Aviation Safety

Latest papers

20 papers1 paper in the last four weeks, down 80% on the four weeks before. 0.0% of all new papers.

Semantic Causal-Factor Inference from Aviation Incident Narratives Using A Variational Autoencoder with Cosine-Similarity-Based Reconstruction
Oct 3, 2026AZIIDA NANYONGA1, HASSAN WASSWA, UGUR TURHAN +2Aviation Safety Variational Autoencoders
SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia · School of Systems and Computing, University of New South Wales, Canberra, ACT 2600, Australia · School of Science, University of New South Wales, Canberra, ACT 2612, Australia +1
Toward a Decision-Assurance Layer for AI-Assisted Flight Planning in Air Traffic Management
Sep 11, 2026Alexandre Barreto, Shou Matsumoto, Jorge Valverde-Rebaza +2AI Assurance Generative AI Evaluation
George Mason University · Department of Cyber Security Engineering George Mason University Fairfax, VA, USA · Tecnológico de Monterrey +1
Uncertainty-Aware Conflict Detection Against Operator-Conditioned Weather Hazards
Sep 10, 2026Balram Kandoria, Seulki Kim, Aryaman Singh SamyalUncertainty Quantification Air Traffic Management
SkyGrid, Austin, TX 78727, USA
FLY-EVAL++: An Evidence-Driven Evaluation Protocol for Safety-Constrained Flight Prediction with Large Language Models
Sep 3, 2026Yalun Wu, Junfeng Fang, Jiawei Wang +6LLM Safety Benchmarks LLM Safety Evaluation
NExT++ Lab, School of Computing, National University of Singapore · 3Xiamen University · University of Manchester +4
Formal, Executable and Explainable Runtime Monitoring of Spoken Air Traffic Control Operational Procedures
Aug 26, 2026Roberto Luvini, Giacomo Longo, Alessandro Armando +1Aviation Safety Runtime Verification
Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Italy · CASD - University School of Advanced Defense Studies, Rome, Italy
Beyond Semantic Accuracy: Consequence-Aware Evaluation for Safety-Critical Language Understanding
Aug 25, 2026Yujing Chang, Thinh Pham, Van-Phat Thai +4LLM Safety Benchmarks LLM Evaluation
1ATMRI, Nanyang Technological University (NTU), Singapore · Centre of AI Research, VinUniversity, Vietnam · School of Management, Indian Institute of Technology Mandi, India
Coverage-Driven Verification for Safety-by-Design in AI-Based Collision Avoidance Systems
Aug 21, 2026Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann +2AI Assurance Aviation Safety
Institute for AI Safety and Security, German Aerospace Center (DLR), Ulm, Germany · Institute for AI Safety and Security, German Aerospace Center (DLR), Sankt Augustin, Germany
On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks
Aug 20, 2026Johann Maximilian Christensen, Thomas Stefani, Elena Hoemann +2Aviation Safety Neural Network Verification
Institute for AI Safety and Security, German Aerospace Center (DLR), Sankt Augustin, Germany · Institute for AI Safety and Security, German Aerospace Center (DLR), Ulm, Germany
Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach
Aug 18, 2026Lu Xu, Xu Li, Linjiang Zheng +3Aviation Safety LLM Interpretability
College of Computer Science, Chongqing University, Chongqing, China · School of Statistics and Data Science, Shanghai University of International Business and Economics, Shanghai, China
AeroCopilotBench: Safety-Gated Evaluation of LLM Agents on Aircraft Emergency Procedures in an Executable Cockpit
Aug 17, 2026Yuchen Yuan, Zhenghuang Wu, Yuangan Li +2LLM Safety Benchmarks AI Agent Safety Benchmarks
School of Aeronautic Science and Engineering, Beihang University
Traceable LLM-Generated Hazard Scenarios for Operational Safety Analysis of Aviation Systems Using ASRS Reports
Aug 5, 2026Cristian Mascia, Roberto Pietrantuono, Daniel Rodriguez +1Text Generation Aviation Safety
University of Naples Federico II · Naples, Italy · University of Alcal´a +1
A Model for Mediating Multi-Modal Human Intent into Safe Maneuvers for UAVs
Jul 13, 2026Sofia Nelson, Dalal Alrajeh, Pedro Antonio Alarcon Granadeno +1Aerial Robotics Safe Motion Planning
University of Notre Dame Notre Dame, IN, USA · Imperial College London London, UK
Runtime Safety Filtering for Learned Small UAS Separation Policies under GNSS Degradation
Jul 10, 2026Alex Zongo, Peng WeiCollision Avoidance Safety Filtering
Department of Mechanical and Aerospace Engineering · George Washington University · Washington, D.C., USA
Mechanistic Interpretability for Learning Assurance of a Vision-Based Landing System
May 20, 2026Romeo Valentin, Olivia Beyer Bruvik, Marc R. Schlichting +1Aviation Safety Representation Disentanglement
Stanford Intelligent Systems Laboratory, Stanford University, Stanford, CA, USA
Towards Automated Air Traffic Safety Assessment Around Non-Towered Airports Using Large Language Models
May 12, 2026Torsten Darrell, Mahyar Ghazanfari, Jordan Kam +3Air Traffic Management Aviation Safety
Department of Mechanical and Aerospace Engineering, George Washington University · Aerospace Program, University of California, Berkeley · Department of Electrical Engineering and Computer Science, University of California, Berkeley +1
An Automatic Ground Collision Avoidance System with Reinforcement Learning
Apr 27, 2026Seyyid Osman Sevgili, Atahan Cilan, Mahir Demir +2RL Control Collision Avoidance
Turkish Aerospace Istanbul, Turkey
Building Trust in the Skies: A Knowledge-Grounded LLM-based Framework for Aviation Safety
Apr 10, 2026Anirudh Iyengar, Alisa Tiselska, Dumindu Samaraweera +1Aviation Safety LLM Reliability
Department of Mathematics, Embry-Riddle Aeronautical University, Daytona Beach, FL
World2Rules: A Neuro-Symbolic Framework for Learning World-Governing Safety Rules for Aviation
Mar 30, 2026Haichuan Wang, Jay Patrikar, Sebastian SchererAviation Safety Neurosymbolic Learning
Carnegie Mellon University, Pittsburgh PA 15213, USA
Safety Guardrails in the Sky: Realizing Control Barrier Functions on the VISTA F-16 Jet
Mar 29, 2026Andrew W. Singletary, Max H. Cohen, Tamas G. Molnar +1Safety-Critical Control Runtime Assurance
A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance
Feb 16, 2026Gabriele Immordino, Andrea Vaiuso, Marcello RighiAviation Safety Retrieval-Augmented Generation
School of Engineering Zurich University of Applied Sciences ZHAW Winterthur, Switzerland

Oct 3, 2026J/K move · Enter open · S save

Semantic Causal-Factor Inference from Aviation Incident Narratives Using A Variational Autoencoder with Cosine-Similarity-Based Reconstruction

AZIIDA NANYONGA1, HASSAN WASSWA, UGUR TURHAN, KEITH FRANCIS JOINER+1

SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia · School of Systems and Computing, University of New South Wales, Canberra, ACT 2600, Australia · School of Science, University of New South Wales, Canberra, ACT 2612, Australia+1

Aviation Safety Variational Autoencoders

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Aviation accident and incident investigations generate extensive unstructured textual information containing evidence relevant to the causes and contributing factors of safety occurrences. Automatically extracting such information is challenging because causal evidence may be distributed across long and complex investigation narratives. This study proposes a semantic causal-factor inference framework combining natural language processing with a variational autoencoder (VAE) to learn the relationship between aviation investigation narratives and expert-reported probable causes. Investigation narratives and their corresponding probable causes are transformed into numerical representations, after which the encoder maps narrative representations to a probabilistic latent space. The decoder estimates representations of the corresponding probable causes and is trained using an objective that combines Kullback-Leibler divergence with cosine-similarity-based semantic reconstruction. The framework was evaluated using 20,919 finalized U.S. National Transportation Safety Board investigation reports from 2005 to 2020. On the held-out test set, the predicted and expert-reported probable-cause representations achieved a mean cosine similarity of 0.786 (SD = 0.120). The predicted representations also yielded interpretable terms associated with causal information in the reports. The results demonstrate the potential of probabilistic latent representation learning for AI-assisted extraction of causal information from aviation safety narratives while retaining expert investigation as the basis for formal causal determination.