Flight Pushback Monitoring via Multi-Modal Intention Fusion
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Solution Overview
Problem
Current air traffic control systems lack the ability to effectively recognize and align controller and flight intentions, leading to potential taxiway conflicts due to delayed detection of unauthorized or unexecuted pushback instructions, resulting in safety hazards.
Innovation Solution
A flight pushback state monitoring method based on multi-modal data fusion, which involves constructing control intention recognition rules from control instruction texts and flight intention recognition models from surface monitoring videos, aligning intentions, and triggering alarms for inconsistencies, using keyword dictionaries and convolutional neural networks for real-time intention analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring of flight pushback state is implemented, then safety and conflict prevention are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The monitoring system is segmented into multiple independent modules: video acquisition module, control instruction recognition module, flight state recognition module, intention alignment module, and alarm module. Each module performs a specific function, making the overall complex system manageable and maintainable while achieving comprehensive real-time monitoring for safety improvement
Solution Approach 2:
An intention alignment rule acts as an intermediary mechanism that bridges control intentions (from controllers) and flight intentions (from flight actions). This intermediary component processes and compares the two types of intentions systematically, enabling automated conflict detection without requiring direct complex interaction between all system components
2Measurement precision
If multi-modal data fusion is used to recognize controller and flight intentions, then intention recognition accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system merges multiple data modalities (video data from surface monitoring, text data from control instructions, and flight state data) into a unified intention recognition framework. By combining these different data sources through data fusion technology, the system achieves higher intention recognition accuracy while managing processing complexity through integrated architecture
Solution Approach 2:
Traditional manual monitoring methods are replaced with automated image recognition algorithms and natural language processing techniques. The flight state recognition model uses computer vision to automatically identify flight states from video, while the control instruction recognition module uses text processing to extract controller intentions, substituting mechanical human analysis with automated computational systems
3Loss of time
If automated intention alignment and conflict detection are implemented, then response time is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by continuously pre-processing and analyzing control instructions and flight states in real-time before conflicts occur. The intention alignment rule is established in advance, enabling the system to proactively detect potential conflicts and trigger alarms before actual taxiway conflicts happen, thus reducing response time
Solution Approach 2:
The system implements a feedback mechanism where the alarm module continuously monitors alignment results between control and flight intentions. When inconsistencies are detected, the system provides immediate feedback through alarms to controllers and relevant personnel, enabling rapid response. This closed-loop feedback system reduces response time by ensuring timely detection and notification of potential conflicts
Data Source
AI summary
A flight pushback state monitoring method based on multi-modal data fusion comprises: 1, constructing a control intention recognition rule, and recognizing a pushback intention from a control instruction sent by a controller; 2, constructing a flight intention recognition model, extracting an aircraft action from a real-time monitoring video, and capturing a flight intention; and 3, constructing an intention alignment fusion rule, and judging whether control intention information conflicts with flight intention information; by fusing the control intention and the flight intention, the method can realize the following auxiliary functions: timely judging whether the aircraft follows the pushback instruction sent by the controller, if a captain does not act according to the control instruction or acts arbitrarily without a control instruction, giving an inconsistent alarm, and a function of monitoring the flight pushback state is implemented.