Evaluation of the Risk of Truck Platoon under Crosswind regarding the Lateral Displacement on Horizontal Curves
Bibliographic record
Abstract
Driving safety on horizontal curves under crosswinds has always been a concern for researchers. However, previous studies focused on single vehicles, especially single trucks. The safety of the truck platoon under crosswinds on horizontal curves was rarely examined. To fill such a gap, this study establishes a model for evaluating the safety of truck platoons under crosswinds on curves. After obtaining aerodynamic coefficients, a cosimulation model of a three-truck platoon is established with Trucksim and Simulink. The influencing factors on the lateral displacement of the truck platoon were analyzed, and the safety of the truck platoon under crosswinds on horizontal curves was then quantified with the response surface method. The results show that the fully autonomous platoon is safer than the human-led platoon. In a three-vehicle platoon, the smaller the lateral displacement of the truck, the further back the truck, and the lateral displacement of the vehicle increases with the loading of the truck. The safety index is computed to quantify the reliability of the truck platoon. The results indicate that the safety index increases with a decrease in the mean wind velocity and mean vehicle velocity and increases with an increase in the mean radius. When crosswinds come from the inside of the curve, the safety index increases with an increase in the mean superelevation; for crosswinds from the outside of the curve, the safety index rises with the drop of superelevation. The proposed approach can quantify the safety level of truck platoons under crosswinds on horizontal curves, and the results provide guidance to support the decision-making of transportation management agencies.
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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 | 0.001 | 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 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".