The Validity of Three New Driving Simulator Scenarios: Detecting Differences in Driving Performance by Difficulty and Driver Gender and Age
Bibliographic record
Abstract
OBJECTIVE: We explored the convergent and discriminant validity of three driving simulation scenarios by comparing behaviors across gender and age groups, considering what we know about on-road driving. BACKGROUND: Driving simulators offer a number of benefits, yet their use in real-world driver assessment is rare. More evidence is needed to support their use. METHOD: A total of 104 participants completed a series of increasingly difficult driving simulation scenarios. Linear mixed models were estimated to determine if behaviors changed with increasing difficulty and whether outcomes varied by age and gender, thereby demonstrating convergent and discriminant validity, respectively. RESULTS: Drivers adapted velocity, steering, acceleration, and gap acceptance according to difficulty, and the degree of adaptation differed by gender and age for some outcomes. For example, in a construction zone scenario, drivers reduced their mean velocities as congestion increased; males drove an average of 2.30 km/hr faster than females, and older participants drove more slowly than young (5.26 km/hr) and middle-aged drivers (6.59 km/hr). There was also an interaction between age and difficulty; older drivers did not reduce their velocities with increased difficulty. CONCLUSION: This study provides further support for the ability of driving simulators to elicit behaviors similar to those seen in on-road driving and to differentiate between groups, suggesting that simulators could serve a supportive role in fitness-to-drive evaluations. APPLICATION: Simulators have the potential to support driver assessment. However, this depends on the development of valid scenarios to benchmark safe driving behavior, and thereby identify deviations from safe driving behavior. The information gained through simulation may supplement other forms of assessment and possibly eliminate the need for on-road testing in some situations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| 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".