Effects of Conformal and Nonconformal Vision Enhancement Systems on Older-Driver Performance
Bibliographic record
Abstract
The effects of two types of vision enhancement system (VES) displays on younger- and older-driver performance were systematically examined in various contexts. Younger and older drivers used either a conformal or a nonconformal VES display while driving in a fixed-base driving simulator. Within each block of trials, traffic scenarios were used to test driver performance: everyday driving, intersection approaches, emergency events, and VES failure. Conformal imagery directly highlighted aspects of the traffic environment, whereas nonconformal displays were coupled to environmental events but not superimposed on them. In all driving scenarios, conformal displays had a performance advantage over nonconformal displays. These advantages, however, depended on what was highlighted and whether a highlight covered or obscured important information about the environment. The perceived benefits of VESs are in situations where visibility is limited by weather (e.g., fog, snow, or rain), time of day (e.g., night or dusk), or roadway geometry (e.g., curves or railway crossings). Implications of the results for the design of conformal and nonconformal VESs and for future research are discussed.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".