Development of a 3D Holographic Flight Situational Awareness System
Bibliographic record
Abstract
Recent inventions of Augmented Reality (AR) Head-Mounted-Device (HMD) devices such as Microsoft’s HoloLens have allowed certain innovations that up till now were only able to exist in Science Fiction. The ability to project holograms within a space have been used in the Aerospace industry since 2016, when the HoloLens was first released. However, the aviation industry has yet to harness the capability that such a device can allow. The conversion of a traditional 2D Primary Flight Display (PFD)to a Volumetric 3D representation of the PFD was explored. The 3D representation of the PFD was created in Unity 3D, and by means of the Holographic Remoting Tool the graphics were displayed on to the HoloLens. The symbology on the PFD was driven by live flight data from a flight simulator. For thisproject two different 3D PFD models were created one for a fixed-winged based aircraft, and another fora quadcopter. Two different flight simulators were used for the two different PFDs. For the fixed-wingedPFD the Digital Combat Simulator (DCS) World by Eagle Dynamics was used, and for the quadcopterPFD the AirSim plugin by Microsoft was ran using Unreal Engine 4 (UE4). Through testing it was found that both the PFD models assist the pilots to safely keep their aircraft in the air and also perform an emergency landing by only using the 3D PFD. Another conclusion made was that in its current state the3D PFD is ideal for Unmanned Arial Vehicle (UAV) pilots as a holographic Ground Control Station(GCS)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".