Evaluating the Roll Stability of Articulated Vehicles Using a Phase-Plane Analysis Method
Bibliographic record
Abstract
An innovative phase-plane analysis method is proposed to assess the roll stability of articulation vehicles. It is well know that the roll instability of articulated vehicles is one of the most serious problems resulting in loss of life and property for drivers. Hence, it is necessary to develop active anti-roll systems to enhance the roll stability of articulated vehicle systems. In order to actuate the active anti-roll control for the articulated vehicle system, effectively threshold values should be determined. Conventionally, vehicle units’ lateral accelerations are used as the roll-over threshold values for active anti-roll control of articulated vehicles. Considering distinguished configurations and unique dynamic features of articulated vehicles, it is questionable whether the lateral-acceleration-based roll-over threshold of single vehicle is effective to evaluate the roll stability of articulated vehicles. In order to address the problem, case studies will be conducted to assess the roll stability of articulated vehicles using the phase-plane method. To this end, this paper will select a car-trailer system, which is represented by a nonlinear vehicle model generated using the CarSim software package. The phase-plane analysis method is used to examine the following relationships between: 1) the leading unit’s roll angle and roll angular velocity (ϕ – dϕ/dt) and 2) the trailing unit’s roll angle and roll angular velocity (ϕ′ – dϕ′/dt). Built upon the conventional phase-plane analysis method for single-unit vehicles, an innovative phase-plane analysis technique is developed in order to effectively assess the roll stability of articulated vehicles. The applicability and effectiveness of the newly developed technique is examined and demonstrated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".