Abstract
The aim of this study is to propose a method of evaluating for level of driver’s mental workload by using machine learning methods. In order to examine parameters related to visual behavior effective for evaluating driver's mental workload in driving, we measured EOG and head movements during simulated driving. In the experiment, we attempted to distinguish the "low mental workload" condition from "high mental workload" condition by using various machine learning methods, such as Adaboost, RBF networks and SVM on data related to EOG and head movements. Through cross-validation using the data from one participant as test data and data from the others as training data, the Adaboost method was determined to have the highest correct discrimination rate (over 85%). These results suggest the possibility of evaluating mental workload while driving.
