Document Type

Open Access Thesis

Embargo Period

8-16-2018

Degree Program

Industrial Engineering & Operations Research

Degree Type

Master of Science in Industrial Engineering and Operations Research (M.S.I.E.O.R.)

Year Degree Awarded

2018

Month Degree Awarded

September

Abstract

This study aims to evaluate algorithms designed to detect distracted driving. This includes the comparison of how efficiently they detect the state of distraction and likelihood of a crash. Four algorithms that utilize measures of cumulative glance, past glance behavior, and glance eccentricity were used to understand the distracted state of the driver and were validated on two separate data sources (i.e., simulator and naturalistic data). Additionally, an independent method for distraction detection was designed using data mining methods. This approach utilized measures like steering degree, lane offset, lateral and longitudinal velocity, and acceleration. The results showed a higher likelihood of distracted events when cumulative glances were considered. However, the state of distraction was observed to be higher when glance eccentricity was added. Additionally, it was observed that glance behavior using the four legacy algorithms were better detectors of the state of distraction as compared to the data mining method that used vehicular measures. This research has implications in understanding the state of distraction, predicting the power of different methods, and comparing approaches in different contexts (naturalistic vs simulator). These findings provide the fundamental building blocks towards designing advanced mitigation systems that give drivers feedback in instances of high crash likelihood.

First Advisor

Shannon C Roberts Ph.D., Chair

Second Advisor

Jenna L. Marquard Ph.D., Member

Third Advisor

Michael Knodler Jr., Ph.D., Member

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