Item – Theses Canada

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OCLC number
1335043995
Link(s) to full text
LAC copy
Author
Zhao, Xin.
Title
Driver Cognitive Workload Detection via Eye-tracking and Physiological Modalities.
Degree
M.A.S. -- University of Toronto,, 2018.
Publisher
[Toronto, Ontario] : University of Toronto, 2018
Description
1 online resource
Abstract
In recent years, autonomous car development has become one of the hottest topics in AI applications and the driver cognitive workload monitoring system is a critical element of the autonomous car. This study explored the feasibility of classifying driver cognitive workload levels with eye-tracking and physiological modalities individually. Around a 70% detection accuracy was obtained with both modalities for ternary classes. Support Vector Machines (SVM) with a Gaussian Kernel function are utilized to build a monitoring system with 5-fold cross-validation. Principal component analysis (PCA) was investigated in terms of system performance. The time gaps between training and testing data are analyzed and the feasibility of using the o-line pretrained model to detect driver cognitive workload is investigated.
Other link(s)
tspace.library.utoronto.ca
hdl.handle.net