Understanding Surveys of Public Sentiment Regarding Automated Vehicles: Summary of Results to Date and Implications of Past Research on the Dynamics of Consumer Adoption

Hassol, Joshua; Perlman, David; Chajka-Cadin, Lora; Shaw, Jingsi · 2019 · ROSA P / United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office

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Summary

This paper, sponsored by the U.S. Department of Transportation’s Intelligent Transportation Systems Joint Program Office, investigates public sentiment toward automated vehicles (AVs). The research addresses the discrepancy between significant industry investment in autonomous technology and widespread public concern, mistrust, and reluctance to adopt these vehicles. The authors aim to synthesize existing survey data to understand consumer attitudes regarding safety, trust, and willingness to try or buy AVs, while also evaluating the methodological limitations of current survey instruments and applying established technology adoption theories to the context of AVs. The authors conducted a comprehensive review of 22 surveys conducted by 15 entities between 2013 and 2019. These surveys utilized various methodologies, including online, telephone, mail, and in-person formats, with sample sizes ranging from 107 to over 4,000 respondents. The analysis focused on three primary metrics: willingness to try an AV, willingness to buy or regularly use an AV, and perceptions of safety. Additionally, the paper reviews literature on technology adoption models, such as the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, to identify factors influencing consumer uptake, including trust, perceived usefulness, social influence, and facilitating conditions. The findings indicate that public interest in AVs is mixed and generally leans negative. Across most surveys, slightly more than half of respondents expressed unwillingness to try riding in an automated vehicle. Specifically, AAA surveys conducted between 2016 and 2019 showed that between 63% and 78% of respondents were "too afraid" to ride in a fully self-driving vehicle. Interest in purchasing or regularly using AVs aligns with this reluctance, with approximately half of respondents in relevant studies indicating they would never buy such a vehicle. Safety perceptions are a primary driver of this resistance; at least half of respondents in most studies expressed concerns about the safety of fully self-driving vehicles. Notably, Cox Automotive surveys revealed that perceived safety decreased as the level of automation increased, with Level 3 vehicles viewed as safer than Level 4 or 5, contrary to industry concerns about Level 3 transition risks. The authors conclude that current survey methods are flawed due to inconsistent terminology, insufficient definitions, and potential bias in how AVs are described to respondents. Because AVs are "really new products" with limited public exposure, survey results are unstable and heavily influenced by framing and media impressions. The paper recommends that future research employ clear, neutral definitions, link surveys to actual vehicle demonstrations to gauge the impact of hands-on experience, and incorporate insights from technology adoption models to better understand the nuanced factors driving consumer acceptance.

Key finding

Across multiple surveys, slightly more than half of respondents were unwilling to try riding in an automated vehicle, and more than half perceived these vehicles as unsafe.

Methodology

review

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• acceptance adoption
• automation surprise
• trust calibration

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: self report data, observational prevalence
• Theoretical Contribution: conceptual framework