Abstract

Driving technology has progressed significantly since the introduction of anti-lock braking and cruise control decades ago. Current driver assist features can alert drivers of oncoming vehicles and even take control to keep the vehicle centered within its lane. The level of trust that people place in automation can impact how they monitor and accept these automated systems. Previous research has shown several performance outcomes associated with improper calibrations of trust in automation. However, there is still a need to examine trust in the context of advanced driving technologies. Research has yet to sufficiently investigate factors influencing trust in assistive driving features. The current study was designed to examine whether changes to the driving environment might influence levels of trust in various driver assist features. In addition, this study sought to evaluate if individual characteristics might also influence automation trust. A sample of 166 participants completed a series of hypothetical driving vignettes describing both high and low complexity environments using five different driver assist features. It was hypothesized that trust in driving technologies would be related to scenario complexity, and that trust would vary across driving features (forward collision warning, cruise control, lane centering assist, adaptive cruise/traffic jam assist, and fully automated driving). Results showed that trust was significantly higher in low complexity than in high complexity scenarios. Furthermore, trust significantly varied across the five driver assist features. Findings also revealed that propensity to trust technology moderated the relationship between trust and driving feature manipulations. Similarly, dispositional trust in three of the five unique driving feature moderated the relationship between trust and scenario complexity. These findings have implications for the design and acceptance of autonomous systems, especially automated/assistive driving technologies, as well as other remotely operated vehicles.

Notes

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Graduation Date

2022

Semester

Fall

Advisor

Mouloua, Mustapha

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

Psychology

Degree Program

Psychology; Human Factors Cognitive Psychology

Format

application/pdf

Identifier

CFE0009347; DP0027070

URL

https://purls.library.ucf.edu/go/DP0027070

Language

English

Release Date

December 2022

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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