A general rollover index for tripped and un-tripped rollovers on flat and sloped roads
Bibliographic record
Abstract
In order to develop a rollover prevention system, it is essential to have a reliable index that properly indicates real-time rollover danger during vehicle maneuvers. The existing rollover indices are mainly for un-tripped rollovers and have limitations in detecting tripped rollovers. This study introduces a general rollover index (GRI) for the detection of rollover in both tripped and un-tripped cases and also on flat and sloped roads. Based on the lateral load transfer ratio, the proposed index is analytically derived in terms of measurable vehicle parameters and state variables. The general rollover index considers both lateral and vertical road inputs and thus can indicate tripped rollovers in the instance of curbs, soft soil or bumps. Sensitivity analysis for the proposed index is also provided to evaluate the effects of different vehicle parameters and different state variables on tripped and un-tripped rollovers. The introduced index can be used not only for the development of active rollover prevention systems, but also for rollover analysis and design of vehicles. The performance of the introduced general rollover index is validated through simulations using a high-fidelity CarSim model for a SUV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".