Comparison between elderly and young drivers’ performances on a driving simulator and self-assessment of their driving attitudes and mastery
DOI: 10.1016/j.aap.2019.105317
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Summary
This study investigates the driving performance, cognitive competencies, and self-assessment of elderly drivers compared to young drivers, addressing the common perception that aging leads to dangerous driving due to cognitive and visual decline. The research aims to determine if elderly drivers employ compensatory self-regulation strategies to mitigate risks and to assess the accuracy of their self-perception regarding these limitations. The authors argue that previous studies often relied solely on self-reporting or objective measures without integrating both, creating a gap in understanding the relationship between actual cognitive deficits, objective driving behavior, and subjective awareness. The methodology involved 30 participants: 12 elderly drivers (aged 65–78) and 18 young drivers (aged 21–35). Participants completed a self-assessment questionnaire on driving habits and attitudes, followed by cognitive testing using the Trail Making Test (TMT) to measure visual-motor processing and mental flexibility, and the NASA-TLX to assess perceived mental workload. Driving performance was evaluated using a fixed-base driving simulator equipped with eye-tracking technology. The simulator scenarios were designed to solicit specific cognitive functions, including visual field assessment in static and dynamic conditions, spatial orientation and route planning involving unexpected roadworks, and management of critical events such as sudden braking, pedestrian crossings, and complex traffic maneuvers. These scenarios allowed for the objective measurement of reaction times, anticipation, mental flexibility, and self-regulating behaviors like speed adjustment and route avoidance. The results indicated significant cognitive differences between the groups. Elderly participants demonstrated slower visual and motor processing speeds and reduced mental flexibility compared to young drivers, as evidenced by longer completion times on the TMT. While overall mental workload scores were not statistically different, elderly drivers reported higher demands in terms of mental and physical effort. Although the provided text truncates before detailing the specific simulator performance metrics, the study design was structured to reveal how these cognitive deficits translate into driving behaviors. The introduction highlights that elderly drivers often adopt self-regulating strategies, such as avoiding complex situations or increasing safety distances, which may compensate for cognitive declines. However, the study also notes that self-assessment of driving ability is often inaccurate, with elderly drivers showing poor insight into their actual cognitive limitations. The significance of this research lies in its comprehensive approach to linking objective cognitive measures with simulated driving performance and subjective self-assessment. The findings suggest that while elderly drivers may not perform as well as young drivers in terms of raw cognitive speed and flexibility, they can implement compensatory strategies that reduce injury risk. The authors conclude that future interventions should focus on enhancing elderly drivers' understanding of their specific difficulties and helping them develop effective coping mechanisms. By identifying the disconnect between self-perception and actual ability, the study provides a foundation for targeted training or feedback programs aimed at improving safety among older drivers.
Key finding
Elderly drivers showed slower visual-motor processing and reduced mental flexibility than young drivers but employed compensatory self-regulation strategies to manage driving challenges.
Methodology
simulator
Sample size: 30
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 | Crossref | — | — | 1 | 2026-06-06 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| 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 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-06-06 |
| promote | success | — | — | — | 1 | 2026-06-06 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 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.
- older drivers
- cognitive capacity variation
- mci dementia driving
- older driver retraining
- age related perceptual decline
- useful field of view
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: behavioral performance data
- Methodological Resource: validation psychometrics
- Theoretical Contribution: computational model