| Summary: | 碩士 === 國立清華大學 === 工業工程與工程管理學系 === 94 === Abstract
Driving task consumes a great deal of operator attention continuously. Either low vigilance or information overload may lead to human errors. Human errors were always major cause of traffic accidents. Therefore, understanding operator mental state is important.
In this study, the virtual environment of freeway was simulated where the drivers drove as usual. Mental workload of bus drivers were affected via different conditions or unexpected events. At the same time, driving performance, physiological index, and subjective ratings were measured during or after driving.
We constructed a multiple regression and polynomial neural networks to predict mental workload which are evaluated by data from subjective ratings, task performance, and physiological indexes. In multiple regression model, it is found the mental workload is effectively related to average speed, average braking depth variation, and heart rate (p=0.000<0.01). The results in this study can be referred to develop adaptive aiding systems and also are potential to enhance comfort and safety in traffic.
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