Age-related effects of executive function on takeover performance in automated driving
DOI: 10.1038/s41598-022-08522-4
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Summary
This study investigates the relationship between age-related declines in executive function (EF) and takeover performance in automated driving, specifically when drivers are engaged in non-driving related tasks (NDRTs). As the elderly population grows, highly automated vehicles offer a solution for maintaining mobility, yet elderly drivers often exhibit worse performance after a takeover request (TOR). The authors argue that chronological age alone is insufficient to explain these differences and that underlying cognitive factors, particularly executive functions, are critical determinants of driving safety. The research aims to identify correlations between specific EF components—updating, inhibition, and shifting—and driving stability during TOR scenarios. The methodology involved 70 licensed drivers (35 young, aged 27.8 years; 35 elderly, aged 72.8 years) recruited from the community. Participants first completed computerized cognitive tasks to assess EF components: an n-back task for updating, a Simon task for inhibition, and a task-switching paradigm for shifting. Principal component analysis (PCA) was used to extract latent EF factors from these task performances. Subsequently, participants engaged in a simulated driving experiment using a desktop simulator. The driving task involved a level 3 automation scenario where participants encountered a stationary obstacle requiring a lane change after a TOR. The experiment employed a 2 (age) × 2 (NDRT engagement) mixed design. The NDRT was an auditory 0-back task designed to impose cognitive load without visual distraction. Driving performance was measured by the standard deviation of the steering wheel angle (sdSteer) and minimum time-to-collision (TTC). Results indicated that older participants had significantly lower executive function abilities, showing slower reaction times and lower accuracy in updating and shifting tasks compared to younger drivers. In the driving simulation, elderly drivers were less stable and more conservative, particularly when engaged in NDRTs. Crucially, the study found a significant correlation between executive function components and lateral driving stability (sdSteer). Specifically, the PCA-derived EF factors explained variance in takeover performance, suggesting that cognitive decline directly impacts the ability to maintain vehicle control during critical transitions. The elderly group exhibited higher steering variability, indicating reduced stability, while maintaining larger safety margins (higher TTC), reflecting a conservative driving strategy. The significance of these findings lies in demonstrating that age-related differences in takeover performance are mediated by specific executive function deficits rather than age alone. This highlights the importance of considering cognitive profiles when designing driver screening tests or human-machine interfaces for automated vehicles. By identifying updating and shifting as key cognitive predictors of driving stability, the study provides evidence for developing targeted interventions or adaptive systems that account for individual cognitive capacities, thereby enhancing safety for elderly users of automated driving technologies.
Key finding
Older drivers exhibited lower executive function abilities and less stable takeover performance, with a significant correlation found between executive function scores and lateral driving stability.
Methodology
simulator
Sample size: 70
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 scout_discovery on 2026-05-08.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | partial | scout | — | — | 2 | 2026-05-08 |
| archive | success | unpaywall | — | — | 1 | 2026-06-04 |
| extract | success | cached | — | — | 3 | 2026-06-15 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | semantic_scholar | — | — | 2 | 2026-06-04 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-15 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-04 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-15; verification: verified.
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- Empirical Findings: behavioral performance data
- Methodological Resource: measurement protocol
- Theoretical Contribution: computational model