Analysis of acoustic data for hybrid and electric vehicles measured on hemi-anechoic chambers
Bibliographic record
Abstract
The Pedestrian Safety Enhancement Act of 2010 requires the National Highway Traffic Safety\nAdministration to conduct a rulemaking to establish a Federal Motor Vehicle Safety Standard requiring an alert sound for pedestrians to be emitted by electric or hybrid vehicles. The goal is to establish performance requirements for an alert sound that allows blind and other pedestrians to reasonably detect a nearby electric or hybrid vehicle.\nThis report documents the analysis of acoustic data for a subset of vehicles measured on hemi-anechoic chambers equipped with a chassis dynamometer. The analyses include data for electric, hybrid, and internal combustion engine vehicles. Ambient noise, measurement repeatability (run-to-run), and measurement reproducibility (site- to-site) are examined. The analysis also includes a limited comparison of indoor and outdoor testing. Indoor test data was provided by Transport Canada. NHTSA’s Vehicle Research and Test Center (VRTC) and the Transportation Research Center (TRC) provided outdoor test data for this analysis.\n
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".