Date of Award

1-10-2024

Publication Type

Thesis

Degree Name

M.A.Sc.

Department

Electrical and Computer Engineering

Supervisor

Balakumar Balasingam

Supervisor

Francesco Biondi

Rights

info:eu-repo/semantics/embargoedAccess

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Automotive manufacturers are increasingly adopting the use of automated driving systems (ADS) to help ease the workload of drivers and to improve road safety. The society of automotive engineers (SAE) defines six levels of automation, from level-0 to level-5, for on-road automotive vehicles where level-0 refers to no driving automation and level-5 refers to full driving automation. In ADS, drivers interact with the automation features by using and depleting their attentional capacity called cognitive capacity. This prompted active research in the recent past to understand the effect of partial automation on driver's performance and road safety. The present thesis utilizes data collected in a recent study that analyzed the drivers' cognitive load during partial automation, specifically during level-0 and level-2 levels. In this study, participants drove vehicles equipped with partial automation features in routes of varying topography. Further, the presented thesis also investigates temporal variation in cognitive load to study the changes in drivers' cognitive load with changing topography. The findings reported in this thesis highlight an increase in drivers' cognitive load over time, emphasizing the importance of considering temporal dimensions in cognitive performance studies.

Available for download on Saturday, February 01, 2025

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