A Virtual Prototyping Toolkit for Assessment of Child Restraint System (CRS) Safety
Bibliographic record
Abstract
Computational modeling continues to play an increasingly significant role in the design of more effective vehicle crash safety systems. Models configured with sophisticated computer analyses permit researchers to perform extensive “what-if?” exploratory studies at a fraction of the cost and time that would be required by physical testing alone. Presently, our research team is developing a modeling and analysis capability that will provide child restraint system (CRS) engineers, designers, and analysts a validation tool that will supplement conventional engineering results attained from sled testing, which is often timely and costly. Supplementing these physical tests and digital modeling capabilities is the newly developed NYSCEDII CRS Visualization Module (NCVM), which allows a user to immersively visualize the MADYMO-calculated automotive crash simulation imagery. Depicted are the motion of, and interactions between, the CRS shell, human “dummy”, harness and latch belt assemblies, and applicable vehicle cabin-interior surfaces and structure; and nodal finite element Von Mises color stress contours for the CRS shell and its attendant restraint straps. Supplemental NCVM features include: plotted instantaneous body segment acceleration-time responses; dummy displacements visually tracked using on-screen reference markers - to be tracked as a function of time; forwards or backwards animation capability; and stereo viewing, using anaglyphic stereo, to convey a sense of depth and immersion. This paper demonstrates the utility of the NCVM using a combination conventional/finite element system model of a recent-production child restraint system (CRS) and its three-year-old dummy occupant in a modified FMVSS 213 sled test environment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.127 | 0.024 |
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".