Measuring the Applicability of Intersection-Based Older Driver Training Programs
Bibliographic record
Abstract
Older drivers remain overrepresented in intersection crashes. Previous evidence suggests that the primary reason for this lies with their lack of scanning for potential threat vehicles while entering stop-controlled intersections. More so, secondary glances prove critical when the conditions obscure potential threat vehicles while approaching the intersection. Currently, simulator-based older driver training programs have proven effective in increasing the frequency of secondary glances taken by older drivers up to 2 years following the training. However, both the need for a full-scale driving simulator and participant dropout rates because of simulator sickness within training programs continue to limit the applicability of these alternatives. This study used a series of micro-scenarios to train older drivers in secondary glances, thus reducing the potential for participant dropouts resulting from simulator sickness. In addition, driver immersion levels varied across multiple training platforms, ranging from low to medium . A total of 91 participants between 67 and 86 years old were assigned to one of five groups. Three groups were provided active, secondary glance training on a driving simulator (one on a low immersion simulator and two on medium immersion simulators), a fourth group was provided passive training using a PowerPoint presentation, and the last group was a control with no training. Following training, all participants were evaluated in their personal vehicles while wearing head-mounted cameras. The medium immersion group resulted in the highest percentage of secondary glances (82%), whereas the control group resulted in the lowest percentage (42%). The results provide evidence to suggest that the training programs using micro-scenarios in medium and low immersion simulators can increase the frequency of secondary glances without having high dropout rates caused by simulator sickness.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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".