2017 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 2017 Forum on the Impact of Vehicle Technologies and Automation on Users, hosted by the AAA Foundation for Traffic Safety and the University of Utah. The forum aimed to identify future research needs regarding how advanced driver assistance systems (ADAS) and automated driving systems (ADS) affect drivers and other road users. The event gathered stakeholders from academia, industry, and government to discuss safety implications, user interactions, and necessary methodological approaches for evaluating these technologies. The forum consisted of two days of presentations and breakout sessions. Day 1 featured expert panels covering vehicle technologies and user factors. Presenters discussed NHTSA guidelines for Level 3 automation and higher, emphasizing voluntary guidance for safety elements like cybersecurity and operational design domains. Experts highlighted the "crash trifecta" and the importance of studying edge cases through naturalistic driving studies and virtual environments. Significant attention was given to human factors, including the decay of situation awareness during automation use, the critical role of consumer education in building trust, and the risks of miscalibrated trust (over-trust or under-trust). Panelists also addressed challenges in control transfer, mode confusion, and the need for training to mitigate skill degradation and misuse. Day 2 involved breakout groups identifying priority research gaps. Key areas included mental models, automation misuse, trust and acceptance, education and training, mixed fleets, human-machine interface (HMI) design, measurement metrics, and driver situation awareness. Groups identified specific questions, such as how inaccurate mental models lead to unintended consequences, how to calibrate driver trust with system capabilities, and how to design intuitive systems that minimize the need for extensive training. The forum also addressed the safety implications of mixed fleets containing both automated and non-automated vehicles, as well as the impact on pedestrians and other road users. The report concludes that addressing these research needs requires a multi-faceted approach utilizing diverse data sources, including naturalistic driving studies, event data recorders, simulations, and longitudinal data. Stakeholders agreed on the necessity of collaboration across sectors to establish consortiums and partnerships. The forum underscored that while automation offers safety benefits, it introduces new risks related to human-automation interaction, particularly regarding driver engagement, trust calibration, and system limitations. Effective implementation depends on resolving these human factors challenges through coordinated research, improved HMI design, and comprehensive user education.
Key finding
Nine breakout groups prioritized research gaps spanning driver mental models, automation misuse, trust calibration, education/training, mixed fleets, HMI design, measurement, situation awareness during hand-offs, and individual differences; attendees agreed multi-stakeholder collaboration and diverse methods (naturalistic driving, simulation, closed track) are needed, with major barriers including proprietary data, lack of standardization, and research lag behind technology.
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 (5 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 | 2 | 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.
- automation
- automation surprise
- situational awareness
- trust calibration
- acceptance adoption
- mode awareness
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).
- Methodological Resource: tool software
- Synthesis & Review: research agenda
- Theoretical Contribution: conceptual framework