Investigating Driver Perceptions of Semi-Autonomous Vehicle Sensing and Response Characteristics

<p>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.</p>

Name thesis:degree_name

Doctor of Philosophy in Human Factors

Level thesis:degree_level

Dissertation - Open Access

Discipline thesis:degree_discipline

Human Factors and Behavioral Neurobiology

Year

2025

Author dc:creator

• Mersinger, Molly

• Semi-Autonomous Vehicles; Public Perceptions; Exploratory Factor Analysis; Confirmatory Factor Analysis; Structural Equation Modeling; Automotive User Experience; Vehicle Sensing; Vehicle Behavior
• Human Factors Psychology

Repository record dc:identifier

https://commons.erau.edu/edt/942

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1983