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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".