Users’ Trust in and Concerns about Automated Driving Systems
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Summary
This research brief examines public trust, adoption preferences, and concerns regarding automated driving systems (AVs) across different levels of automation. The study was motivated by the rapid progression of automation technology in production vehicles and the critical need for public trust to achieve large-scale market penetration. While AVs offer potential benefits such as reduced human-error crashes, decreased congestion, and improved mobility for vulnerable populations, previous studies have shown mixed public attitudes. This work specifically investigates how trust and concern vary with the specific capabilities (SAE Levels 0–5) of the technology, building on a 2019 AAA Foundation study. The study utilized data from the 2019 Traffic Safety Culture Index, a national online survey conducted by the AAA Foundation for Traffic Safety. The survey included 3,511 U.S. residents aged 16 or older, recruited via probability-based random digit dial and address-based sampling to ensure representativeness. Data were weighted to align with the U.S. population characteristics. The analysis employed descriptive statistics and Pearson’s Chi-squared tests to examine trust (defined as confidence in crash reduction), adoption comfort (preferred ownership level if cost were no barrier), and concerns across automation levels. Results were compared with 2018 data to identify trends. Key findings indicate that public trust in AVs for crash prevention decreases as automation levels increase. Respondents were more likely to trust lower-level systems (Levels 2 and 3) than higher-level ones (Levels 4 and 5). Similarly, adoption preferences favored lower automation; in 2019, there was a notable increase in respondents preferring Level 0 or 1 vehicles, particularly among those aged 19–24. Demographic differences emerged, with younger adults and men showing higher trust and comfort with higher automation levels compared to older adults and women. Concerns also scaled with automation level; technology malfunction was the primary worry for all levels, affecting 76% of respondents for Level 5. However, the nature of concerns differed by trust level. Those who distrusted AVs were significantly more concerned about safety and control issues, such as malfunction, over-reliance, hacking, and privacy. In contrast, those who trusted AVs cited purchase price as their top concern across all levels, with technology malfunction as the second highest. The significance of these findings lies in the identification of distinct barriers to AV adoption based on user trust. The divergence in concerns between trusting and distrusting groups suggests that public education must address both safety limitations and economic factors. The authors conclude that developing "strong mental models" of AV capabilities and limitations is essential for improving safety and performance. Balanced information that explains both functions and constraints can help mitigate over-reliance and reduce unfounded fears, thereby fostering the public trust necessary for the widespread integration of automated driving systems.
Key finding
Public trust in automated driving systems decreases as automation levels increase, and individuals who distrust these systems report significantly higher concerns about safety and security issues compared to those who trust them.
Methodology
survey
Sample size: 3511
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_aaa_foundation on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | aaa_foundation | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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