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Articles 1 - 30 of 988
Full-Text Articles in Aviation Safety and Security
Prototype Ai Assistant For Private And Student Pilots, Shawn De La Osa
Prototype Ai Assistant For Private And Student Pilots, Shawn De La Osa
Discovery Day - Daytona Beach
This research explores AI-based decision support in light general aviation (e.g., Cessna 172, Diamond), where onboard hardware is typically too limited for modern AI. In aviation, AI has the potential to act as a “copilot” supporting planning, execution, and assessment, particularly since single pilots must simultaneously aviate, navigate, and communicate under high workload. However, although AI adoption is rapidly expanding, many implementations are introduced without fully incorporating human-centered design processes that account for pilot workflow and workload. We leverage a human factors process in this research. First, we design and evaluate a cloud-based AI copilot . The system leverages Starlink …
Flight Testing And System Identification Of An Experimental Cessna 182, Mariano Chavez Rangel
Flight Testing And System Identification Of An Experimental Cessna 182, Mariano Chavez Rangel
Discovery Day - Daytona Beach
Flight testing and system identification are essential for accurately characterizing aircraft dynamics and supporting the development of reliable flight control systems. This work presents the use of an experimental Cessna 182 as a full-scale platform for flight testing and system identification, conducted by the Eagle Flight Research Center. The objective is to generate high-fidelity flight data to estimate aerodynamic and dynamic coefficients and establish a baseline model for comparison with a sub-scale aircraft incorporating Integrated High-Lift Propulsor (IHLP) technology. The experimental aircraft is equipped with a comprehensive onboard instrumentation suite designed to capture synchronized measurements of air data, aircraft motion, …
Adaptive Health Monitoring For Runtime Safety Assurance Of Advanced Air Mobility Applications, Michael Budihartono, Francisco Bustamante, Gabriela Gavilanez
Adaptive Health Monitoring For Runtime Safety Assurance Of Advanced Air Mobility Applications, Michael Budihartono, Francisco Bustamante, Gabriela Gavilanez
Discovery Day - Daytona Beach
This work proposes a comprehensive, adaptive framework for abnormal condition detection and flight envelope prediction in complex systems. The approach integrates data reduction techniques, including principal component analysis and lower-dimensional projections, to efficiently distinguish between nominal and abnormal operational states while maintaining computational efficiency through metacognitive interventions. A hybrid detection architecture combines bio-inspired real-value negative selection algorithms with support vector machines, augmented by generative machine learning models to synthesize failure data and support training through digital twin environments. Online fault trend analysis enables continuous monitoring of system degradation, while the system dynamically adapts to operational variations by minimizing offline training …
Crowd Monitoring And Firearm Detection Uas, Arjun Nambiar, Sang-A Lee, Diego Espino, Will Obot Jr, Ethan Encarnacion, Jarrett Usui, Jacob D. Kline
Crowd Monitoring And Firearm Detection Uas, Arjun Nambiar, Sang-A Lee, Diego Espino, Will Obot Jr, Ethan Encarnacion, Jarrett Usui, Jacob D. Kline
Discovery Day - Daytona Beach
The increasing complexity of public safety operations in urban and high-density environments necessitates intelligent, mobile surveillance systems capable of real-time threat identification and situational awareness. Traditional monitoring approaches, such as fixed CCTV systems and manual observation, are often limited by coverage, scalability, and response latency in complex environments or large-scale events. This project addresses these challenges through the development of a computer vision-enabled Uncrewed Aerial System (UAS) designed for crowd monitoring and firearm detection. The primary objective is to design and validate a modular, Artificial Intelligence (AI)-Machine Learning (ML)-driven model capable of identifying firearms within dynamic environments while supporting real-time …
Characterizing Air Traffic Density Using Nationwide Ads-B Data, Taeyun Yoo, Arjun Nambiar, Sang-A Lee
Characterizing Air Traffic Density Using Nationwide Ads-B Data, Taeyun Yoo, Arjun Nambiar, Sang-A Lee
Discovery Day - Daytona Beach
This study addresses the increasing need to understand aircraft activity in low-altitude airspace, where emerging operations such as Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) are expected to heighten traffic complexity and safety risks. This research develops a scalable framework for constructing a nationwide, high-resolution, altitude-stratified airspace density atlas using Automatic Dependent Surveillance–Broadcast (ADS-B) data. The methodology incorporates large-scale data acquisition, terrain-referenced altitude normalization, and spatial aggregation using a hexagonal grid system, followed by statistical modeling to estimate traffic density across spatial and temporal dimensions. Preliminary results indicate the capability to generate detailed geospatial representations of aircraft activity, …
Spatial Dynamics Of Lead Waste: An Esda Approach To U.S. Interstate Transfers, Santiago Acuna Gonzalez
Spatial Dynamics Of Lead Waste: An Esda Approach To U.S. Interstate Transfers, Santiago Acuna Gonzalez
Discovery Day - Daytona Beach
Spatial Dynamics of Lead Waste: An ESDA Approach to U.S. Interstate Transfers This research applied a novel Exploratory Spatial Data Analysis (ESDA) methodology to analyze interstate movements (n= 446) of lead (Pb; CAS 7339-92-1) among facilities that transported more than 1,000 lbs offsite across the United States, using the Environmental Protection Agency’s (EPA) 2024 Toxic Release Inventory (TRI). Using QGIS, the analysis produced three geospatial products by integrating TRI Form R Schedule 3A with coordinates from the EPA Facility Registry Service (FRS) to populate offsite locations. First, a state-level net-flow choropleth distinguished exporting from importing states. Second, an export-focused map …
Environmental Ethics And Impact Of Artificial Intelligence Data Centers, Jacky Yang
Environmental Ethics And Impact Of Artificial Intelligence Data Centers, Jacky Yang
Discovery Day - Daytona Beach
Data centers, including artificial intelligence (AI) facilities, serve as the backbone for many modern digital services and products. With the rapid digitization of society, the expansion of these centers in the United States has accelerated significantly. From an anthropocentric and utilitarian perspective, they provide economic benefits, supporting employment and generating public revenue. However, they can also create a significant amount of environmental and societal challenges. From an ecocentric and environmental justice perspective, AI data centers has contributed to increased competition for the limited resources, such as water, electricity, and land and has been linked to rising utility costs, noise pollution, …
Learning Casual Structures From Aviation Accident Narratives Using Natural Language Processing And Graph-Based Knowledge Representation, Stephanie Ramsey, Katherine Hoffsetz, Madeline Gorman, Logan Lambeth
Learning Casual Structures From Aviation Accident Narratives Using Natural Language Processing And Graph-Based Knowledge Representation, Stephanie Ramsey, Katherine Hoffsetz, Madeline Gorman, Logan Lambeth
Discovery Day - Daytona Beach
Understanding the complex causal relationships underlying aviation accidents is critical for improving safety and preventing future incidents. However, much of this information exists in unstructured narrative reports, making large-scale analysis difficult. This project aims to automatically extract and model causal chains from National Transportation Safety Board (NTSB) accident narratives using a combination of traditional natural language processing (NLP) techniques, transformer-based architectures, and graph-based knowledge representation. Traditional NLP methods, including named entity recognition, dependency parsing, and rule-based pattern matching, will be used to identify structured cause–effect relationships. These approaches will be compared with transformer-based models, including a lightweight encoder for classification …
Quantitative Assessment Of Cybersecurity Risk Variability Across Transportation Modes, Paulo Carreon
Quantitative Assessment Of Cybersecurity Risk Variability Across Transportation Modes, Paulo Carreon
Discovery Day - Daytona Beach
Quantitative Assessment of Cybersecurity Risk Variability Across Transportation Modes examines how cybersecurity risks differ across major transportation sectors and addresses the lack of a structured, cross modal analysis in existing transportation cybersecurity research. As transportation systems increasingly rely on digital infrastructure, communication networks, operational technologies, and interconnected platforms, they become more exposed to cyber threats that can affect safety, mobility, operations, and public trust. Despite the growing importance of this issue, there is still limited research that quantitatively compares how cyber risks vary across transportation modes such as road and intelligent transportation systems, aviation, rail and transit, and maritime systems. …
Advancing Runway Incursion Severity Prediction Through Tabular-To-Image Deep Learning, Bill Deng Pan, Yupeng Yang
Advancing Runway Incursion Severity Prediction Through Tabular-To-Image Deep Learning, Bill Deng Pan, Yupeng Yang
Discovery Day - Daytona Beach
Runway incursions remain one of the most persistent safety challenges in modern aviation operations, often resulting from complex interactions among human, environmental, and operational factors. While existing Safety Management System (SMS) frameworks emphasize monitoring the frequency of incursions, they provide limited predictive capability regarding event severity. This ongoing study seeks to address that gap by applying a novel tabular-to-image deep-learning approach to improve the accuracy and interpretability of runway-incursion severity prediction models. Data for this study will be drawn from the Federal Aviation Administration (FAA) Runway Safety Statistics and National Transportation Safety Board (NTSB) accident databases. The methodology will involve …
Assessing Safety Culture Among Airport Operators Through Stakeholder Perceptions, Bill Deng Pan
Assessing Safety Culture Among Airport Operators Through Stakeholder Perceptions, Bill Deng Pan
Discovery Day - Daytona Beach
The effective implementation of Safety Management Systems (SMS) at airports depends not only on compliance with regulatory requirements but also on the presence of a strong and measurable safety culture. While the Federal Aviation Administration (FAA)’s increasing emphasis on SMS has accelerated adoption across certificated airports, many airport operators, often coming from operational, engineering, or business backgrounds, lack practical methods to assess safety culture in a structured and actionable manner. Existing guidance highlights the importance of safety culture and safety climate. However, limited research exists on how airport stakeholders perceive and experience safety culture within the airport operating environment. This …
Sequential Causal Architecture For Multimodal Aviation Accident Prediction, Kaitlyn Cavanaugh, Isaac Morrison
Sequential Causal Architecture For Multimodal Aviation Accident Prediction, Kaitlyn Cavanaugh, Isaac Morrison
Discovery Day - Daytona Beach
Aviation accidents are rarely the result of a single failure but rather from a complex causal chain of latent failures. While traditional data mining models often predict incident occurrence, they frequently overlook the sequential mechanics defined by known accident causation theoretical frameworks like the Swiss Cheese Model and the FAA's HFACS. This project addresses the need for interpretable, reliable, multi-stage forecasting by proposing a Sequential Causal Architecture that transforms theoretical causation models into a structured Directed Acyclic Graph (DAG) for multimodal accident causation chain prediction. Data from the NTSB and DOT is used and connected together in a meaningful way …
Modeling Aircraft Collision Risk Using Machine Learning And Traffic Density Data Ac, Jadia Renee Ewing, Alexander Van Baelan, Conrad Prisby, Rafal P. Smietana
Modeling Aircraft Collision Risk Using Machine Learning And Traffic Density Data Ac, Jadia Renee Ewing, Alexander Van Baelan, Conrad Prisby, Rafal P. Smietana
Discovery Day - Daytona Beach
Air traffic congestion is an increasingly important factor in aviation safety as global flight activity continues to grow. This project investigates whether higher traffic density is associated with an increased risk of aviation incidents and identifies key contributing factors. Using publicly available flight (ADS-B) and incident (NTSB) data, we apply several machine learning models to analyze traffic patterns and predict risk. We begin with logistic regression to evaluate the relationship between density and incident probability, followed by decision trees to extract interpretable rules describing high-risk conditions. K-nearest neighbors (KNN) is used to examine similarity in traffic patterns among incident flights, …
Impact Of Stress & Fatigue On Cardiovascular Function In Young Pilots: A Thematic Review, Swarna Manoj
Impact Of Stress & Fatigue On Cardiovascular Function In Young Pilots: A Thematic Review, Swarna Manoj
Discovery Day - Daytona Beach
Impact of Stress & Fatigue on Cardiovascular Function in Young Pilots: A Thematic Review Authors: Swarna Manoj, Scott Ferguson Keywords: pilot, stress, fatigue, heart rate variability (HRV), cardiovascular disease (CVD) Aviation is expanding globally and young pilots are the major workforce, yet FAA protocols require mandatory ECG only from age 35, with annual cardiac screening after age 40. Over the past five years, a pattern of sudden cardiac arrests in pilots particularly aged 30-40 has urged safety concerns. Despite the existing literature examining fatigue, burnout, stress, sleep hygiene and circadian disruption, only few studies systematically examine how these factors impact …
Human Factors Challenges In Automated Flight Operations, Jorge Ivan Gonzalez Rivera, Zoe Guyette, Emma Hatten, Katie Huynh, Benjamin Marsh
Human Factors Challenges In Automated Flight Operations, Jorge Ivan Gonzalez Rivera, Zoe Guyette, Emma Hatten, Katie Huynh, Benjamin Marsh
Discovery Day - Daytona Beach
Automation complacency, a critical aviation hazard, is commonly seen amongst flight crews in high-automation commercial flight decks. This issue occurs when over-reliance on automated systems leads to a decline in manual flying expertise and situational awareness, posing a severe risk of loss of control in-flight (LOC-I) and catastrophic accidents. To bridge the safety gap between current operational demands for efficiency and the regulatory requirement for pilot proficiency, this study proposes the Manual Flight Compliance and Monitoring System (MFCMS). This solution integrates engineering and administrative controls to reduce automation-related risks by mandating 15 minutes of manual flight per hour during cruise …
Multimodal Interpretation Of Pilot–Controller Communications For Runway Safety Assurance And Enhanced Atc Situational Awareness., Chaebin Song, Juin Park
Multimodal Interpretation Of Pilot–Controller Communications For Runway Safety Assurance And Enhanced Atc Situational Awareness., Chaebin Song, Juin Park
Discovery Day - Daytona Beach
Recent runway safety events have highlighted the importance of accurate and timely interpretation of pilot–controller communications, especially in complex airport environments where aircraft and ground vehicles interact on or near active movement areas. In light of the recent LaGuardia Airport collision, which has drawn attention to communication, coordination, and surface safety challenges, this project investigates how multimodal data can support air traffic controllers’ situational awareness and safety assurance. This study develops an in-progress framework that combines Automatic Speech Recognition (ASR) of pilot–controller radio communications with ADS-B trajectory data to improve interpretation of operational intent and cross-check communications against observed aircraft …
Applying The Hfacs Framework: A Case Study Of Air Florida Flight 90, Ashley Perez-Galvan, Cenk Altinkum, Ryan Hunt
Applying The Hfacs Framework: A Case Study Of Air Florida Flight 90, Ashley Perez-Galvan, Cenk Altinkum, Ryan Hunt
Discovery Day - Daytona Beach
Applying the HFACS Framework: A Case Study of Air Florida Flight 90 Abstract This study applies the Human Factors Analysis and Classification System (HFACS), a widely used safety framework for identifying human and organizational contributors to accidents to examine the crash of Air Florida Flight 90 crash on January 13, 1982. The accident occurred shortly after takeoff from Washington National Airport during severe winter weather, when the aircraft failed to gain sufficient lift and impacted a bridge before entering the Potomac River. The goal this analysis is to understand how multiple layers of human and organizational factors contributed to this …
The Impact Of Environmental Contributing Factors In Spatial Disorientation And Non-Spatial Disorientation Related General Aviation Accidents, Victoria Cornaro, Molly Mersinger
The Impact Of Environmental Contributing Factors In Spatial Disorientation And Non-Spatial Disorientation Related General Aviation Accidents, Victoria Cornaro, Molly Mersinger
Discovery Day - Daytona Beach
The Impact of Environmental Contributing Factors in Spatial Disorientation and Non-Spatial Disorientation Related General Aviation Accidents Spatial disorientation (SD) is an inherent risk of flying and with a high risk of resulting in a fatal accident (Gibbs et al, 2011). SD related accidents occur when a pilot’s perception of the aircraft altitude, position, or relative motion conflict with reality (Benson, 1999). SD accidents are significantly more likely to result in a fatality than non-SD cases. The purpose of this study was to investigate the role of different contributing factors on SD and non-SD general aviation accidents. We used the NTSB …
Remora Ads-B In Receiver, Noah Evans, Sara Patel, Nicholas Gatto, Daniel Ingleton
Remora Ads-B In Receiver, Noah Evans, Sara Patel, Nicholas Gatto, Daniel Ingleton
Discovery Day - Daytona Beach
The Remora is a compact Automatic Dependent Surveillance Broadcast (ADS-B) receiver, designed to enhance situational awareness for pilots operating experimental and homebuilt aircraft. Unlike conventional panel or windscreen-mounted systems, the Remora is installed externally, making it a low-profile solution to the ADS-B In challenge. The device integrates easily with the aircraft’s existing power supply. It transmits data wirelessly to electronic flight bags (EFBs) within the cockpit, using existing iOS and Android software for display and user interaction. The primary function of the Remora is to provide public-access, real-time traffic awareness and weather forecasting, enabling pilots to make informed mission decisions. …
Human Factors And Passenger Survival In Aircraft Emergency Evacuations, Anjelysa Oleszewski, Adam Dvorak, Anthony Bulko, Christopher Lee, Nicholas Murphy
Human Factors And Passenger Survival In Aircraft Emergency Evacuations, Anjelysa Oleszewski, Adam Dvorak, Anthony Bulko, Christopher Lee, Nicholas Murphy
Discovery Day - Daytona Beach
In the event of an emergency where evacuation of the aircraft is necessary, passengers have 90 seconds to locate their nearest exit and egress as safely and quickly as possible. However, news outlets have reported multiple occurrences of passengers evacuating and taking their belongings with them during this decade. The root of this hazard occurs in multiple areas of flight, particularly during safety briefings and during emergency evacuations. The purpose of this project is to focus on improving passenger knowledge of safety features such as exit door locations through digital mediums and strengthen deliverance of life-saving information. The project shares …
Zfq-50 "Radiance", Craig Slovensky, Michael Rath Iii, Kyan Spaete, Long P. Nguyen, Woo Hyun Lee, Roman Czerniejewski
Zfq-50 "Radiance", Craig Slovensky, Michael Rath Iii, Kyan Spaete, Long P. Nguyen, Woo Hyun Lee, Roman Czerniejewski
Discovery Day - Daytona Beach
This project presents the preliminary design of the ZFQ-50 "Radiance", a Collaborative Unmanned Vehicle (CUV) built around the VerdeGo VH-5 blended turbofan intended for military defense applications. This design presents a novel aircraft coupled with a powerplant that blends traditional combustion thrust with electrical power output, an new and evolving capability within the aerospace industry. This aircraft was sized considering multiple constraint parameters, configuration trade studies, CFD analysis, and mission requirements including carrier assisted take-off and landing, an effective operational range, and a loiter period with 40 kW of continuous power output from the powerplant. The selected configuration allows for …
Classification Of Sequential Factors In Aviation Accident Cause Prediction, Sophia Nasca, Addyson Wolfe
Classification Of Sequential Factors In Aviation Accident Cause Prediction, Sophia Nasca, Addyson Wolfe
Discovery Day - Daytona Beach
Uncovering the root causes of aviation accidents is a critical component of improving aviation safety. Traditional approaches are largely reactive, relying on post-incident analysis rather than proactively identifying risk factors. This project addresses the need for proactive safety by using a multi-source dataset that integrates aviation accident records, weather conditions, and maintenance data extracted from investigative reports. The objective of this work is to move beyond predicting broad probable causes and instead model the sequence of contributing factors that lead to aviation incidents. Using the Swiss Cheese Model, the study will capture layered failures across operational, environmental, and maintenance domains. …
Small Uas Detection: Threat Intelligence & Risk Management Project, Tyler Johnson
Small Uas Detection: Threat Intelligence & Risk Management Project, Tyler Johnson
Discovery Day - Daytona Beach
The TRANSPORTATION SECURITY ADMINISTRATION / FEDERAL AIR MARSHAL SUAS DETECTION: THREAT INTELLIGENCE & RISK MANAGEMENT PROJECT addresses the emerging safety and security challenges posed by the rapid growth of small Unmanned Aircraft Systems (sUAS) in complex airspace environments. This study analyzed 92 days of sensor-captured Remote Identification (RID) data collected near Fort Lauderdale-Hollywood International Airport (FLL) to assess operational behaviors, aviation risk, and ground risk associated with drone activity. The primary objective of this research is to identify patterns of unauthorized or hazardous sUAS operations to enhance situational awareness and inform actionable risk-mitigation strategies. The analysis identified 335 flights from …
Case Study - China Airlines Flight 006, Jaxon Daniels, Ryan Lutwin, Jacob Patterson, Elizabeth Bosan, Ronyn Orrell
Case Study - China Airlines Flight 006, Jaxon Daniels, Ryan Lutwin, Jacob Patterson, Elizabeth Bosan, Ronyn Orrell
Discovery Day - Daytona Beach
On February 19, 1985, China Airlines Flight 006, a regularly scheduled flight from Taipei, Taiwan, to Los Angeles, California, experienced a serious in-flight upset while cruising at high altitude. The aircraft subsequently entered a steep, uncontrolled spiral dive, during which it experienced extreme G-forces and sustained structural damage. After regaining control, the crew diverted the aircraft to San Francisco International Airport, where it landed safely (National Transportation Safety Board, 1986). The Human Factors Analysis and Classification System (HFACS) framework helps to identify and analyse the organizational elements and conditions that lead to aviation accidents (Small, 2020). Aviation accidents are not …
An Innovative Approach To Real-Time Noise Monitoring And Detection In Airport Ramp Operations, Grace Hamilton, Nicole E. Egan, Anderson Peralta, Deyaneira Rodriguez, Maria G. Valentinez
An Innovative Approach To Real-Time Noise Monitoring And Detection In Airport Ramp Operations, Grace Hamilton, Nicole E. Egan, Anderson Peralta, Deyaneira Rodriguez, Maria G. Valentinez
Discovery Day - Daytona Beach
Airport ramp personnel are routinely exposed to hazardous noise levels exceeding occupational safety thresholds, increasing the risk of permanent hearing loss, degraded communication, and operational incidents. Traditional ramp noise management relies primarily on periodic monitoring and personnel protective equipment (PPE), which limits real-time hazard recognition and a proactive risk mitigation strategy. The purpose of this study is to evaluate the safety and financial effectiveness of implementing a real-time noise monitoring and detection system within airport ramp operations in alignment with Safety Management System (SMS) principles. A literature review, Preliminary Hazard Analysis (PHA), and financial cost-benefit analysis were conducted to compare …
Fatigue And Circadian Disruption In Long-Haul Ferry Pilots: Human Factors Challenges And Mitigation Strategies, Parneet Makkar
Fatigue And Circadian Disruption In Long-Haul Ferry Pilots: Human Factors Challenges And Mitigation Strategies, Parneet Makkar
Discovery Day - Daytona Beach
Fatigue and circadian rhythm disruption present significant human factors challenges in long-haul aviation operations, particularly for ferry pilots (pilots responsible for transporting aircraft long distances between locations) who often travel across multiple time zones before beginning flight duties. Circadian rhythms regulate the human sleep-wake cycle and are closely tied to cognitive performance, alertness, and coordination. Rapid travel across time zones can disrupt these rhythms, producing symptoms commonly associated with jet lag such as sleep disturbances, fatigue, and reduced cognitive functioning. For ferry pilots, long travel periods and irregular schedules may increase the likelihood of fatigue before flight operations even begin. …
An Evaluation Of Machine Learning Models' Efficacy In Determining Uav Spoofing Attacks, Nicolas Machado, Jaxon Selzer
An Evaluation Of Machine Learning Models' Efficacy In Determining Uav Spoofing Attacks, Nicolas Machado, Jaxon Selzer
Discovery Day - Daytona Beach
An Evaluation of Machine Learning Models' Efficacy in Determining UAV Spoofing Attacks - The rapid integration of Unmanned Aerial Vehicles (UAVs) into urban airspace has introduced significant cybersecurity concerns, particularly due to vulnerabilities in Automatic Dependent Surveillance–Broadcast (ADS-B), which lacks authentication and encryption. This project addresses the problem of detecting spoofing and data manipulation attacks that can compromise UAV safety and mission reliability. The objective of this work is to evaluate the effectiveness of machine learning–based anomaly detection, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, as protocol-agnostic solutions for identifying anomalous UAV behavior. To achieve this, …
Fault-Aware Flight Path Assessment For Evtols Using An Integrated Air Traffic Management Environment, John Clardy, Edison Alberto Martinez Samaniego
Fault-Aware Flight Path Assessment For Evtols Using An Integrated Air Traffic Management Environment, John Clardy, Edison Alberto Martinez Samaniego
Discovery Day - Daytona Beach
This study introduces a simulation framework to evaluate Fault-Aware Flight Paths for electric vertical takeoff and landing (eVTOL) aircraft within an integrated air traffic management environment. By combining an air traffic management system with intelligent trajectory generation frameworks, the approach assesses the development of safe, adaptive, and fault-aware flight paths that account for traffic interactions, airspace features, and specific operational constraints. Real-world historical airspace traffic data was used to accurately simulate complex and congested operational conditions. The main goal is to evaluate the operational impact of integrating eVTOL operations into the National Airspace System, with a focus on conflict detection, …
A System Safety Approach To Assuring Artificial Intelligence Enabled Functions In Civil Aviation, Evan Bear, Quinn Galen
A System Safety Approach To Assuring Artificial Intelligence Enabled Functions In Civil Aviation, Evan Bear, Quinn Galen
Discovery Day - Daytona Beach
Artificial intelligence and machine learning techniques are increasingly proposed for use in safety-critical civil aviation functions including perception decision support and pilot assistance. Existing aviation safety and certification standards such as ARP4754A and DO-178C were developed under assumptions of determinism explicit requirements and complete behavioral specification which do not directly apply to learning-enabled systems. This mismatch has created uncertainty regarding how artificial intelligence enabled avionics can be safely assured and certified. This paper presents a system safety approach for assuring artificial intelligence enabled functions within existing aviation certification frameworks. In this approach safety assurance is based on explicitly identifying the …
Real-Time Fused Sensor System For Early Onboard Detection Of Weather Phenomena, Sanjana Singh
Real-Time Fused Sensor System For Early Onboard Detection Of Weather Phenomena, Sanjana Singh
Discovery Day - Daytona Beach
Weather-related hazards continue to be a major cause of operational interruptions and safety issues in the aviation sector. Atmospheric phenomena, including turbulence, microbursts, convective storms, and rapidly changing boundary-layer conditions, can arise quickly and often occur on spatial scales that are not adequately addressed by regional forecasting systems. These phenomena particularly endanger aircraft flying at low altitudes, such as general aviation planes, unmanned aerial vehicles (UAVs), and those during takeoff and landing Although meteorological forecasting systems and ground-based radar networks offer important regional insights, they may fail to detect localized atmospheric variations encountered along specific flight routes. Consequently, pilots and …