Assessing lateral stability of long combination vehicles using stochastic modeling of varying operating conditions and parameter uncertainties
Bibliographic record
Abstract
Long combination vehicles (LCVs) have been increasingly applied for highway freight transportation due to their improved fuel economy and reduced greenhouse emissions. However, LCVs exhibit poor high-speed lateral stability owing to their multi-unit structures, large sizes, and high center of gravity (CG). Given the unique dynamic features of these large vehicles and varied operating conditions, LCVs’ lateral stability is difficult to predict. To date, simulation has been widely used to evaluate the dynamic performance of road vehicles. High fidelity simulations may provide excellent insights into the dynamics features of LCVs under a predefined operating condition. However, under varying operating conditions and in the presence of vehicle parameter uncertainties, it is difficult to use simulation for reasonably evaluating the lateral stability of LCVs. To address this problem, we propose an effective simulation method, which consider different road conditions and trailer payload variations using Monte Carlo based stochastic modeling method. The numerical simulations are executed on a co-simulation platform, consisting of TruckSim for LCV modelling, MatLab/SimuLink for updating vehicle model and operating condition, and Python for data management and analysis. Simulation results demonstrate the effectiveness of the proposed stochastic modeling method.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".