Mooij, Milan de (2021) Working Memory Load and Visuospatial Demands During Driving: A Behavioral and Eye-Tracking Analysis. Bachelor's Thesis, Artificial Intelligence.
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Abstract
To combat human-error in driving and increase car safety, a tremendous amount of research has been conducted in the field of automated driving. However, there is evidence that complete autonomous control is not desirable when it comes to the safest driving experience. Adaptive automation is a system where the level of automation is adjusted to the state of the operator. In the context of driving, such a system could change its level of automation to the cogntive load of the driver to counteract the negative effects of cognitive overload or underload. For such a system to function, a robust method of measuring cognitive load is required. This study investigated whether cognitive load, here defined as a combination of working memory load (WML) and visuospatial demands, has an influence on working memory performance and driving performance. Furthermore, we also used eye-tracking to find out whether pupil size and eye-fixations are influenced by cogntive load. A simulated-driving experiment with eye-tracking was conducted in which WML and visuospatial demands were manipulated separately. In the simulation participants drove on a straight highway for 60 minutes. WML was manipulated by an n-back task (n = 0, 1, 2, 3, 4), by doing a speed-regulation-task. Visuospatial demands were manipulated by a change in the driving environment: a construction site with reduced lane width, increasing driving difficulty. Results indicate that working memory performance is only influenced by WML. D
Item Type: | Thesis (Bachelor's Thesis) |
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Supervisor name: | Borst, J.P. |
Degree programme: | Artificial Intelligence |
Thesis type: | Bachelor's Thesis |
Language: | English |
Date Deposited: | 15 Jul 2021 08:53 |
Last Modified: | 15 Jul 2021 08:53 |
URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/25249 |
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