Advanced Air Mobility Research and Innovation Lab

Visual Clearances for Uncrewed Aircraft — Gap Analysis and Recommendations

Funding Agency: National Aeronautics and Space Administration (NASA)
Lab Affiliate: Dr. Ryan Wallace, Ph.D.

Integrating Advanced Air Mobility (AAM) into the NAS involves addressing significant complexities. AAM represents a transformational shift in aviation operations, requiring seamless coexistence between traditional air traffic and autonomous systems. Existing air traffic management (ATM) procedures and emergent AAM innovations must be evaluated to identify technologies that meet the requirements of existing visual approach functions as well as technological gaps, problems or inconsistencies in the ability of AAM operators to execute visual clearances using existing methods. Establishing clear standards and recommended practices (SARPs) and technological solutions to close operational gaps and ensure safe and efficient integration is imperative.

Safety-Aware Learning and Assure Autonomy for Aviation Applications (SALA^4)

Funding Agency: National Aeronautics and Space Administration (NASA)
Lab Affiliate: Dr. Victor Fraticelli Rivera, Ph.D.

SALA^4 focuses on advancing the safety and resilience of next-generation Advanced Air Mobility (AAM) systems through the development of intelligent autonomy and adaptive control technologies. The research aims to enable real-time detection, self-assessment and response to abnormal conditions, allowing autonomous systems to adjust their behavior dynamically while maintaining safety under operational constraints. These capabilities will support intelligent decision-making and resilient control in complex aviation environments, ensuring safe and reliable operations even in the presence of unexpected events or disturbances.

Safety-Aware Learning and Assured Autonomy for Aviation Applications

Funding Agency: National Aeronautics and Space Administration (NASA)

This project aims to research continuously updating, self-diagnostic vehicle health management to enhance the safety and reliability of Advanced Air Mobility vehicles.

Assessment of AAM Vehicle Integration at the Orlando International Airport

Funding Agency: Greater Orlando Aviation Authority (GOAA)

The assessment of Advanced Air Mobility (AAM) aircraft integration at the Orlando International Airport (MCO) examined the potential operational impacts associated with incorporating AAM traffic into the airport's Class Bravo airspace. The team developed a series of corridor prototypes to assess potential traffic conflicts and the risks of wake turbulence between commercial and AAM operations. Furthermore, an AAM ecosystem was established at MCO to enable the simultaneous integration of realistic traffic routes for commercial and AAM flights. This ecosystem was centered on operational assumptions derived from the FAA's AAM implementation plans, concepts of operation, and stakeholder involvement.

Digital Transformation and Modernization of General Aviation Airports, With a Special Emphasis on Preparations for AAM Operations

In partnership with Altaport, a survey instrument, targeting general aviation airport stakeholders across the U.S., was designed, implemented, and analyzed focusing on modernization efforts and digital adoption, with a special emphasis on preparations for advanced air mobility (AAM) operations. A resulting industry report provided insights into airport modernization trends, challenges, and the evolving landscape of airport technology and infrastructure in response to AAM developments.

Decentralized Traffic Management for Advanced Air Mobility

This project aims to develop and evaluate decentralized traffic management protocols for Advanced Air Mobility (AAM) to ensure safe, efficient, and fair operations of air taxis in urban airspace.

Efficiency Analysis of Florida Airspace for Optimizing Advanced Air Mobility and Space Launch Operations

This project aims to use flight data to improve the accuracy of trajectory prediction models for advancing Florida airspace with AAM and space flight operations using a data-driven method.

Propose Right-of-Way Rules for UAS Operation and Safety Recommendations

Funding Agency: Federal Aviation Administration (FAA)

The overall purpose of this project was to inform rulemaking and standards development regarding potential Right of Way (RoW) concepts for crewed and uncrewed aircraft in the low altitude environment.

Crowdsensing-Based Multi-Agent Data Collection and Aerial-Terrestrial Hybrid Route Determination for Three-Dimensional Urban Environments

This project introduces a novel approach that combines crowdsensing and agent-based modeling to gather high-resolution data in a simulated 3D urban environment, offering predictive enhancements for AAM operations.

Usability of Urban Air Mobility: Quantitative and Qualitative Assessments of Usage in Emergency Situations

Funding Agency: U.S. Department of Transportation (USDOT)

The purpose of these studies was to determine the usability of urban air mobility (UAM) vehicles in the emergency response to natural disasters and the ideal locations for their take-off and landing sites to occur. UAM involves aerial vehicles, mostly operated autonomously, which can complete short flights around urban areas, although their applications are expanding to rural operations as well. While initially designed to support advanced transportation mobility, these vehicles could offer numerous advantages in the emergency response to natural disasters. Through a series of four studies with over 2,000 total participants, quantitative and qualitative methods were used to identify UAM vehicles' usability in response to natural disasters. The studies examined the types of natural disasters and types of missions where UAM could be considered usable, along with the creation of a valid scale to determine vertiport usability. Interviews were also be conducted to provide qualitative insights to complement the quantitative findings.

Identify Models for Advanced Air Mobility (AAM)/Urban Air Mobility (UAM) Safe Automation

Funding Agency: Federal Aviation Administration (FAA)

Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) operations are expected to involve significant amounts of machine automation in order for operations to be profitable. This work focused on Uncrewed Aircraft Systems (UAS) used for passenger transport and cargo delivery in urban areas. The research evaluated AAM/UAM core technology, system architecture, automation design, and system functional concepts to aid the FAA and industry standards development organizations in creating paths forward for these new operational capabilities.

GUMP: General Urban Area Microclimate Predictions Tool

Funding Agency: National Aeronautics and Space Administration (NASA)

Adverse weather conditions, particularly high winds, can have a highly adverse impact on small uncrewed aircraft system (sUAS) operations. These conditions can vary significantly within a small area (particularly in an urban environment); thus, hyperlocal weather predictions are often necessary in order to determine whether a particular sUAS route will be safe to fly. The General Urban area Microclimate Predictions tool (GUMP) seeks to provide such predictions through the use of machine learning (ML) models and computational fluid dynamics (CFD) simulations. Specifically, ML models are trained to ingest mesoscale forecasts from the National Oceanic and Atmospheric Administration (NOAA) and output refined forecasts for some specific location, typically, a weather station that serves as a source of ground truth data during training. At the same time, CFD simulations over 3D models of structures (e.g., buildings) are utilized to extend the refined forecast to other points within the area of interest surrounding the location. Because it is difficult to perform such simulations in real-time, they are executed offline under a wide range of boundary conditions, generating a broad set of resulting wind flow fields. During deployment, GUMP retrieves the wind flow field that is most consistent with the ML model’s forecast. The wind flow field can be converted into an intuitive risk map for sUAS operators through the use of appropriate thresholds on wind velocities.

MoVE: A Mobility Virtual Environment for Planning, Rehearsing, Collecting and Visualizing Atmospheric Observations Using Multiple Coordinated Uncrewed Aerial Vehicles

Uncrewed Aircraft Systems (UAS) have become prevalent in a wide variety of meteorological investigations. UAS afford the ability to fill an important atmospheric observational gap, namely observations in the domain between the reach of ground-based sensors and the altitudes that manned aircraft can safely operate at. Fixed-wing UAS offer an opportunity to cover vast horizontal and vertical distances in a continuous manner with high spatial resolution. Multirotor UAS possess the ability to launch and recover in small spaces, fly at slow airspeeds, hover, accomplish vertical profiles, and probe obstacle laden environments while making spatially dense observations. Each of these UAS categories offer a new observational strategy that is efficient, reusable, durable, repeatable, has a much lower cost barrier, requires minimal infrastructure, and renders superior spatial flexibility, range, and resolution.

Swarms of meteorologically instrumented UAS provide an opportunity to further capitalize on these advantages. However, any given UAS flight must remain within visual line of sight (VLOS) of the remote pilot. Therefore, in order to observe a large geographical area that spans beyond VLOS, multiple UAS must be simultaneously flown. Likewise, to accomplish more spatially dense observations in an immediate area, multiple UAS must undertake concurrent observations. Each of these strategies increase the complexity of the operation and present a challenge in tying together disparate measurements.

To assist in multi-vehicle data collection, as previously described, the open-source, publicly available Mobility Virtual Environment (MoVE) has been developed. This software is designed to first rehearse multi-vehicle scenarios in simulation and then collect real data in real time using a cellular network. In simulation, MoVE can be used to select waypoint routes that ensure safety, are appropriate for the objectives of the atmospheric investigation, and fit within the performance envelope of the involved pilots and uncrewed aircraft (UA). Once observational strategies are reasonably well prepared in the virtual environment, real pilots with real UA can rehearse or undertake flight plans with uninstrumented or instrumented UA. Through MoVE, all data is brought together in time and geo-tagged at suitable frequencies making it easy to combine individual UA data together into a single data set. In this presentation, the ability of MoVE to streamline the planning, execution, post-processing and visualization of data in multi-vehicle field campaigns is explored. The benefits MoVE affords the atmospheric science community can also translate to the broader scientific and engineering communities.

Real-Time Weather Observations for Urban Air Mobility

Urban air mobility (UAM) is expected to be an integral component of cities of the future. However, the urban environment is a new setting for sustained aviation operations. The lower mass, more limited thrust and slower speeds of these vehicles increase their sensitivity to the spatially and temporally dynamic urban environment. Exacerbating this situation is the fact that traditional aviation weather products for observations and forecasts on the outskirts of a metropolitan area do not necessarily translate well to the urban setting. The initial and continuing costs associated with a dense meteorological observation network, required for the heterogeneous nature of the urban environment, make the creation of one in every participating metropolitan area across the country unrealistic. This project explores a variety of potential data sources and proposes a cyber-physical system (CPS) architecture, including an incentive-based crowdsensing application, for real-time aviation observations.

Data Link Security Analysis With Threat Modeling, Simulation Testbed and Prototype Risk Assessment Tool

Funding Agency: Federal Aviation Administration (FAA)

The analysis of data link security with threat modeling led to the development of a simulation testbed and a prototype risk assessment tool. Simulated tests of identified threats were conducted to determine the optimal solution for protecting against potential aviation data link security threats. As part of this task, a testbed environment was created to replicate critical wireless data links and collect real data that mimicked the identified threats posed by potential hackers. Based on the data collected from this testbed and the review of existing security assessment tools, a requirements and specification document for an effective security assessment tool was created. Additionally, a prototype FAA security risk assessment tool was designed and built using existing commercial and/or open-source tools to test each threat scenario identified during the data collection phase

UAS Traffic Analysis

Funding Agency: Federal Aviation Administration (FAA)

In order for the FAA to maintain the safety of the NAS and accommodate new types of UAS operations, it is important to monitor the effectiveness of existing UAS regulations and forecast future UAS integration needs. Using detection data, first of its kind, this research will provide data to support those needs by analyzing sUAS traffic at several urban locations across the NAS.

Analyze Drone Traffic

Funding Agency: Federal Aviation Administration (FAA)

The FAA’s mission is to maintain the safety of the NAS while accommodating new types of Uncrewed Aircraft System (UAS) operations, and – to that end – it is important to assess the effectiveness of existing drone regulations and forecast future UAS integration needs. Using detection data, first of its kind all available data including registration, survey, surveillance and navigation, this research will provide data to support those needs by analyzing drone traffic and drone traffic collision risks at several urban locations across the NAS. Note that this research will expand upon prior work and continue efforts to analyze and understand traffic trends for UAS operations in the NAS.

Integrate and Analysis of sUAS Survey, Registration and Airspace Data for Enhanced Risk Assessment and Forecasting

Funding Agency: Federal Aviation Administration (FAA)

The purpose of this project is to analyze FAA furnished and publicly available sUAS datasets to support the following tasks: 1) estimation and forecasting of sUAS population; 2) understand sUAS operational behaviors (such as operational times, flight frequency, flight duration, operation type, and related factors); 3) create an instrument or algorithm for assessing sUAS operational air and ground risk; 4) evaluate potential implications of sUAS operations on future National Airspace System activities (such as Advanced Air Mobility) and, 4) support integration of further analysis capabilities within the FAA’s Geographic Low Altitude Risk Estimation (GLARE) analysis tool. Implementation of additional capabilities are effected via additional or modification of various geographic information systems (GIS) layers within the FAA’s ArcGIS infrastructure, or incorporated into a supporting data visualization tool, such as Tableau. Specific emphasis is placed on enabling refined estimation of sUAS population and operational activities at county-level geospatial fidelity. When possible, data is assessed for temporal changes to enable trend identification.

• Fraticelli Rivera, V., Thomas, R., Castro Peña, C., & Kuba, S. (2025). Assessment of Advanced Air Mobility Vehicle Integration at the Orlando International Airport. Aerospace, 12(5), 391. https://doi.org/10.3390/aerospace12050391
• Simoneau, T. (2025). Advanced Air Mobility Panel [Panel discussion]. Hot Topics in Air & Space Law, hosted by the New York City Bar Association's Air & Space Law Committee. https://services.nycbar.org/EventDetail?EventKey=AERO021225&WebsiteKey=f71e12f3-524e-4f8c-a5f7-0d16ce7b3314
• S. Kuba, &  Akbas, I.M. (2025). Congestion and Efficiency Analysis of Florida Airspace for Optimizing Advanced Air Mobility and Space Launch Operations. Interservice/Industry Training, Simulation and Education Conference (I/ITSEC), December, Orlando, Florida.
• Pinto, J., Lave, J., Sauer, J., Munoz-Esparza, D., Boehnert, J., Wilson, M., de Boer, G., Bailey, S., Adkins, K., De Wekker, S., Bland, G., Houston, A., Smith, E., & Jacob, J. (2025). Evaluation of Building-Resolving Simulations Using Observations from the ISARRA microSCALES Field Campaign. 24th Conference on Aviation, Range, and Aerospace Meteorology of the American Meteorological Society, 105th AMS Annual Meeting, Baltimore, MD.
• Friedenzohn, D., & Simoneau, T. (2024). Preempting the buzz: Challenges for state and local regulation of drone noise. Journal of Air Law & Commerce, 89(4), 573–623. https://doi.org/10.25172/jalc.89.4.2
• Lange, R., Rice, S., Wallace, R. J., Winter, S. R., Vasquez, M., Woods, S. (2024). Creation of a system taxonomy for advanced air mobility operations. Drone Systems and Applications, 12, 1-16. doi: https://doi.org/10.1139/dsa-2024-0013
• Vempati, L., Gawron, V., & Winter, S. R. (2024). Advanced air mobility: A systematic review of human factors scientific publications and policy. Journal of Air Transportation, 32(1), 22-33. doi: 10.2514/1.D0366
• Winter, S. R., Rice, S., Crouse, S. R., & Wallace, R. J. (2024). The impacts of accident count and vehicle length of time in service on consumer’s willingness to fly in autonomous air taxis. 27th Air Transport Research Society’s World Conference, Lisbon, Portugal.
• Compere, M., Adkins, K., Muthu Krishnan, A., Schroeder, R., and James, C. (2024). The Mobility Virtual Environment (MoVE): an Open Source Framework for Gathering and Visualizing Atmospheric Observations Using Multiple Vehicle-Based Sensors, Environ. Sci.: Atmos., 4, 214-232. doi.org/10.1039/D2EA00106C.
• Krishnan, A.M., Compere, M., and Adkins, K. (2024). The Wind-Arc Method: Estimating the Wind Using the Vehicle as a Sensor. 9th Conference of the International Society for Atmospheric Research using Remotely-piloted Aircraft, Tulsa, OK.
• Abdullah, Q., Adkins, K., Davis, J., and Macchiarella, N. (2024). Transportation Applications for Unmanned Aerial Systems (UAS), 103rd Transportation Research Board (TRB) Annual Meeting, Washington, DC.
• Adkins, K., Compere, M., Krishnan, A.M., Macchiarella, N., Becker, W., Ayyalasomayajula, S., Lavenstein, S., Vlachou, K., and Miller, D. (2023). Validation of the GUMP Hyperlocal Forecasting Tool Using Meteorologically Instrumented Uncrewed Aircraft, 8th Conference of the International Society for Atmospheric Research using Remotely-piloted Aircraft, Bergen, Norway.
• Vempati, L., Myers, P., & Winter, S. R. (2023). The technology readiness and acceptance model as a predictor of pilots’ willingness to operate in UAM integrated airspace. The International Journal of Aerospace Psychology, 33(4), 288-306. Doi: 10.1080/24721840.2023.2242397
• Winter, S. R., Crouse, S. R., Wallace, R. J., Rice, S. & Friedenzohn, D. (2023). The importance of reliability for consumer acceptance of advanced air mobility. 26th Air Transport Research Society’s World Conference, Kobe, Japan.
• Adkins, K., Becker, W., Ayyalasomayajula, S., Lavenstein, S., Vlachou, K., Miller, D., Compere, M., Krishnan, A.M., and Macchiarella, N. (2023). Hyperlocal Weather Predictions with the Enhanced General Urban Area Microclimate Predictions Tool. Drones, 7(7):428. doi.org/10.3390/drones7070428.
• Compere, M., Adkins, K., and Muthu Krishnan, A. (2023). Go with the Flow: Estimating Wind Using Uncrewed Aircraft. Drones 2023, 7, 564. doi.org/10.3390/drones7090564.
• Gonzalez Nunez, J. A. & Akbas, M. I. (2023). Crowdsensing of Meteorological Data for Safety and Efficiency of Unmanned Aerial Traffic in Urban Environment. Interservice/Industry Training, Simulation and Education Conference (I/ITSEC), Paper No. 23382, Orlando, Florida.
• Gonzalez Nunez, J. A. & Akbas, M. I. (2023). Demo: Agent-Based Crowdsensing Simulation for Urban Meteorological Data Collection and Hybrid Aerial-Terrestrial Route Determination. IEEE Conference on Local Computer Networks (IEEE LCN), Daytona Beach, Florida. doi: 10.1109/LCN58197.2023.10223330.
• Rice, S., Winter, S. R., Crouse, S., & Ruskin, K. J. (2022). Vertiport and air taxi features valued by consumers in the United States and India. Case Studies in Transport Policy, 10, 500-506.
• Oseguada, L., and Akbas, M. I. (2022). Using Agent-Based Modeling and Simulation to Evaluate Collision Avoidance in UAS Swarms. Interservice/Industry Training, Simulation and Education Conference (I/ITSEC).
• Vempati, L., Woods, S., & Winter, S. R. (2021). Pilots’ willingness to operate in urban air mobility integrated airspace: A moderated mediation analysis. Journal of Unmanned Vehicle Systems, 10(1), 59-76.
• Akbas, M., Adkins, K., and Compere, M. (2021). Real-Time Urban Observations for Aviation, Proceedings of the 2021 AIAA Aviation Forum. doi.org/10.2514/6.2021-2359.
• Vempati, L., Winter, S. R., Rice, S., Gawron, V., & Robbins, J. M. (2021). Pilots’ willingness to operate in unmanned aircraft system integrated airspace. The International Journal of Aerospace Psychology, 31(4), 343-359. Doi: https://doi.org/10.1080/24721840.2021.1896365
• Ward, K. A., Winter, S. R., Cross, D. S., Robbins, J. M., Mehta, R., Doherty, S., & Rice, S. (2021). Safety systems, culture, and willingness to fly in autonomous air taxis: A multi-study and mediation analysis. The Journal of Air Transport Management, 91, 101975.
• Muna, S. I., Mukherjee, S., Namuduri, K., Compere, M., Akbas, M. I., Molnár, P., & Subramanian, R. (2021). Air corridors: Concept, design, simulation, and rules of engagement. Sensors, 21(22), Article 7536. https://doi.org/10.3390/s21227536
• Vaughn, A., Winter, S. R., Rice, S., & Crouse, S. R. (2021). Consumer’s willingness to support urban air mobility in response to natural disasters. Presentation at the 24th Air Transport Research Society’s World Conference, Sydney, Australia.
• Adkins, K. (2021). Are We Clear for Launch: Preparations for the Coming of Advanced Air Mobility, DeLand Sport Aviation Showcase, Deland, FL.
• Adkins, K. (2021). Weather Alert: Tech Needs for AAM and UAS, National Business Aviation Association (NBAA) Business Aviation Convention & Exhibition (BACE), Las Vegas, NV.
• Adkins, K., Compere, M., and Krishnan, A. (2021). MoVE: A Mobility Virtual Environment for Planning, Rehearsing, Collecting and Visualizing Atmospheric Observations Using Multiple Mobile Sensors, 21st Symposium on Meteorological Observation and Instrumentation, American Meteorological Society Annual Meeting 2021, New Orleans, LA.
• Winter, S. R., Rice, S., & Lamb, T. L. (2020). A prediction model of consumer’s willingness to fly in autonomous air taxis. Journal of Air Transport Management, 89, 101926.
• Adkins, K., Akbas, M., and Compere, M. (2020). Real-Time Urban Weather Observations for Urban Air Mobility, International Journal of Aviation, Aeronautics, and Aerospace, 7(4).
• Adkins, K., Wambolt, P., Sescu, A., Swinford, C., and Macchiarella, N.D. (2020). Observational Practices for Urban Microclimates Using Meteorologically Instrumented Unmanned Aircraft Systems, Atmosphere, 11, 1008. doi.org/10.3390/atmos11091008.
• Lamb, T. L., Ragbir, N. K., Winter, S. R., & Rice, S. (2020). The impact of accident severity on trust and willingness to ride in driverless vehicles and autonomous air taxis. Poster at the 2020 Human Factors and Applied Psychology Conference, Orlando, Florida.
• Winter, S. R. & Rice, S. (2019). A prediction model of consumer's willingness to fly in autonomous air taxis. 23rd Air Transport Research Society World Conference, Amsterdam, The Netherlands.
• Compere, M., Adkins, K., Legon, O., and Currier, P. (2019). MoVE: A Mobility Virtual Environment for Testing Multi-Vehicle Scenarios, Proceedings of the 2019 NDIA Ground Vehicle Systems Engineering and Technology Symposium. http://gvsets.ndia-mich.org/publication.php?documentID=721
• Ward, K. A., Winter, S. R., & Rice, S. (2019). Aircraft systems and passenger willingness to fly in autonomous air taxis. Poster presented at the 10th International Conference on Applied Human Factors and Ergonomics, Washington, D.C.
• Adkins, K. (2019). Urban Flow and Small Unmanned Aerial System Operations in the Built Environment, International Journal of Aviation, Aeronautics, and Aerospace, vol. 6.1. doi.org/10.15394/ijaaa.2019.1312.
• ‘Drones in Cities: The Future of Urban Air Mobility’, Pix4D Featured Case Study, September 2019. https://www.pix4d.com/blog/city-drones-urban-air-mobility
• ‘Urban Drone Flight over Kosovo Offers Rare Insights’, sUAS News, August 2019. https://www.suasnews.com/2019/08/urban-drone-flights-over-kosovo-offer-rare-insights

• Kevin A. Adkins, Ph.D., Director
• M. Ilhan Akbas, Ph.D.
• Marc Compere, Ph.D.
• Sean R. Crouse, Ph.D.
• Stephen Rice, Ph.D.
• John Michael Robbins, Ph.D
• Avinash Muthu Krishnan
• Nikolas "Dan" Macchiarella, Ph.D.
• Victor Miguel Fraticelli Rivera, Ph.D
• Ryan Wallalce, Ed.D
• Scott R. Winter, Ph.D.
• Trevor Simoneau