The influence of cognitive ability on older adults’ ability to take over driving control from an automated vehicle
Bibliographic record
Abstract
Abstract Background Automated vehicles (AVs) hold potential promise in sustaining the safe mobility of older adults whose driving is compromised due to cognitive impairments. However, current AVs have an operational limit and when this limit is reached, the driver is expected to promptly take over driving control. The timely performance of takeover task draws upon cognitive resources that can be compromised in people with dementia (PwD) and people with Mild Cognitive Impairments (PwMCI). Therefore, this study investigates the abilities of PwD and PwMCI to perform an AV driving takeover task. Method Population. 20 participants per three groups of PwD, PwMCI, and older adult controls will be recruited. Currently, the data collection for the controls and recruitment for the PwD and PwMCI are on‐going. The present results are based on seven control participants who have completed the study to date. Study Design & Setting. Participants completed a battery of sensory and cognitive tests and performed takeover tasks in response to an audio‐visual alert in a simulated AV using a high‐fidelity driving simulator. Each participant performed takeovers in eight driving conditions varying in lighting level (day/night), road structure (straight/curved), and speed limit (60/120 km/h). Measures. To characterize participants’ takeover abilities, their response time was measured as the time interval between the audio‐visual alert and uptakes in speed (longitudinal takeover time), and changes in steering wheel angle (lateral takeover time). Result A correlation analysis was conducted between each of takeover time measures and cognitive and visual tests. MoCA scores showed a significant, negative correlation with lateral takeover time (r k = ‐0.85, p = .015, 95% CI [‐0.98, ‐0.27]). To investigate the effects of driving condition on takeover time, pairwise Kruskal‐Wallis tests were conducted on measures of takeover time as separated by driving condition. Significant differences were only observed in lateral takeover time in the curved, high‐speed condition compared to other conditions (χ2(3) = 29.46, p < .001). Conclusion This study will help clarify the associations between older adults’ cognitive abilities and takeover abilities in AVs which can, in turn, inform the guidelines around the safe use of AVs by PwD and PwMCI.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".