Expressway Lane Change in Fog Environment by Dynamic Strategic Game
Bibliographic record
Abstract
To study the behavior of expressway driver’s lane change in a foggy environment, the driver’s vision is adopted as the index for assessing the lane change behavior. The normalization theory is introduced to analyze the driver’s intention of changing lanes. Using the statistical examination results of the driver’s gaze zones, this paper has analyzed the gaze and glance features of the driver during the change of lanes. Based on the driving condition of changing lanes in foggy environment, the game theory is adopted in the study to elaborate on the strategy equilibrium of the driver’s decision-making in the foggy environment. Moreover, a model is established to analyze the driver’s lane changes under the foggy environment. Through the calibration of parameters and verification of the model, the reliability of the model has been proved. The research findings indicate that the features of the driver’s gaze and glance under circumstances of the fine weather differ from those in the foggy environment, thus laying a theoretical foundation for the safety management of subsequent lane changes in the expressway in foggy environment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".