Evaluating Self-Unlocking Doors in Rollover Accidents using a Shock Testing Machine
Bibliographic record
Abstract
Automotive manufacturers often rely upon features such as automatic locking to enhance the security and crashworthiness of doors in rollover accidents. This can be verified in warnings conveyed to vehicle owners through some owner's manuals. At the present time, there are no requirements on the dynamic performance of door locking systems within the Federal Motor Vehicle Safety Standards (FMVSS), although some static inertia requirements exist for latch systems. Field accident investigation and laboratory testing has revealed that some locked doors can self-unlock in rollover accidents when a vehicle sustains a roof impact. Using standard laboratory shock testing machinery, the acceleration boundaries required to trigger self-unlocking have been mapped for some sample doors. Impact pulses of surprisingly low levels of acceleration, when combined with sufficient duration have been found to trigger this response. Furthermore, two entirely different failure mechanisms have been identified and documented thus far. One failure mode results directly from inertial triggering of the locking system; this mode is anticipated by FMVSS. The second failure mode is the unexpected consequence of the vibratory response of the lock system linkage. This failure mode has not yet been recognized in the safety literature. This paper presents the findings of a field accident investigation where self-unlocking was believed to have occurred as well as test data from doors exhibiting the two different self-unlocking mechanisms.
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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 | 0.002 |
| 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) | 0.003 | 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".