A Simulation Study for Lateral Stability Control of Vehicles on Icy Asphalt Pavement
Why this work is in the frame
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Bibliographic record
Abstract
Black ice is an ice layer formed by freezing rain or accumulated water on the asphalt pavement surface in cold weather. This ice layer completely shields the texture structure of the pavement and destroys the original microstructure. The direct contact between the automobile tire and the ice surface leads to a sharp decrease in the adhesion coefficient, so the automobile is prone to lateral instability on the icy pavement. In this paper, the simulation model of the icy pavement is established in Matlab/Simulink to verify the control effect of the lateral stability controller based on the Electronic Stability Program under two steering limit conditions. The results show that the vehicle without a lateral stability controller will lose stability and sideslip even when it is steering at low speed on the icy pavement, and the lateral stability controller can effectively control the yaw rate of the vehicle when it is steering, which greatly reduces the offset of the sideslip angle of the centroid and inhibits the lateral acceleration exceeding the ice surface limit, which improves the maneuverability and stability of the vehicle under the freezing limit condition. The application of the controller is of great significance to improve the driving safety of the regional asphalt pavement. Due to the low adhesion coefficient of the icy pavement and the limited braking force and additional yaw moment of the tire provided by the adhesion force, the vehicle with a lateral stability controller is still likely to lose stability under the critical condition of medium or high-speed single shift line.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it