ORCID Number
0000-0003-3013-9639
Date of Award
Fall 2025
Access Type
Dissertation - Open Access
Degree Name
Doctor of Philosophy in Human Factors
Department
Human Factors and Behavioral Neurobiology
Committee Chair
Alex Chaparro
Committee Chair Email
chapara3@erau.edu
First Committee Member
Barbara S. Chaparro
First Committee Member Email
chaparb1@erau.edu
Second Committee Member
Joseph R. Keebler
Second Committee Member Email
keeblerj@erau.edu
Third Committee Member
Michael Nees
Third Committee Member Email
Neesm@lafayette.edu
College Dean
Jayathi Raghavan
Abstract
As semi-autonomous vehicle (SAV) technologies become increasingly integrated into modern transportation, understanding how the public perceives their capabilities is essential for ensuring safe and effective human-automation interaction. This study examined drivers’ perceptions of SAV detection and discernment abilities across 28 driving conditions and how those perceptions related to expected vehicle behaviors in 17 corresponding scenarios. Exploratory and confirmatory factor analyses identified three perceptual dimensions: Traffic Infrastructure and Vehicle Recognition, Dynamic Roadway Hazards and Vulnerable Road Users, and Human Interaction and Contextual Cues. Moderate overlap between the first two dimensions suggested that participants viewed certain structured and dynamic roadway features as conceptually similar. Structural equation modeling revealed significant relationships between demographic and experiential factors and perceived SAV detection and discernment capabilities, including effects of age, gender, education, total advanced driver assistance systems (ADAS) experience, and self-reported confidence in using ADAS. However, these results should be interpreted cautiously due to weaker overall model fit. Higher confidence in using ADAS predicted stronger agreement with SAV detection and discernment capabilities, while greater experience using ADAS and higher education were associated with more critical evaluations. Across behavioral scenarios, participants generally expected appropriate rule-based responses when they believed detection or discernment was possible, but several cases revealed misalignments between perceived sensing and expected actions. These findings highlight how exposure and confidence interact to shape public expectations of automation and highlight the importance of user education and interface transparency to support safe engagement with SAV technologies.
Scholarly Commons Citation
Mersinger, Molly, "Investigating Driver Perceptions of Semi-Autonomous Vehicle Sensing and Response Characteristics" (2025). Doctoral Dissertations and Master's Theses. 942.
https://commons.erau.edu/edt/942
