應用模糊理論與類神經網路建構煞車行爲模式

Applying Fuzzy Theory and Neural Network to Develop the Braking Behavior Model

10.6459/JCM.200809_5(2).0005

柳永青(Y.C. Liu)；何晉亨(C.H. Ho)

煞車行爲 ； 駕駛模擬 ； 模糊推論 ； 倒傳遞類神經網路 ； 跟車行爲 ； Driving Simulator ； Braking Behavior ； Fuzzy Inference ； Back-Propagation Neural Network ； Car following model

危機管理學刊

5卷2期（2008 / 09 / 01）

39 - 48

繁體中文

本研究利用駕駛模擬器探討在跟車行爲、交叉路有左右來車時和前方出現交通號誌時的煞車行爲和煞車績效。藉由實驗數據收集，找出影響駕駛者決策之駕駛因素，並分別結合模糊理論和倒傳遞類神經網路，建立上述不同的交通動態下的煞車行爲模式，並加以比較兩者之優劣，發現兩者皆能有效推論出其煞車行爲且並無明顯的差異。

The study designed three different experiment scenarios to examine the braking behavior results under different traffic conditions, and made use of driving simulator to make experiment. The three traffic conditions included car following, crossing vehicles, and traffic signs condition. By collecting experiment data and statistics analysis, the study can find driving factors which real influence braking behavior under the three traffic conditions. Finally the study made use of Fuzzy theory and Back-Propagation Neural Network to set the driving behavior models. 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.

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