Simulated driving behavior over the adult age span
Bibliographic record
Abstract
Background: Motor vehicle accidents remain a leading cause of accidental death worldwide. Death and injury rates are particularly high for both young inexperienced drivers and elderly drivers. Understanding the behavioral changes that are associated with maturation and aging could inform assessments of driving performance and lead to new measures identifying at-risk drivers. To shed further light on such effects, this study aims to characterize simulated driving behavior across and within age groups using a large driving simulation dataset. Methods: The analyzed dataset consisted of 112 participants [47/112 (42%) female] between the ages of 17 and 85 (average ± standard deviation: 54 ± 22 years). Participants performed navigation in scenarios modeled after the standard licensing test of Ontario, Canada, which included a series of turns at intersections with different levels of complexity (e.g., involving oncoming traffic or pedestrians) and levels of distraction (requiring auditory responses to common-knowledge questions). Behavioral metrics were defined and investigated not only for the full completion of each task but also based on common subtasks (e.g., braking at an intersection), which were then compared across and within age groups (young, middle-aged, old). Results: Overall, young adults behaved similarly to middle-aged adults for basic tasks but showed differences during traffic navigation subtasks when distracted, such as starting to decelerate significantly later when approaching intersections. Old drivers, on the other hand, drove at lower average speed, stopped earlier at intersections, and left increased distances to pedestrians, but required significantly more time to complete the driving tasks. Conclusion: With rich detail arising from intra-task quantification, the results were consistent with and additive to previous literature showcasing that compared to middle-aged adults, young adults showed performance suggestive of riskier driving behavior, and old adults showed performance suggestive of caution consequent to declining driving ability. In particular, the intra-task quantification revealed that the driving of young adults was more impacted by the presence of distraction (e.g., delayed decelerating), whereas old adults prioritized safe driving (e.g., correctly braking at intersections) over responding to distractions. The study may be used as motivation for future studies of driving safety and accident prevention, and informed assessment of governmental regulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".