Pilot tests of a seat belt gearshift delay on the belt use of commercial fleet drivers : traffic tech.
Bibliographic record
Abstract
Wearing a seat belt has been shown effective in avoiding\nor reducing serious injury due to traffic crashes. While\nbelt use rates in the United States increased from under\n60% in 1994 to 83% in 2008, a substantial number of drivers\nstill drive unbelted. Current efforts to increase seat belt\nuse focus primarily on high-visibility enforcement campaigns,\npublic education, and seat belt reminder systems.\nNHTSA investigated a novel engineering approach using\na gearshift delay to increase belt use among commercial\ndrivers in the United States and Canada.\nA car with automatic transmission cannot shift into gear\nif the vehicle senses that the brake lights are not lit, meaning\nthat the driver must have a foot on the brake pedal.\nThis safety feature was designed to prevent vehicles from\naccelerating unintentionally after being placed into drive\nor reverse. For this study, a relatively simple change to the\nsoftware code allowed the system to make an additional\ncheck before the vehicle can be placed into drive. Specifically,\nat the same time as the brake light check occurs,\nthe vehicle’s computers checked to see if the driver was\nbelted. If the driver was not belted, a gearshift-seat belt\ndelay system prevented the driver from shifting out of\npark for several seconds.\nThe timing of the reminder system was designed to prompt\ndrivers before they started driving to avoid the possibility\nof stimulus overload as they negotiated their way into traffic,\na trip segment associated with high cognitive demand.\nThis timing should allow most drivers sufficient time to\nbuckle up, thereby avoiding the prompt. It also had the\nsafety benefit of prompting unbuckled drivers before they\nplaced their vehicles in motion.\n
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".