Combining Virtual CRASH and MADYMO to reconstruct motor vehicle collision dynamics and assess injury risk to occupants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We evaluated Virtual CRASH motion output as input to MADYMO for assessing risk of injury to rear seat occupants of a vehicle involved in a three-vehicle collision. The vehicle accelerometer records captured by the vehicle’s EDR served as a reference. We determined that Virtual CRASH can faithfully reproduce crash scene evidence and general vehicle motion, but it overestimates peak accelerations during impacts, which would lead to overestimating the risk of injuries. Although EDR records provide a reliable input for MADYMO, since they are only 0.3 s in duration and represent vehicle motion in the reference frame of the vehicle, their utility in reconstructing events following an impact is limited. We demonstrate the utility of combining Virtual CRASH with MADYMO to reconstruct the entire sequence of events during the collision and accurately assess the risk of injury to the rear seat occupants of the most damaged vehicle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it