Name: Brittany Holthausen
Dissertation Proposal Meeting
Date: April 20, 2020
Time: 2:30pm
Location: https://bluejeans.com/846678150?src=join_info
 
Advisor:
Bruce Walker, Ph.D. (Georgia Tech)
 
Thesis 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 nuanced 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 aims to test the STS-AD in a higher fidelity environment using a mid-fidelity driving simulator. Participants drove two routes in the driving simulator. One route they drove with low automation (adaptive cruise control only) and the other with high automation (adaptive cruise control with automated lane keeping). The results of the simulator study will further validate the separable nature of the situational trust construct, establish the factorial structure of the scale, and determine if level of automation is an influencing factor in situational trust. The development and validation of 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.