................................................................................... 47
1 駕駛意圖推論決定規則 (Decision Rules).................................................. 47
2 資訊源探討................................................................................................... 47
2.1 單一資訊源........................................................................................ 47
2.2 前車資訊及雙資訊源........................................................................ 50
2.3 多資訊源............................................................................................ 54
3 疲勞資訊改善效果....................................................................................... 56
4 線上DO意圖識別與控制調節器之效能驗證實驗 ................................... 60
第五章 結論與未來展望............................................................................................ 65
1 總結與結論................................................................................................... 65
2 未來工作與展望........................................................................................... 66
參考文獻...................................................................................................................... 67
附錄A 線上實驗規劃 ................................................................................................ 71
附錄B 程式架構Pseudo-Code .............................................................................. 76

                                

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