An Enhanced Security Autonomous Control System for Unmanned Rubber‐Tired Vehicles Operating in Underground Mines
Bibliographic record
Abstract
The operating environment in underground mines is complex and fraught with various hazards that pose severe risks to miners’ safety. As an essential auxiliary transportation device in mines, the operational safety and reliability of rubber‐tired vehicles are crucial to coal mine production safety and efficiency. Therefore, developing an L4‐level autonomous driving system for these vehicles will accelerate the achievement of inherent safety in underground transportation, holding significant theoretical and practical value. This paper mainly studies and improves the control strategy of the chassis of the underground unmanned railless rubber wheeled vehicle and elaborates the MPC controller in detail, including its specific implementation principle, advantages and disadvantages, and the improvement should be carried out in the underground working conditions. Finally, a concrete feasible control scheme is given, and the safety and stability of the scheme are verified by experiments. This research offers theoretical foundations and technical support for the automation and intelligence of rubber‐tired vehicles in underground mines and has made important contributions to the application and industrialization of safe autonomous driving in such environments.
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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".