Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Joshua Henderson, Senior Alexander Landau Braxton Moore Jacky Yang
Lead Presenter's Name
Joshua Henderson
Lead Presenter's College
DB College of Aviation
Faculty Mentor Name
Dr. Ali Aljaroudi
Abstract
Pilot fatigue remains a persistent systemic operational hazard in commercial aviation because it can degrade attention, reaction time, decision-making, and crew coordination during safety-critical phases of flight. Although regulatory duty-time limits and Fatigue Risk Management Systems provide baseline protections, these controls rely heavily on voluntary reporting and post-event analysis, which may fail to detect fatigue in real time. The purpose of this project was to design and evaluate an artificial intelligence-enabled fatigue monitoring concept intended to reduce undetected pilot fatigue risk within the airline Safety Management System framework. The study applied a structured research approach including a literature review to identify fatigue control gaps and the creation of a Project Specification Outline detailing an infrared-based cockpit monitoring system. A Preliminary Hazard Analysis was used to compare risk levels before and after implementation as well as a cost-benefit and return-on- investment analysis. Results indicated that the highest fatigue-related hazard risk score decreased from 16 to 4, representing a 75% reduction, while the risk associated with fatigue underreporting decreased from 12 to 4, a 66.7% reduction. Estimated annual fatigue-related operational costs decreased from $10,510,653.23 to $8,661,267.63, producing annual savings of $1,849,385.60 and a projected payback period of approximately two years and nine months. These findings demonstrate that AI-enabled biometric monitoring can significantly reduce fatigue-related risk while enhancing proactive safety management in commercial aviation.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Industrial and Organizational Psychology Commons, Occupational Health and Industrial Hygiene Commons, Policy Design, Analysis, and Evaluation Commons
AI-Based Pilot Fatigue Monitoring for Enhanced Aviation Safety
Pilot fatigue remains a persistent systemic operational hazard in commercial aviation because it can degrade attention, reaction time, decision-making, and crew coordination during safety-critical phases of flight. Although regulatory duty-time limits and Fatigue Risk Management Systems provide baseline protections, these controls rely heavily on voluntary reporting and post-event analysis, which may fail to detect fatigue in real time. The purpose of this project was to design and evaluate an artificial intelligence-enabled fatigue monitoring concept intended to reduce undetected pilot fatigue risk within the airline Safety Management System framework. The study applied a structured research approach including a literature review to identify fatigue control gaps and the creation of a Project Specification Outline detailing an infrared-based cockpit monitoring system. A Preliminary Hazard Analysis was used to compare risk levels before and after implementation as well as a cost-benefit and return-on- investment analysis. Results indicated that the highest fatigue-related hazard risk score decreased from 16 to 4, representing a 75% reduction, while the risk associated with fatigue underreporting decreased from 12 to 4, a 66.7% reduction. Estimated annual fatigue-related operational costs decreased from $10,510,653.23 to $8,661,267.63, producing annual savings of $1,849,385.60 and a projected payback period of approximately two years and nine months. These findings demonstrate that AI-enabled biometric monitoring can significantly reduce fatigue-related risk while enhancing proactive safety management in commercial aviation.