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3D Simulation of Advanced Air Mobility Vehicles in a Photorealistic Urban Environment

2025· preprint· en· W4412873322 on OpenAlexaffabout
Mohsen Rostami, Pratik Pradhan, Jeffery Omorodion, Aditya Venkatesh, Charith Kongara, Joon Chung

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsToronto Metropolitan University
Fundersnot available
Keywords3d simulationEnvironmental scienceComputer scienceAutomotive engineeringTransport engineeringEngineeringSimulation

Abstract

fetched live from OpenAlex

In recent years, the concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) have risen rapidly. While the companies designing and manufacturing them focus on creating some of the marvellous aircraft in existence, the MIMS Lab has been exploring options to develop a custom simulator for UAM vehicles from different perspectives. This paper presents a proof-ofconcept application designed using Unreal Engine and Cesium plugin and assets collected and modified from various online databases to produce a photorealistic flight simulator focused on UAM flight and traffic. While the project is still in its infancy, this paper presents proof of concept for a possible scenario for UAM vehicles navigating in an urban setup such as the City of Toronto. Here, three modes of simulation are presented: a pre-programmed flight with a tunnel-inthe-sky corridor for Head-Up Display (HUD) visualization, a tower view perspective to monitor multiple UAM flights within the area, and a pilot-controlled Vertical TakeOff and Landing (VTOL) flights between different helipads in Downtown Toronto. As the project progresses, the plans include implementing proper flight dynamic models for multiple recognized aircraft that fall under the UAM category and conducting a pilot study to obtain their perspectives, areas of improvement and results.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.240
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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