A novel tripped rollover prevention system for commercial trucks with air suspensions at low speeds
Bibliographic record
Abstract
This paper presents a novel system to avoid tripped rollovers at low-speed operations for commercial vehicles with air suspension systems. This is of particular significance since truck rollovers have become a serious road safety problem, which usually lead to severe injuries and fatalities. Several active anti-rollover systems have been proposed in the past two decades; however, most of them focus on untripped rollover prevention instead of the tripped rollovers. Up to now, very few pieces of literature discuss the approaches that are used to avoid tripped rollovers of trucks. Furthermore, the air suspension is widely used for commercial vehicles, thus it provides an opportunity to prevent rollovers when properly manipulated. Therefore, a novel tripped rollover prevention system is proposed for trucks at low-speed operations with air suspensions. A roll dynamics model with an air spring is built to investigate the dynamic behavior and the time response of the whole system. More importantly, the feasibility of this new anti-rollover system is discussed and verified by the co-simulations in TruckSim and MATLAB/Simulink under two possible tripped rollover conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".