Monte Carlo Simulation-Based Risk Assessment for Unmanned Ground Equipment Taxiing Guidance
Bibliographic record
Abstract
Due to the potential safety hazards such as incorrect or missed aircraft guidance caused by human-operated guiding, the introduction of unmanned guiding vehicles can effectively reduce these unsafe events. However, the risks associated with unmanned driving guiding vehicles in the process of guiding aircraft taxiing have not yet been thoroughly and quantitatively studied. This paper collects the kinematic parameters of the unmanned driving guiding vehicle during the process of guiding manned aircraft, applies Monte Carlo simulation to generate a dataset of simulated operational processes that cover the entire taxiing guidance process, and introduces three major risk assessment indicators based on the motion process between the unmanned driving guiding vehicle and the manned aircraft during the taxiing guidance process. Through the normalization function of risk evaluation indicator weights and based on the Gaussian distribution that satisfies the normal distribution, a qualitative evaluation of risk levels is conducted based on quantifiable actual operational processes. The results show that quantifiable risk assessment indicators can provide risk evaluation results with stronger real-time reference and offer operable solutions for risk avoidance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".