Older and Younger Driver Performance at Complex Intersections: Implications for Using Perception-Response Time and Driving Simulation
Bibliographic record
Abstract
Older drivers are at increased accident risk at intersections for a variety of maneuvers. To examine why, a study was conducted to assess older driver performance at complex intersections in a driving simulator. The University of Calgary Driving Simulator was used to test healthy older drivers (65-83, M = 71.4) and younger drivers (19-22, M = 20.7). Critical scenarios included the sudden appearance of a pedestrian in an intersection, a lastsecond yellow light, an unexpected change during a left, and a vehicle violating a stoplight. Older drivers had significantly higher perception response times (PRT) than younger drivers for the latter three of the four intersection scenarios. Analysis of specific maneuvers also revealed qualitative response differences between young and old groups. In contradiction to expectation, more older drivers ran the yellow light than younger drivers. The capability of older drivers to respond under time constraints is implicated. The utility of driving simulators to assess older driver performance at intersections was limited by the prevalence of simulator sickness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".