Examining Driver Situation Awareness in the Takeover Process of Conditionally Automated Driving With the Effect of Age
Bibliographic record
Abstract
Background: Previous research has shown that drivers from different age groups demonstrated different Situation Awareness (SA) levels in the takeover process of conditional automated driving, where drivers need to collect surrounding information, perceive hazardous events, and make correct actions. Objective: To further explore the reason for this age-related difference, we investigated the SA from two different measures, Hazard Perception Time and scores from delayed SAGAT questions. The Hazard Perception time reflects both cognitive processes and ocular movements. The delayed SAGAT test was completed after each scenario, revealing how drivers perceived and comprehended information. Method: This study recruited drivers from three age groups, young, middle-aged, and older drivers. Especially, this study recruited old-old drivers (75+ years old), who have more severe cognitive impairments compared to young-old drivers (65 - 75 years old). Each driver went through 12 driving scenarios with different road types (highway straight, highway curved, city straight, and city curved) and driver types (manual only, autopilot only, and autopilot with non-driving-related Tasks). Results: The result showed that older drivers had significantly higher Hazard Perception Time and statistically equivalent SAGAT scores compared to the other two age groups. Also, curved roads led to significantly higher Hazard Perception Time for older drivers. Conclusion: Older drivers’ decreased SA mainly came from the delayed ocular movement, which was moving their gaze toward hazardous events in the current experiment. Researchers should be more mindful regarding the slowed ocular movement of older drivers when designing takeover systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".