Enabling Novel Collaborative Control Paradigms between Humans and Machines in Electric Vertical Takeoff and Landing (eVTOL) Aircraft
Bibliographic record
Abstract
Electric Vertical Takeoff and Landing (eVTOL) vehicles undergoing advanced air mobility (AAM) operations feature increasingly autonomous systems (IAS) with non-traditional role allocations. Ensuring the safety of these operations and their novel human–machine teaming (HMT) paradigms requires an appropriate body of knowledge created through relevant, reproducible research. In this paper, we briefly examine the meaning of teaming; current regulation, standards, and guidance; and the knowledge required to build resilient HMTs before turning our attention to how this knowledge is being created by recent research and what conclusions or recommendations can be made. We identify the need for further research into the holistic performance of HMTs, the effect of novel allocations of roles between humans and machines, the ability of humans to provide resilience to unforeseen dangers when acting as a part of these teams; and the characteristics required for clear, timely, and accurate communication between the humans and machines. This work is done in the context of eVTOL aircraft with an indirect flight control system (IFCS) undergoing urban air mobility operations.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".