Individual Predictors of Autonomous Vehicle Public Acceptance and Intention to Use: A Systematic Review of the Literature
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This systematic review addresses the critical challenge of predicting public acceptance and intention to use fully autonomous vehicles (AVs). The authors argue that while AVs promise significant societal benefits, including improved safety, traffic efficiency, and facilitated mobility for disadvantaged groups, these benefits depend heavily on widespread public adoption. Since AVs are not yet widely available, predicting travel demand and ownership trends is difficult. The study identifies a knowledge gap in existing literature, noting that over half of relevant publications were released in 2019, necessitating an updated, cohesive analysis of individual determinants influencing AV acceptance. The research specifically focuses on passenger vehicles with SAE Level 4 and Level 5 automation. The study employs a systematic literature review methodology to identify and classify individual predictors of AV acceptance. It synthesizes existing theories of technology adoption, including the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Use of Technology (UTAUT). These frameworks help explain how perceived usefulness, ease of use, subjective norms, and performance expectancy influence user behavior. The authors developed a conceptual framework to map influential factors, categorizing them into three main groups: demographic characteristics, psychological factors, and mobility behavior characteristics. This approach allows for a structured analysis of both observable factors, such as age and current travel habits, and unobservable latent attitudes, such as risk perception and trust. The findings reveal that public perceptions and adoption intentions vary significantly across different socio-demographic cohorts. Commuters value different aspects of AV technology, which directly shapes their willingness to accept or adopt the technology. The review highlights that consumer perceptions are the ultimate determinant of AV success, potentially driving policy changes. The study notes that while some individuals are enthusiastic about AVs, others are unwilling to relinquish control, indicating that low public acceptance, rather than technological limitations, may be the primary barrier to diffusion. The conceptual framework successfully maps these diverse factors, showing how demographic, psychological, and behavioral traits interact to form attitudes toward AVs. The significance of this work lies in its utility for urban and transport policymakers, managers, and planners. By identifying key individual predictors, the study aids in planning for a healthy AV adoption process with minimal societal disruption. The authors conclude that direct experience with AVs, combined with targeted education and communication strategies, is essential for positively shifting public attitudes. This review provides a foundational understanding of the behavioral drivers behind AV adoption, offering insights that can guide future research and policy development to ensure the successful integration of autonomous vehicles into existing transport systems.
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-25 |
| archive | success | openalex | — | — | 5 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
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