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研究生:何晉亨
研究生(外文):Jin-Heng Hao
論文名稱:不同動態交通下駕駛行為模式之建立與分析
論文名稱(外文):Study of Driving Behavior Model under Different Traffic Conditions
指導教授:柳永青柳永青引用關係
指導教授(外文):Yung-Ching Liu
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:工業工程與管理研究所碩士班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:99
中文關鍵詞:模糊推論駕駛行為駕駛模擬倒傳遞類神經網路
外文關鍵詞:Driving BehaviorDriving SimulatorBack-Propagation Neural NetworkFuzzy Inference
相關次數:
  • 被引用被引用:18
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  • 下載下載:107
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隨著交通問題日益嚴重,且行的問題與人類的生活緊密結合,所以解決交通問題已是世界主要都市的首要目標。在政府提暢安全概念之際,交通安全是非常值得重視之一環。
本研究計畫利用駕駛模擬器,分別設計四個不同的實驗劇本,以探討在跟車行為、超車行為、交叉路有左右來車時和前方出現交通號誌(紅綠燈)時的駕駛行為和績效。藉由數據收集,找出真正影響駕駛者之駕駛因素,並結合模糊理論和倒傳遞類神經網路,建立上述四種不同的交通動態下的駕駛行為模糊推論模式和倒傳遞類神經網路模式,並加以比較。
經由統計分析得到四種交通動態下駕駛因素為:1.跟車情況:前車煞車情況、與前車之間距、相對速度;2. 超車情況:橫向位置、道路類型、與前車之間距、相對速度;3. 前方左右來車情況:道路類型、來車通過狀況、縱向速度、距離;4. 前方交通號誌情況:距離、燈號狀況,模糊推論模式和倒傳遞類神經網路比較,模糊推論模式的誤差均方根在跟車、紅綠燈的情形下均較倒傳遞類神經模式為小,然而倒傳遞類神經網路在左右來車、超車情況下優於模糊推論。且由RMSE得知,兩個模式皆有不錯的預測能力。
The study designed four different experiment scenarios to examine the driving behavior results under different traffic conditions, and made use of driving simulator to make experiment. The four traffic conditions included car following, overtaking, crossing vehicles, and traffic signs condition. By collecting experiment data and statistics analysis, the study can find driving factors which real influence driving behavior under the four traffic conditions. Finally the study made use of Fuzzy theory and Back-Propagation Neural Network to set the driving behavior model.
Results showed the driving factors under the four traffic conditions are: 1.car following: breaking condition of lead car, headway with lead car, and relative speed. 2.overtaking: lateral lane position, type of road, headway with lead car, and relative speed. 3.crossing vehicles: type of road, car crossing conditions, driver longitudinal velocity, and headway with crossing car. 4.traffic signs condition: headway with traffic sign and traffic signs conditions. Comparing between Fuzzy inference model and BPN forecasting model by RMSE, the result showed no significant evidence to know which is better. However both of them had good forecasting ability.
目錄
目錄 ii
表目錄 iv
圖目錄 v
第一章 緒論 1
1.1背景 1
1.2研究動機 3
1.3研究目的 4
1.4研究流程 6
1.5研究範圍和限制 7
第二章 文獻探討 8
2.1 模糊理論的應用 8
2.1.1 模糊理論之簡介 8
2.1.2 模糊理論之應用 9
2.2 倒傳遞類神經網路的應用 10
2.2.1 類神經網路介紹 10
2.2.2 倒傳遞網路介紹 12
2.3 動態交通下駕駛行為之探討 14
2.3.1 剎車行為 14
2.3.2 跟車行為 19
2.3.3 超車行為 22
第三章 研究方法 24
3.1 駕駛模擬器 24
3.2實驗一:跟車實驗 26
3.2.1受測者 26
3.2.2跟車道路劇本描述 26
3.2.3受測者工作 27
3.2.4實驗程序 27
3.2.5資料收集與分析 28
3.3實驗二:超車實驗 28
3.3.1受測者 28
3.3.2超車道路劇本描述 29
3.3.3受測者工作 29
3.3.4實驗程序 30
3.3.5資料收集與分析 30
3.4實驗三:交叉路有左右來車實驗 31
3.4.1受測者 31
3.4.2交叉路有左右來車實驗劇本描述 31
3.4.3受測者工作 32
3.4.4實驗程序 33
3.4.5資料收集與分析 33
3.5實驗四:紅綠燈號誌實驗 33
3.5.1受測者 34
3.5.2紅綠燈號誌實驗劇本描述 34
3.5.3受測者工作 35
3.5.4實驗程序 35
3.5.5資料收集與分析 36
3.6模糊推論模式之建立 36
3.7 倒傳遞類神經網路模式(Back-Propagation Neural Networks) 38
第四章 結果分析與模式建立 40
4.1 實驗數據分析 40
4.1.1 跟車實驗數據分析 40
4.1.2 超車實驗數據分析 44
4.2模糊推論模式和倒傳遞類神經網路之實作 59
4.2.1 模糊推論模式實作 59
4.2.2 倒傳遞類神經網路模式實作 63
4.3 模糊推論模式和倒傳遞類神經網路比較 66
第五章 討論與建議 76
5.1 討論 76
5.2 建議 82
參考文獻 83
附錄(A)實驗同意書 87
附錄(B) 模糊規則庫 88
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