An aggressive car-following model in the view of driving style
Bibliographic record
Abstract
The complexity of the driving behavior restricts the realism of traffic simulation. This paper proposed that vehicle mobility models should be established according to diverse driving styles to further approximation of real driving behavior. With Krauss model represented, the conservative (driving style) of safe distance car-following model is analyzed. The analysis means that real vehicles can occasionally break the safe distance rule, and on average, real vehicle gap is slightly smaller than that in the Krauss model. An aggressive car-following model is proposed in the view of driving style. Simulation results show the new model can simulate aggressive driving style, which has significance to simulate traffic using diverse driving style models. Since it breaks the safe distance rule, the new model has the possibility of generating rear-end collisions when simulating. Drivers’ characteristics, prediction behavior, the cause of accidents, and the effects of time granularity on a simulation are studied. The concept of “road black hole” is put forward, which is believed to reduce velocity of traffic flow.
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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 | 0.000 | 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 | 0.001 | 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".