Modeling Analysis of Improved Minimum Safe Following Distance under Internet of Vehicles
Bibliographic record
Abstract
The development of the Internet of vehicles technology can improve the communication between vehicles, thereby changing the driving behavior of drivers. Therefore, the traditional safe-following model cannot accurately describe the driving behavior and needs to be improved accordingly. First, two key parameters (i.e., drivers’ reaction sensitivity and road friction coefficient) are obtained through a comprehensive comparative analysis of influencing factors on the Internet of vehicles environment. And the calculation methods of these two parameters are proposed by using the multilevel comprehensive weighted evaluation method and the BP neural network. Then, these two key parameters are used to improve the traditional minimum safety distance model for adapting to driving behavior under the Internet of vehicles environment. Finally, through setting up simulation experiments and comparative analysis, the relationship between different influencing factors and the minimum safe following distance is obtained, and the influence degree of different influencing factors is sorted. The most important factor affecting car-following safety is the drivers’ characteristics. It can provide strong theoretical support for the safe driving assistance system of vehicles.
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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 | 0.000 | 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".