Date of Award
10-30-2020
Publication Type
Master Thesis
Degree Name
M.A.Sc.
Department
Electrical and Computer Engineering
Keywords
Advanced Driver Assistance System, Cognitive Load estimation, Eye tracking, N-back task, Physiological Metrics, Response Time
Supervisor
Balakumar Balasingam
Supervisor
Francesco Biondi
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
In this thesis, we investigate some physiological metrics to estimate the cognitive load experienced by drivers in a driving simulated environment and how their cognitive load is varies and affects certain physiological metrics.It is important to better understand the cognitive status of the human beings inorder to design efficient machines for automation. For instance, an advanced driver assistance system (ADAS) with the ability to understand the cognitive state of human will enhance the overall safety of the roads. In order to achieve such an intelligent automation system involving humans we need to develop approaches that can better estimate cognitive load through non-invasive means and to develop control strategies for real-time system adaptation with humans. The focus of this thesis is to present some hypotheses for cognitive load estimation based on some physiological measures, such as, pupil diameter, heart rate, and response-time and how each of these metrics change for varying cognitive difficulty levels. The variation in these metrics is proved using statistical analysis techniques.
Recommended Citation
Ramakrishnan, Priyadharshini, "Cognitve Load Estimation in Drivers for Advanced Driver Assistance System" (2020). Electronic Theses and Dissertations. 8472.
https://scholar.uwindsor.ca/etd/8472