Rollover stabilities of three-wheeled vehicles including road configuration effects
Bibliographic record
Abstract
This study investigates the rollover stabilities of three-wheeled vehicles including the effects of road configurations. Tripped and untripped rollovers on flat and sloped roads are studied, and a new rollover index is introduced. To explore the unique dynamic behaviours of three-wheeled vehicles, the rollover stability is investigated on the basis of the lateral load transfer ratio, and the proposed rollover index is expressed in terms of measurable vehicle parameters and state variables. In addition to the effects of the lateral acceleration and the roll angle, the proposed rollover index takes the effects of the longitudinal acceleration and the pitch angle into account as well as the effects of banked roads and graded roads. Lateral and vertical road inputs are also considered since they can represent the effects of kerbs, soft soil and road bumps as the main causes of tripped rollovers. Sensitivity analysis is also provided in order to evaluate and compare the effects of different vehicle parameters and different state variables on the rollover stabilities of three-wheeled vehicles. To evaluate the proposed rollover index, simulations are also conducted using a high-fidelity CarSim model for a three-wheeled vehicle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".