Driving simulator scenarios and measures to faithfully evaluate risky driving behavior: A comparative study of different driver age groups
DOI: 10.1371/journal.pone.0185909
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study investigates the relationship between mental workload, driver age, and risky driving behavior, aiming to identify optimal driving simulator scenarios and performance measures for evaluating different age groups. The research addresses a gap in driving simulation methodology: while simulators are valuable for assessing safety, previous studies often failed to account for how varying levels of task complexity (mental workload) affect the sensitivity of driving measures. The authors sought to determine which scenario complexity best highlights age-related differences in driving ability and whether novel perceptual-cognitive tests could predict crash risk. The researchers conducted a cross-sectional study using a high-fidelity motion-based driving simulator with 115 licensed drivers divided into three groups: young inexperienced drivers (18–21 years), experienced adults (25–55 years), and experienced older adults (70–86 years). Participants completed three scenarios designed to induce low, moderate, and high mental workload: highway, rural, and urban environments, respectively. In addition to driving performance, participants underwent a 3-Dimensional Multiple Object Tracking (3D-MOT) test to assess their perceptual-cognitive capacity. The study analyzed 18 specific driving measures, including established metrics like crash frequency and lateral position deviation, as well as novel measures capturing abrupt braking, steering changes, and anticipation behaviors. Statistical analyses included bivariate correlations to reduce redundant data and comparisons across age groups and scenarios. The results indicated that moderate scenario complexity (the rural scenario) was the most effective at highlighting well-documented differences in driving ability between age groups and eliciting naturalistic driving behavior. Extreme scenarios—either too simple (highway) or too complex (urban)—caused drivers to become under-aroused or overloaded, respectively, reducing the sensitivity of performance measures. Furthermore, several novel driving measures provided non-redundant information that complemented traditional metrics. Crucially, the 3D-MOT test proved to be an effective predictor of elevated crash risk and decreased naturally adopted mean driving speed, particularly among older adults. This suggests that an individual’s ability to process complex visual information is strongly linked to their driving safety outcomes. The significance of this study lies in its methodological contributions to driving simulation research. It demonstrates that scenario design must carefully balance mental workload to accurately assess driver competence, as extreme task demands can obscure true performance differences. Additionally, the findings support the integration of perceptual-cognitive assessments, such as 3D-MOT, into driving risk evaluations. These insights suggest that future research and clinical assessments should prioritize moderate-complexity scenarios and incorporate cognitive tracking tests to better predict and mitigate risky driving behaviors, especially in aging populations.
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-25 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- cognitive capacity variation
- older drivers
- novice drivers
- mental demand
- simulator validity fidelity
- workload measurement
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
- Theoretical Contribution: theory or model, computational model