Conditionally Automated Vehicle Driving Performance Across Different Cognitive Groups
Bibliographic record
Abstract
BACKGROUND: Driving cessation due to cognitive decline impacts independence and quality of life. Conditionally Automated Vehicles (CAVs) may extend safe mobility for older adults with cognitive impairments by handling most driving tasks while requiring driver control in emergencies. However, the extent to which critical safety measures such as reaction time and the ability to takeover control of the vehicle during emergencies, differ across persons with different levels of cognitive impairment remains underexplored. This study addresses this gap by comparing the driving performance of older adults with normal cognition, subjective cognitive decline (SCD), and cognitive impairment (mild cognitive impairment and very mild dementia). METHOD: Participants included those with normal cognition (n = 10, age: 74.4 ± 5.36), SCD (n = 10, age: 75.8 ± 5.37), and cognitive impairment (Clinical Dementia Rating = 0.5, n = 7, age: 75.4 ± 6.45). In a high-fidelity driving simulator, they completed four takeover requests (TORs) during a ten-minute CAV drive, assuming control when operational limits were reached on straight and curved roads at urban and highway speeds. Takeover performance (reaction time, steering angle changes, and lane deviation) was analyzed using repeated-measures ANOVA to assess cognitive group, road geometry, and speed effects. RESULT: No significant main effect of cognitive group or interactions with cognitive group on takeover performance were observed, though low statistical power may have limited detection. Road geometry influenced all groups, with faster reaction times, larger steering angle changes, and greater lane deviations on curved than straight roads. Road speed affected reaction time, with slower reactions in urban than highway sections. Although road speed did not significantly impact steering angle changes or lane deviations, its interaction with road geometry showed that curved roads at highway speeds resulted in greater steering angle changes and lane deviations than urban speeds. These findings highlight the impact of road geometry and speed on takeover performance, with high-speed curved roads posing greater challenges for all cognitive groups. CONCLUSION: While takeover performance did not differ across cognitive groups, suggesting CAVs may support older adults with cognitive impairments, the small sample limits confidence. Further research with larger datasets is needed to ensure safe and effective CAV use for this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".