本研究利用Matlab XPC架構下的車輛模擬器進行資料收集與實驗，並透過機率類神經網路分類器結合駕駛人模型交越頻率做為推論疲勞的可能資訊再利用ARMAX模型結合車輛動力學預估出更加準確的路徑來計算車道偏離時間，最後將資訊源統整分析駕駛人在車道維持的操控情境下，當發生的非自主性的車道偏離事件時，各種資訊源對於警示時機的影響跟恰當性。

In this research the integration of the driver information to the lane departure warning system is investigated. The probabilistic neural network is used as the warning decision mechanism. The estimated open-loop crossover frequency of the driver-vehicle combination is selected as the driver information representing the fatigue variation. Furthermore, the on-line estimated driver model is used to improve the accuracy of the vehicle path prediction that is used in the warning decision. The driving simulator is used to conduct human-in-the-loop experiments. The simulator data is used to evaluate the effectiveness of the selected driver information in the enhancement of lane departure warning.

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