Effects of Conformal and Nonconformal Vision Enhancement Systems on Older-Driver Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it