駕駛人之行為與狀態乃是影響車輛安全的主要因素，精準的駕駛人狀態監控能有效提供各式主動式安全系統之設計、以及應用時之輔助，駕駛人於煞車事件中的延遲反應時間或可做為代表駕駛人狀態之一項指標。本研究旨在開發改良式面積法以能在車輛行駛過程中，計算駕駛人於煞車事件中的延遲反應時間。有別於文獻中的駕駛人延遲反應時間之分析大多著重於行車資料的事後統計，本研究之演算法主要特色在於能於行車過程中進行估算。本研究進行實際駕駛人於正常的車輛行駛條件下之煞車事件實車實驗，並經由實驗資料與演算法估算結果之比對以驗證本研究所開發之演算法之正確性。經由實驗數據之統計結果顯示，本研究所開發之改良式面積法能提供合理的駕駛人反應延遲時間估算。
除此之外，煞車事件之過程可細分成數個階段，而駕駛人於煞車過程中各階段之時間取決於車輛行進間的各種行車資訊。本研究嘗試探討駕駛者在煞車事件中，各行車變數與不同駕駛人行為特徵間之關聯，作為後續研究駕駛人延遲反應時間的基礎。透過灰關聯分析實驗數據並進行統計，本研究找出在煞車過程中影響駕駛人反應延遲時間的主要行車資訊。

Driver behavior and status are the primary factors that influence the vehicle driving safety. An accurate driver status monitoring can help the design and functioning of the active safety systems. It is conjectured that the driver reaction time during the braking events can serve as an indicator of the driver status. The objective of this research is to develop a revised area method that can provide an estimate of the human reaction time during the brake event. In contrast to the results presented in the literature about human reaction time, where the objectives are usually posterior statistical analyses of the human behavior, the feature of the proposed method is to provide the estimate of the human behavior during vehicle driving. The driving experiments with different human drivers in normal driving conditions have been conducted to collect brake events data. The experimental data is used to verify the estimated driver reaction time as calculated by the revised area method. The validation results indicate that the proposed revised area method can yield reasonably well estimate of thehuman reaction time. Furthermore, it is known that the brake events can be further divided into several segments, and the duration of each segment is expected to be dependent on the various driving variables. In this research, the correlations between the vehicle driving variables and the characteristics of human braking behavior are investigated to serve as the foundation for future research on human reaction time delay. The gray analysis technique is employed and the results are statistically analyzed, the primary factors that influence the different braking characteristics of the human drivers are identified.

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