Fault-tolerant Trajectory Prediction Using Random Forest Methodology Application to UAS-S4 Ehécatl
Bibliographic record
Abstract
Accurately predicting aircraft trajectory plays a crucial role in optimizing air traffic management systems and ensuring the safety and efficiency of aviation transportation. In this research, a comprehensive study is conducted to examine the effectiveness of the Random Forest (RF) methodology in aircraft trajectory prediction. The RF methodology is compared with two well-known generative models, namely Generative Adversarial Nets (GANs) and Autoencoders. To conduct this study, a substantial dataset is used, that consists of historical aircraft trajectory data generated through the UAS-S4 simulator. The results from our experiments demonstrate that the RF methodology outperforms both GANs and Autoencoders in terms of accurately predicting trajectories in the testing phase. Moreover, the RF model exhibits remarkable generalization capabilities, enabling reliable predictions even in the presence of faults and failures. These findings highlight the potential of the RF methodology as a promising alternative to generative neural network models for aircraft trajectory prediction.
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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".