Crash pulse data from event data recorders in rigid barrier tests
Bibliographic record
Abstract
In recent years, major advances in field data collection and analysis have been achieved through the integration of real-world vehicle crash data captured by on-board, electronic, event data recorders (EDRs). For some time, data has been publicly available from EDR’s in General Motors, Ford, and Chrysler vehicles. Recently, Toyota has provided a proprietary tool through which researchers can access EDRs installed in their vehicles. The current study looks at the crash data that are available and explores the accuracy of this information. The study uses a series of staged collisions with EDR-equipped vehicles and compares data downloaded from these devices to equivalent information captured by laboratory instrumentation. Full-frontal crash tests, conducted by Transport Canada, at 48 km/h into a rigid barrier are used. The results show generally good agreement between the two datasets, with some limitations in the EDR-reported data being noted. These comparisons of data obtained from on-board vehicle EDRs, with equivalent information collected using sophisticated laboratory instrumentation, provide a valuable measure of confidence in the use of similar data collected from real-world events.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".