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Name: Brittany Holthausen
Dissertation Defense Meeting
Date: May 11, 2020
Time: 1:00 pm
Location: https://bluejeans.com/680287802 meeting ID code: 680 287 802
Advisor:
Bruce Walker, Ph.D. (Georgia Tech)
Dissertation Committee Members:
Jamie Gorman, Ph.D. (Georgia Tech)
Richard Catrambone, Ph.D. (Georgia Tech)
Karen Feigh Ph.D. (Georgia Tech)
David Keller, Ph.D. (Naval Surface Warfare Center Dahlgren)
Title: Development and Validation of the Situational Trust Scale for Automated Driving (STS-AD)
Abstract:
Trust in automation is currently operationalized with general measures that are either self-report or behavioral in nature. However, a recent review of the literature suggests that there should be a more specific approach to trust in automation as different types of trust are influenced by different factors (Hoff & Bashir, 2015). This work is the development and validation of a measure of situational trust for the automated driving context: The Situational Trust Scale – Automated Driving (STS-AD).
The first validation study showed that situational trust is a separable construct from general trust in automation and that it can capture a range of responses as seen in the difference between scores after watching a near automation failure video and non-failure videos. The second study aimed to test the STS-AD in a mid-fidelity driving simulator. Participants drove two routes: low automation (automated lane keeping only) high automation (adaptive cruise control with automated lane keeping). The results of the second study provided further support for situational trust as a distinct construct, provided insight into the factorial structure of the scale, and pointed towards a distinction between advanced driver assistance systems (ADAS) and automated driving systems (ADS).
The STS-AD will revolutionize the way that trust in automation is conceptualized and operationalized. This measure opens the door to a more nuanced approach to trust in automation measurement that will inform not only how drivers interact with automated systems; but, can impact how we understand human-automation interaction as a whole.
