Lateral stability analysis of on-road vehicles using the concept of Lyapunov exponents
Bibliographic record
Abstract
This paper deals with the application of the concept of Lyapunov exponents in vehicle lateral stability analysis. The constructive nature of the available methods for calculating Lyapunov exponents as the `invariant' measure of the dynamics is the main advantage of this concept. The vehicle model has two degrees of freedom (2-DOF), and its non-linearity is caused by the third-order polynomial expression between the sideslip forces on the the tires and the tire sideslip angles. In this paper, firstly, the concept of Lyapunov exponents and the standard algorithm to calculate them are presented. Then, by applying this concept to the case of a straight-line motion, the lateral stability region of the vehicle model is estimated. Moreover, the effects of driving conditions such as the vehicle longitudinal velocity, road friction and steering angle on the stability region are investigated. The comparison of the results obtained by the concept of Lyapunov exponents with those given in literature by simulation based methods verifies the effectiveness of this concept for vehicle lateral stability analysis.
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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 | 0.000 | 0.001 |
| 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".