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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Highly automated vehicles require drivers to remain aware enough to takeover during critical events. Driver distraction is a key factor that prevents drivers from reacting adequately, and thus there is need for an alert to help drivers regain situational awareness and be able to act quickly and successfully should a critical event arise. This study examines two aspects of alerts that could help facilitate driver takeover: mode (auditory and tactile) and direction (towards and away). Auditory alerts appear to be somewhat more effective than tactile alerts, though both modes produce significantly faster reaction times than no alert. Alerts moving towards …

Contributors
Brogdon, Michael A, Gray, Robert, Branaghan, Russell, et al.
Created Date
2018

Reading partners’ actions correctly is essential for successful coordination, but interpretation does not always reflect reality. Attribution biases, such as self-serving and correspondence biases, lead people to misinterpret their partners’ actions and falsely assign blame after an unexpected event. These biases thus further influence people’s trust in their partners, including machine partners. The increasing capabilities and complexity of machines allow them to work physically with humans. However, their improvements may interfere with the accuracy for people to calibrate trust in machines and their capabilities, which requires an understanding of attribution biases’ effect on human-machine coordination. Specifically, the current thesis explores …

Contributors
Hsiung, Chi-Ping, Chiou, Erin, Cooke, Nancy, et al.
Created Date
2019

Human-robot interaction has expanded immensely within dynamic environments. The goals of human-robot interaction are to increase productivity, efficiency and safety. In order for the integration of human-robot interaction to be seamless and effective humans must be willing to trust the capabilities of assistive robots. A major priority for human-robot interaction should be to understand how human dyads have been historically effective within a joint-task setting. This will ensure that all goals can be met in human robot settings. The aim of the present study was to examine human dyads and the effects of an unexpected interruption. Humans’ interpersonal and individual …

Contributors
Shaw, Alexandra Luann, Chiou, Erin, Cooke, Nancy, et al.
Created Date
2019

With the growth of autonomous vehicles’ prevalence, it is important to understand the relationship between autonomous vehicles and the other drivers around them. More specifically, how does one’s knowledge about autonomous vehicles (AV) affect positive and negative affect towards driving in their presence? Furthermore, how does trust of autonomous vehicles correlate with those emotions? These questions were addressed by conducting a survey to measure participant’s positive affect, negative affect, and trust when driving in the presence of autonomous vehicles. Participants’ were issued a pretest measuring existing knowledge of autonomous vehicles, followed by measures of affect and trust. After completing this …

Contributors
Martin, Sterling, Cooke, Nancy, Chiou, Erin, et al.
Created Date
2019