2018 Forum on the Impact of Vehicle Technologies and Automation on Vulnerable Road Users and Driver Behavior and Performance: A Summary Report
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Summary
This report summarizes the proceedings of the 2018 Forum on the Impact of Vehicle Technologies and Automation on Vulnerable Road Users and Driver Behavior and Performance, hosted by the AAA Foundation for Traffic Safety and the University of Iowa. The forum aimed to identify future research needs regarding how emerging transportation technologies affect vulnerable road users (VRUs)—such as pedestrians, cyclists, older adults, and novice drivers—and driver behavior. Stakeholders from academia, industry, and government convened to discuss current challenges, exchange ideas, and prioritize collaborative research efforts to improve traffic safety in an era of rapid technological evolution. The event featured two expert panels and breakout sessions. Panel 1 focused on VRUs, with presentations covering federal initiatives, simulation-based studies of pedestrian interactions, risks for novice drivers, and the specific challenges older drivers face with advanced automation. Panel 2 addressed driver behavior and performance, examining the rollout timeline of automated vehicles (AVs), crash investigations by the National Transportation Safety Board, field operational tests revealing driver confusion and "nuisance" alerts, and the role of trust and mental models in technology acceptance. Day 2 involved small groups synthesizing these discussions to identify pressing research gaps and potential collaborations. The forum identified several critical research needs categorized into broad themes. First, there is a need to map specific crash scenarios and user needs to appropriate technologies, particularly for VRUs with varying capabilities. Second, Human-Machine Interface (HMI) design requires refinement to ensure accessibility, intuitive operation, and effective communication of system uncertainty to drivers. Third, research must determine how AVs should communicate their intentions to pedestrians and other road users to prevent misunderstandings. Fourth, the quality of drivers’ mental models regarding AV capabilities is a major concern, as inaccurate perceptions can lead to misuse, overgeneralization, and unintended consequences. Finally, the report highlights needs in driver training, education, and driver-state monitoring to ensure systems adapt to user fitness and awareness levels. The significance of this report lies in its structured identification of research priorities to guide future development and policy. By highlighting the disconnect between current technology capabilities and user understanding, the forum underscores the necessity of improving transparency, training, and HMI design. The findings suggest that successful integration of AVs depends not only on technical performance but also on fostering appropriate trust, ensuring accessibility for vulnerable populations, and establishing clear communication protocols between vehicles and all road users. The report serves as a roadmap for researchers and practitioners to coordinate efforts in addressing these complex human factors and safety challenges.
Key finding
Breakout groups synthesized pressing research gaps spanning scenario-to-technology mapping for vulnerable road users, HMI transparency and adaptive automation, external communication of AV intent, mental models and unintended consequences (especially for novice and older drivers), training and education, driver-state monitoring, and roadway infrastructure for mixed fleets; many themes echoed the 2017 forum, indicating persistent needs despite ongoing research progress.
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 (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 | 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.
- acceptance adoption
- ehmi external hmi
- automation surprise
- situational awareness
- automation
- vru facing ehmi
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: observational prevalence
- Synthesis & Review: research agenda
- Theoretical Contribution: conceptual framework