AI Flight Path Classification for Go-Around Detection Alerts
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Solution Overview
Problem
Flight dispatchers lack real-time awareness of go-arounds by aircraft, which are not reported in available data streams, complicating situational awareness and operational management.
Innovation Solution
A computer-implemented method using AI models, such as CNNs, to classify aircraft flight paths by analyzing historical and real-time data from various sources, including images and telemetry, to detect go-arounds and provide timely alerts to dispatchers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dispatchers manually monitor flight data streams to detect go-arounds, then they can identify deviations from scheduled flights, but they cannot detect go-arounds that are not reported in available data streams until much later
Solution Approach 1:
The patent introduces an intermediary AI system that processes multiple data streams (ADS-B, radar, flight management system data) to detect go-arounds. This intermediary system bridges the gap between unreported go-around events and dispatcher awareness, converting raw data into actionable alerts without requiring direct pilot reporting.
Solution Approach 2:
The patent replaces manual monitoring of flight data streams with an automated AI-based detection system. The mechanical process of dispatcher review is substituted with computational algorithms that continuously analyze position data, flight path deviations, and telemetry to automatically identify go-around events.
2Reliability
If dispatchers proactively check each aircraft landing status, then they may identify go-arounds, but this increases the time and effort required to manage multiple concurrent flights
Solution Approach 1:
The system enables self-service detection where the AI automatically monitors and identifies go-around events without requiring dispatcher intervention. The system serves itself by continuously processing data streams and generating alerts, freeing dispatchers from manual checking while maintaining high reliability in go-around identification.
Solution Approach 2:
The patent implements preliminary detection by continuously analyzing flight data streams before dispatchers need to take action. The system performs preliminary sorting and filtering of data to identify potential go-around events in advance, allowing dispatchers to receive ready-analyzed information rather than raw data requiring manual review.
3Loss of information
If multiple data streams are analyzed to detect unreported go-arounds, then situational awareness improves, but the complexity of the detection system increases
Solution Approach 1:
The patent segments the detection system into specialized modules: data collection from multiple sources, preprocessing and filtering, go-around detection algorithms, and alert generation. Each module handles a specific aspect of the detection process, making the overall complex system manageable through functional decomposition and independent development of components.
Data Source
AI summary
A method, system, computer program product and computer program for managing vehicle paths is presented. The method includes receiving a historical dataset, the historical dataset including a first set of images of vehicle paths associated with a first location and training an AI model with the first set of images to determine a first model and a set of classifications for the first set of images. The method further includes identifying a first vehicle, creating a first image of a first path taken by the first vehicle; applying the first image to the first model to determine a first classification of the set of classifications for the first path, and based on the first classification, performing an action related to the first vehicle.