2021 Virtual Forum on the Impact of Vehicle Technologies and Automation on Users: A Summary Report
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Summary
This report summarizes the proceedings of the 2021 Virtual Forum on the Impact of Vehicle Technologies and Automation on Users, convened by the AAA Foundation for Traffic Safety. The forum aimed to gather stakeholders from academia, industry, and government to discuss the safety implications of emerging vehicle technologies and identify critical research needs. The event featured panel discussions and breakout sessions focused on three themes: understanding and perception of automation, driver interactions with automated systems, and education and training strategies. The forum presented findings from various research initiatives. Survey data indicated that while most respondents preferred Level 2 or 3 automation if cost were no object, self-reported understanding correlated with trust, and higher-level automation did not necessarily increase perceived crash prevention effectiveness. Research on mental models revealed that drivers with stronger system understanding responded more effectively to edge cases, though some users remained misinformed and unwilling to learn. Naturalistic driving studies showed that experienced users of Level 2 automation engaged in more distracting activities and longer glances away from the road compared to manual driving, whereas inexperienced users did not exhibit this pattern. Additionally, research highlighted that system branding and educational tone significantly impact driver expectations, even when technical descriptions are accurate. Breakout discussions identified several pressing research needs and barriers. A primary focus was on developing effective education and training methods to build accurate mental models, particularly given the lack of standardization across manufacturers and the challenges of training for passive monitoring rather than active driving. Participants emphasized the need for better metrics to measure real-time understanding and training efficacy. Concerns were raised regarding driver state monitoring (DSM), including public acceptance, privacy issues, and drivers circumventing systems. Other key issues included the impact of over-the-air updates on safety and training, the need for clearer operational design domains, and the influence of misleading media terminology (e.g., "self-driving") on public perception. Policy gaps were noted, with participants suggesting that original equipment manufacturers should lead on safety standards, such as DSM implementation, due to the slow pace of legislative action. The significance of this report lies in its synthesis of current knowledge gaps and the urgent need for coordinated action among stakeholders. It underscores that technological advancement is outpacing user understanding and regulatory frameworks. The identified research priorities—ranging from improved human-machine interfaces and calibrated trust to standardized nomenclature and effective training delivery—provide a roadmap for future efforts to ensure the safe integration of vehicle automation. The report highlights that without addressing these barriers, particularly regarding user education and system transparency, the safety benefits of automation may not be realized.
Key finding
The forum identified that driver trust and behavior are heavily influenced by mental models and educational framing, with experienced users showing increased distraction during automation use, while highlighting critical gaps in policy, standardized training, and public perception management.
Methodology
mixed_methods
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 (9 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 | 6 | 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.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- acceptance adoption
- automation surprise
- automation
- situational awareness
- mode awareness
- 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
- Synthesis & Review: research agenda
- Theoretical Contribution: conceptual framework