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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Mersinger, Molly"],"institution":null,"degree_name":"Doctor of Philosophy in Human Factors","degree_level":"Dissertation - Open Access","degree_discipline":"Human Factors and Behavioral Neurobiology","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-10-01T07:00:00Z","date_published":"2025-10-01T07:00:00Z","updated_at":"2026-07-27T19:26:22Z","subjects":["Semi-Autonomous Vehicles; Public Perceptions; Exploratory Factor Analysis; Confirmatory Factor Analysis; Structural Equation Modeling; Automotive User Experience; Vehicle Sensing; Vehicle Behavior","Human Factors Psychology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/942","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Mersinger, Molly"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Human Factors and Behavioral Neurobiology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Human Factors"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Semi-Autonomous Vehicles; Public Perceptions; Exploratory Factor Analysis; Confirmatory Factor Analysis; Structural Equation Modeling; Automotive User Experience; Vehicle Sensing; Vehicle Behavior","Human Factors Psychology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/942"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Investigating Driver Perceptions of Semi-Autonomous Vehicle Sensing and Response Characteristics"]}]}],"canonical_facts":{"dc:creator":["Mersinger, Molly"],"dc:description.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.
"],"dc:identifier":["https://commons.erau.edu/edt/942"],"dc:subject":["Semi-Autonomous Vehicles; Public Perceptions; Exploratory Factor Analysis; Confirmatory Factor Analysis; Structural Equation Modeling; Automotive User Experience; Vehicle Sensing; Vehicle Behavior","Human Factors Psychology"],"dc:title":["Investigating Driver Perceptions of Semi-Autonomous Vehicle Sensing and Response Characteristics"],"thesis:degree_discipline":["Human Factors and Behavioral Neurobiology"],"thesis:degree_level":["Dissertation - Open Access"],"thesis:degree_name":["Doctor of Philosophy in Human Factors"]},"updated_at":"2026-07-27T19:26:22Z"}