ORCID Identifier(s)

0009-0006-8678-3481

Graduation Semester and Year

Summer 2026

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Civil Engineering

Department

Civil Engineering

First Advisor

Dr. Sharareh Kermanshachi

Second Advisor

Dr. Arpita Bhatt

Third Advisor

Dr. Jay Michael Rosenberger

Fourth Advisor

Dr. Karthikeyan Loganathan

Fifth Advisor

Dr. Apurva Pamidimukkala

Abstract

Autonomous vehicles (AVs) are an emerging automation technology that is expected to transform the existing mobility landscape. At their highest level of automation, these self-driving vehicles, classified as fully autonomous vehicles (FAVs), can safely operate under all road conditions without the presence of a human driver. While the advent of FAVs may seem to be a facet of the distant future, continued investment in the technology and steady advancements in its development indicate that their deployment is imminent. As with any emerging technology, public perception plays a significant role in the success of this technology. Consumers’ resistance to this autonomous driving systems may prevent society from reaping the full benefits of AVs and is a significant barrier to their effective deployment. Prior research has extensively explored the different facets contributing to public acceptance of AVs through interview and survey data. While the existing body of knowledge offers important insight into the factors driving AV adoption, the following knowledge gaps were identified: (1) few studies have explored how inclination for use changes as the level of automation increases, (2) the literature lacks an empirical assessment of the attitudinal factors of residents of a city hosting a real-world AV pilot project, and (3) the integration of and interactions between first responders and AVs have been overlooked despite the extensive research on the safety of AVs and how the public will respond to this emerging technology. To address these knowledge gaps and contribute to the existing literature on AV acceptance, this study aims to provide a comprehensive evaluation of perceptions of AVs at varying levels of automation and adoption modes. Additionally, the study provides an assessment of first responders’ knowledge, experiences, concerns, and perceptions of AVs.

Informed by the literature review, the study began by developing a structured interview protocol to explore university students’ attitudes towards FAVs. A qualitative analysis using grounded theory revealed three primary themes: (1) experience with RAPID (Rideshare, Automation, and Payment Integration Demonstration), a shared autonomous vehicle (SAV) pilot project operating at partial autonomy within the study area; (2) attitudes towards FAVs and SAVs; and (3) future adoption of FAVs. The results suggested that first-hand experience with autonomous driving technologies led to a more positive impression of the technology among RAPID users. Both users and non-users voiced concerns about riding in a FAV without a safety attendant present, and most indicated their preference for partially autonomous vehicles due to their lack of confidence in the safe operation of AVs at full autonomy.

To capture the breadth and depth of FAV perceptions, a survey was then developed and distributed to residents of Arlington, Texas. The city presents a unique context for the evaluation of perceptions of autonomous driving systems due to its car-centric nature and the deployment of the RAPID project since March of 2021. Thus, residents of the city offer a unique perspective of FAV acceptance due to their direct exposure to AVs either as individuals that have ridden in a RAPID SAV or interacted with these vehicles on the road. The survey analysis was organized into two distinct parts, the first of which aimed to analyze perspectives on shared mobility and FAVs. A rank-ordered logit model was developed based on the survey data to evaluate residents’ preferences across automation levels and shared mobility. The results revealed that decision criteria factors, demographics, and mobility behavior shape interest in utilizing certain transportation modes. Additionally, the findings indicated a majority preference for private conventional vehicles, followed by private ride-hailing services. The second analysis utilizes non-parametric statistical tests to examine how demographic and attitudinal factors influence automation preference rankings. The findings indicated significant variations in ranking preferences, with an overall preference for manual vehicles, followed by partially autonomous vehicles and FAVs. Among different demographic groups, age, gender, race, and prior exposure to AVs were found to be significant for ranking manual vehicles and FAVs. Spearman’s rank correlation analysis revealed a mirrored and opposite trend across attitudinal factors for rankings of manual vehicles and FAVs, but preferences for partial automation were not strongly influenced by them. These findings collectively highlight the importance of private vehicle drivers’ acceptance of FAV for the successful implementation of this technology.

The second part of the survey analysis was designed to analyze the drivers of future mobility adoption to understand the factors that shape transportation preferences. An ordinal logistic regression model was developed to evaluate the impact of the perceived benefits and concerns of SAVs at the highest level of automation on adoption intent. The results revealed that adoption likelihood was positively and significantly impacted by the benefits of reducing driving stress and lowering transportation costs. Conversely, a lack of confidence in the SAVs’ capacity to handle unexpected situations, concerns about interactions with other road users, and the challenges of learning to use a FAV made people less likely to embrace the technology. The findings were supplemented by a structural equation modeling (SEM) analysis, built upon the unified theory of acceptance and use of technology. SEM was used to identify the factors influencing individuals’ behavioral intentions to adopt FAVs, and the results indicated that effort expectancy, social influence, and public services were the most significant predictors of FAV adoption intent. A thematic content analysis of the qualitative survey data uncovered four primary themes: trust in AV technology, the integration of AVs into existing roadways, intentions to adopt FAVs, and institutional assurances. The final survey analysis employed factor-based clustering based on attitudes towards FAVs to evaluate adoption trajectories. Three distinct classes that describe the user archetypes of FAV adopters were identified: tech-trusting skeptics, traditional drivers, and pragmatic adopters. Demographic analysis across clusters confirmed that FAV adoption is shaped by technological readiness and emotional and cognitive factors that vary across population demographics.

Lastly, the study conducted structured interviews with first responders (law enforcement officers, firefighters, and emergency medical personnel). The interview questionnaire was design to assess first responders’ familiarity with AVs in personal and professional contexts, identify AV-related training gaps, and explore how policymakers can assist first responders by facilitating smoother interactions with AVs and better equip first responders to integrate them into their occupational responsibilities. The responses underwent thematic analysis, and the findings provide valuable information on first responders’ knowledge, experiences, concerns, and perceptions of AVs. Grounded theory was employed and a conceptual framework was developed, illustrating how first responders’ initial preferences and their perceived AV-related benefits and concerns directly shape how they view AV incident management and the integration of this technology into their operational duties. Crucially, this relationship is moderated by institutional assurances, including the introduction of standardized training protocols, and the implementation of regulations and legislation.

This study highlights the importance of public receptivity to FAVs and may benefit policymakers by providing insight into the factors that affect the perceptions of prospective users, thereby facilitating their successful integration onto existing roadways. The findings provide valuable insights into consumer adoption intent and first responders’ perceptions of autonomous driving systems, enabling policymakers and AV manufacturers to design public policies that ensure societal readiness and a smooth transition from partially autonomous to fully autonomous vehicles.

Keywords

Fully autonomous vehicles, Shared autonomous vehicles, Autonomous vehicles, Shared mobility, First responders, Policy, Technological acceptance, Attitudes, Future adoption

Disciplines

Civil Engineering

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