HumanFIRST Driving Simulation Educational Development
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Summary
This report details the development and usability testing of three educational driving simulation scenarios at the University of Minnesota’s HumanFIRST Laboratory. The project was motivated by the need to update the laboratory’s aging simulator infrastructure and to create effective educational tools for addressing prevalent roadway safety issues: speeding, distracted driving, and the challenges of automated vehicle hand-offs. Traffic crashes remain a leading cause of death in the United States, with speeding and distraction being primary preventable factors. Additionally, the integration of automation introduces new risks, such as driver disengagement during automated modes. The authors aimed to leverage the upgraded simulator to provide immersive, safe environments for educating stakeholders and the public on these high-risk behaviors. The research team replaced the legacy simulator, which utilized a 2002 Saturn chassis and outdated computing systems, with a modern system featuring a 2016 Ford Fusion chassis, high-resolution projection screens, and updated motion and audio systems. Using this new platform, the team designed three distinct five-minute educational demonstrations: a rural highway scenario to demonstrate the effects of distraction on lane keeping and speed; an urban grid scenario to highlight collision risks when speeding through intersections with vehicles and bicycles; and an automated vehicle scenario on a highway to illustrate the process of engaging and disengaging autonomous driving modes. These scenarios were designed to be shorter and more interactive than traditional research drives, incorporating on-screen instructions and immediate performance feedback. To ensure the effectiveness of these educational tools, the team conducted a user-centered iterative design process involving five participants. Participants underwent usability tests for each scenario, performing tasks such as texting while driving in the rural scenario, driving at varying speeds (20 mph and 35 mph) in the urban scenario to experience collision risks, and managing automation hand-offs in the highway scenario. Data collection included quantitative metrics like the Rating Scale of Mental Effort (RSME) and System Usability Scale (SUS), as well as qualitative feedback through exit interviews and researcher observations. The testing aimed to evaluate the cognitive workload, user-friendliness, and representativeness of the scenarios. The findings indicated that the usability testing provided critical feedback for refining the simulation designs. Adjustments were made to interface elements, such as chassis positioning and center stack luminance, based on participant input. The report concludes that integrating user-centered design and iterative testing is essential for maximizing the educational impact of driving simulators. By creating engaging, realistic scenarios that highlight the dangers of speeding, distraction, and automation pitfalls, the HumanFIRST Laboratory can effectively reach drivers and stakeholders, thereby contributing to improved roadway safety through targeted education and outreach.
Key finding
Usability testing with five participants revealed that iterative design adjustments to interface features and feedback metrics improved the cognitive intuitiveness and educational effectiveness of the driving simulation scenarios.
Methodology
lab_experiment
Sample size: 5
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_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 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.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- simulator validity fidelity
- situational awareness
- simulator sickness
- simulator training transfer
- automation surprise
- urban rural setting
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, validation psychometrics
- Theoretical Contribution: computational model