Restitution and Crash Pulse Duration from Low-Speed Crash Tests
Bibliographic record
Abstract
Frontal crashes are the most common crash mode in the US vehicle fleet, and a large proportion of these crashes are “fender-benders” or low-speed collisions. This, among other considerations, led the Insurance Institute for Highway Safety (IIHS) to conduct a series of low-speed front and rear bumper impact tests. These crash tests have been performed on passenger vehicles manufactured by various manufacturers since 1970 and continuing through the 2009 model year. Test data and video for individual tests are available through IIHS’s online data portal, most extensively for model years 2007 to 2009. While IIHS’s test protocol varied over the years, these tests specified, in part, a full engagement impact of the tested vehicle into a rigid, bumper-shaped barrier covered with an energy absorber. Although IIHS reported the closing speed for each test, they did not report the separation speed or crash pulse duration. These values have been determined, in the current study, by analyzing the high-speed video provided for each test by IIHS. While restitution (which characterizes the relationship between impact and separation speeds) and crash pulse duration for low-speed impacts have been previously discussed in the literature, a comprehensive analysis of low-speed crash test data has not been published. Furthermore, since the IIHS test data consisted of both underride and full engagement tests (as classified by IIHS), this paper will also evaluate the difference in restitution and crash pulse duration between these impact configurations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".