Flight Simulation Competency Analysis Using Maneuver Detection
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Solution Overview
Problem
Current pilot training programs are expensive, labor-intensive, and time-consuming, lacking objective metrics and real-time assessment capabilities, and do not leverage advanced technologies like machine learning and artificial intelligence.
Innovation Solution
A data-driven approach using machine learning and artificial intelligence to analyze flight simulation data, automatically assessing pilot proficiency and competency by identifying flight maneuvers, generating performance metrics, and evaluating core competencies in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation and subjective analysis by flight instructors are used, then pilot training can be conducted with human expertise, but the process becomes expensive, time-consuming, and lacks objective metrics
Solution Approach 1:
The patent replaces manual mechanical observation and subjective human assessment with an automated computer-based system that objectively measures pilot performance. The system uses software to automatically detect flight maneuvers, track performance metrics, and generate assessments, eliminating the need for continuous manual monitoring while providing precise, objective data-driven evaluations.
Solution Approach 2:
The system enables self-assessment by automatically analyzing flight simulation data and generating performance evaluations without requiring constant instructor intervention. The automated system performs its own analysis of pilot actions, compares them against performance norms, and produces competency assessments, allowing the training process to serve itself rather than requiring continuous human oversight.
2Productivity
If real-time automated analysis of flight simulation data is implemented, then objective assessments can be made efficiently, but the system must process large amounts of data continuously without introducing delays
Solution Approach 1:
The system performs preliminary actions by pre-defining performance norms and thresholds for various flight maneuvers before actual assessment occurs. These pre-established criteria allow the system to rapidly compare real-time flight data against known standards without needing to analyze every detail from scratch, enabling fast real-time assessments with minimal processing delay.
Solution Approach 2:
The patent segments the complex flight simulation data into distinct flight maneuvers and performance metrics. By dividing the continuous data stream into discrete, manageable segments (individual maneuvers), the system can process and assess each segment independently and efficiently, maintaining real-time capability while handling the complexity of large datasets.
3Adaptability or versatility
If machine learning and artificial intelligence are integrated into pilot training, then advanced technological advantages can be leveraged, but the system complexity and computational requirements increase
Solution Approach 1:
The patent introduces performance norms as an intermediary layer between raw flight simulation data and final competency assessments. These pre-computed norms act as mediators that simplify the analysis process, allowing the system to compare flight data against established standards rather than performing complex real-time machine learning inference, thus reducing computational complexity while maintaining advanced analytical capability.
Data Source
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AI summary
In an embodiment, the disclosed technologies receive flight simulation data from a flight simulator computer, input the flight simulation data into a machine learning time-series classifier to identify a plurality of maneuvers from the flight simulation data, evaluate a plurality of performance metrics associated with the identified maneuvers to evaluate a pilot's proficiency of the plurality of maneuvers. In one aspect of the embodiment, the performance metrics associated with the identified maneuvers are used to evaluate a plurality of competency indicators measuring a pilot's aviation competency.