Simulation Research on Highway Driving Stability Early Warning System Under Crosswind Conditions
Bibliographic record
Abstract
Aiming to address the issue of highway traffic safety under crosswind conditions, this study utilizes the CarSim/TruckSim simulation platform to systematically analyze the effects of crosswind speed and direction on the driving stability of cars and trucks. A safety speed model is developed for different road adhesion coefficients, and a highway crosswind warning system is designed. Through 625 simulation experiments, the study reveals that lateral offset, lateral acceleration, and lateral load transfer rate are significantly influenced by vehicle speed, wind speed, wind direction, and road adhesion coefficient, with the road adhesion coefficient identified as the key factor. Separate safety speed models for cars and trucks under various road and crosswind conditions are established. The findings are as follows: for cars, crosswind speed and direction impact safe driving speed only when the road adhesion coefficient is 0.1. Overall, for constant wind direction, safe driving speed decreases as wind speed increases; at a constant wind speed, safe driving speed gradually decreases as wind direction shifts from 45° to 135°. For trucks, when the road adhesion coefficient ranges from 0.1 to 0.9, the relationship between safe driving speed, wind speed, and wind direction mirrors that of small cars. However, the critical safety speed for trucks is 40% lower than that for cars under identical crosswind conditions when the road adhesion coefficient is 0.1. Based on the Visual FoxPro platform, which enables real‐time early warning decision‐making through the integration of the safety speed model, the highway driving stability early warning system (comprising information collection, processing, and release modules) is applied to the Zhengzhou Taohuayu Yellow River Highway Bridge case. The system is verified to significantly enhance highway driving safety and provides technical support for dynamic safety management and control of highways under crosswind conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".