Changing Lanes in a Simulator: Effects of Aging on the Control of the Vehicle and Visual Inspection of Mirrors and Blind Spot
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine lane change strategies in active younger and older drivers. Visual inspection of mirrors and the blind spot and the control of the vehicle were documented in a simulator environment. METHODS: Younger (n = 10, 21-31 years) and older (n = 11, 65-75 years) active drivers drove through a continuous simulated environment including urban and rural sections. The scenario included events where, to negotiate a secure lane change, the driver needed to look at 3 regions of interest (ROI): (1) the rearview mirror, (2) the left side mirror, and (3) the left blind spot. The lane change maneuvers were necessary to avoid a vehicle parked halfway in the rightmost lane that was partially or completely blocking the lane or for overtaking a slower moving vehicle. RESULTS: Compared with younger drivers, older drivers showed a reduced frequency of visual inspection toward the rearview mirror and the blind spot. Also, though the older drivers showed a constant frequency of visual inspection across the 2 types of driving maneuvers, the younger drivers increased their frequency of inspection when overtaking a slower vehicle. Control of the car was mostly similar for both groups. CONCLUSION: A better knowledge of the drivers' visual search strategies when changing lanes could help in identifying suboptimal strategies at-risk of causing crashes and also serves to develop retraining programs.
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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 | 0.003 |
| 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 | 0.000 | 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".