Speed Management of Road Sections with Sudden Change in Road Adhesion Coefficient
Bibliographic record
Abstract
Tire-road friction coefficient (TRFC) characterizes the interaction of the vehicle with the road and is closely related to driving safety. In this study, speed management of vehicles traveling on road sections with a sudden change in TRFC was proposed. Above all, fuzzy inference system (FIS) was adopted for detailed TRFC calculations of various pavement conditions (material type, pavement humidity, vehicle load) based on the aggregated data for various methods for measuring TRFC and calculating TRFC. Then combined with TRFC, the stopping sight distance (SSD) model, and the driver reaction time, the relationship between TRFC and the design speed was deduced. Furthermore, principles of vehicle dynamics were carried out to derive the speed adjustment distance required for the vehicle to travel on different road connection sections. It was found that design speed and TRFC were positively correlated. In addition, TRFC was positively correlated with vehicle load and negatively correlated with surface moisture. The length of the transition section was related to the pavement type and the connection sequence of the pavement junctions. The greater the performance differences between the two pavements, the longer the transition length should be required. Finally, the co-simulation of Carsim and Simulink verified the feasibility of speed management which was well suited to vehicle dynamics.
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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) | 0.000 | 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 | 0.000 |
| 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 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".