Research on Vehicle Following and Overtaking Safety Distance Models Considering Trust Level of Driving
Bibliographic record
Abstract
To investigate the influence of drivers’ cognitive differences on the vehicle driving process, this paper focuses on the mechanism of drivers’ cognition of vehicles, roads, and environments. Existing studies of vehicle following and overtaking usually set the driver reaction time, vehicle braking deceleration, and acceleration as fixed values, ignoring the individual differences of drivers and their influence by road conditions. Therefore, firstly, the concept of “trust level of driving” is proposed and real vehicle tests and simulator tests for different drivers and different working conditions are designed to quantify the trust level of driving. Addressing the limitations of existing car‐following and overtaking models, a safety distance model and a two‐way dual‐lane highway overtaking model are developed based on trust level of driving. Finally, the driving simulator is used to create scenarios, and the effectiveness of the model is verified. The safe vehicle distance and overtaking distance required by drivers with different trust levels of driving are obtained, and their applicability is explained. In this study, a more accurate and humanized model of safe distance and overtaking was developed to help address driver cognitive deficits and improve traffic safety.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".