Vertical Occupant Loading in Car Crashes; Test Methods and Countermeasures
Bibliographic record
Abstract
Vertical loading can cause thoracic and lumbar spine injuries to a car occupant. Crashes potentially causing occupant vertical loads include; rollover events or free flying events when the car lands on its wheels, and run off road events when the car goes into the ditch and collides with an embankment. To date, there is no standardized test method evaluating this occupant loading mechanism. The aim of this study was to develop test methods addressing vertical occupant loading for car occupants and to evaluate countermeasures for reduction of such loads. Based on real world run off road crashes, representative test track methods were developed. These complete vehicle test track methods were used to provide input to a simplified and repeatable rig test method. The rig test method comprises a dummy positioned in a seat attached to a frame and exposed to a vertical acceleration. Vertical pelvis acceleration is monitored, as an indication of potential loads through the spine. Two different seat designs are compared. The modified seat concept includes a deformation element which is built into the rear part of the seat connection to the seat frame. The deformation element allows for a controlled deformation of up to 25 mm. The space under the seat is cleared to allow for total occupant movement up to 150 mm. During this movement, energy is absorbed by the deformation element as well as the springs and seat cushion material. Compared to the reference production seat, the dummy pelvis vertical acceleration is reduced by 25-32% in the situations tested in this study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".