Collaborative Management for Spatial Safety for Unmanned Intelligent Vehicles in Human-Machine Coexistence Environment
Bibliographic record
Abstract
The expansion of unmanned intelligent vehicles in daily living spaces increases the demand for their safe operation in human-machine coexistence environments. Effective space management is crucial to ensuring the safety and reliability of intelligent vehicles by preventing conflicts and controlling fear toward humans to a low level. Traditional spatial models, such as trajectory-based operation (TBO) and free-flight operation (FFO), are more or less deficient in maintaining low space occupancy and reducing the probability of conflicts. Hence, we present a MAPPO algorithm based on real-time dynamic motion data and a fear index model that quantifies human apprehension towards vehicles, aiming to allocate exclusive operational spaces for each vehicle. An octree-based spatial partitioning method is used to further mitigate conflicts among these space. Simulation experiments indicate that the proposed solution ensures collision-free allocation and reduced spatial occupancy that is 6.24% to 40.84% of other schemes , which achieves equilibrium between TBO and FFO.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".