Collision avoidance strategies for advanced air mobility using UAS-to-UAS communications
Bibliographic record
Abstract
Advanced air mobility (AAM) has introduced a new mode of air transportation that can be integrated, providing services including air taxis, which can quickly transport people and cargo from one place to another. However, urban airspace is already congested with commercial air traffic, so there is a need for an efficient and autonomous airspace management system. Establishing structured air corridors and enabling UAS-to-UAS (U2U) communications are essential to achieve autonomy. Air corridors are designated airspace primarily reserved for AAM traffic, which will streamline the movement of unmanned aircraft systems (UAS). Meanwhile, U2U communications facilitate efficient collision avoidance strategies (CAS). A key aspect of this system is the development of CAS, which requires advanced communication protocols to monitor traffic patterns and detect potential collisions. This paper explores designing and implementing CAS using U2U communications. Use cases for U2U communications include merging, minimum separation, information relay, collaborative sensing, and rerouting. All these use cases demand real-time solutions for managing traffic conflicts involving multiple UAS. The CAS discussed in this paper utilizes U2U communications to mitigate the risk of collisions in the airspace and demonstrates how U2U communications can assist in efficient AAM traffic management through simulations.
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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".