Validation of a driving simulator for research into human factors issues of automated vehicles
DOI: 10.33492/jacrs-d-18-00279
archive: archived pipeline: cataloged verified
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Summary
This study addresses the need to validate driving simulators for research into human factors associated with automated vehicles, specifically focusing on Level 3 automation where drivers must remain ready to resume control. While simulators offer a safe and economical environment for testing, their artificial nature raises concerns about behavioral validity. The authors aimed to determine if the Monash University Accident Research Centre (MUARC) automation driving simulator could reliably reproduce driver responses comparable to real-world conditions, a critical step before using the simulator for broader investigations into driver acceptance and transfer of control. The experimental design involved 20 participants who completed both on-road drives in an instrumented Holden Commodore and simulated drives in the MUARC simulator. To ensure comparability, participants sat in the passenger seat in both conditions, with a researcher driving the vehicle. Participants were instructed to imagine they were in control of a Level 3 automated vehicle and to rate their willingness to resume control (WTRC) and perception of safety (POS) at various decision points using a tablet. The routes were matched for length, road conditions, traffic density, and situation complexity. Statistical analysis using Generalised Estimating Equations compared the ratings between the two environments across various events, such as merging, roundabouts, and urban driving. The results confirmed the relative behavioral validity of the simulator for the majority of tested scenarios. There were no significant differences between on-road and simulator ratings for overall willingness to engage, perception of safety, or specific events like free driving on freeways, roundabouts, give-way signs, congestion, stopped buses, and pedestrians. However, significant discrepancies were found for merging onto the freeway and uphill urban driving. For merging, the simulator event was less complex and demanding than the on-road counterpart, leading to different risk perceptions. For uphill driving, differences in road width and lane configuration between the two environments influenced safety ratings. Additionally, minor differences emerged for medium traffic density and situation complexity on freeways, attributed to challenges in perfectly replicating realistic freeway dynamics in the simulator. The study concludes that the MUARC simulator is a valid tool for human factors research in automated driving, provided its limitations are acknowledged. The findings suggest that simulators accurately represent low-to-medium complexity events but may struggle with high-mental-workload scenarios or situations requiring precise environmental replication, such as complex merging or specific road geometries. These insights will guide the design of future studies, ensuring that simulator-based research on automated vehicle human factors remains credible and transferable to real-world applications. The authors emphasize the need for further investigation into how simulator representations of high-workload situations correlate with on-road mental workload and perceived risk.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-07 |
| archive | success | canonical_url | — | — | 1 | 2026-06-09 |
| extract | success | cached | — | — | 2 | 2026-06-09 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| promote | success | — | — | — | 1 | 2026-06-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 8 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-09 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- simulator validity fidelity
- acceptance adoption
- simulator training transfer
- automation
- simulator sickness
- situational 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: validation psychometrics, tool software
- Theoretical Contribution: computational model