Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Nada Attar

Second Advisor

Robert Chun

Third Advisor

Sayma Akther


Distracted driving, driving performance


Distracted driving has grown in criticality over the recent years, given the numerous distractions that drivers face today, and which have further been magnified by the proliferation of in-vehicle technologies and mobile devices. Such distractions can seriously compromise a driver's ability to be fully focused on the road and to carry out timely responses and informed decisions that are key in minimizing the risks of a crash and maximizing road safety. The general aim of the research is to observe how simple versus complex distractors affect driving performance and gaze patterns. The eyetracker used is the Tobii Pro Fusion, synchronized with an eye-tracking driving simulator, whereby the participant is run through various types of simulated driving sessions, including both simple and complex distraction scenarios. Eye-movement data and pupil dilation measurements will be analyzed to reveal gaze patterns and pupil dilation changes that signify a reallocation of attention off the road, together with decrements in driving performance. This study should add sensitivity to the typical distracted driving problem by comparing the effects of different types of distraction systematically. This information is also to further inform the development of intervention strategies aimed at the general public and public awareness campaigns targeted at reducing the associated risks of distracted driving within a complex, technology-dependent transportation environment.

Available for download on Sunday, May 25, 2025