Semantic Parsing of Air Traffic Control Audio
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Solution Overview
Problem
Current aviation systems lack an efficient method to automatically interpret flight commands from air traffic control (ATC) radio communications, which can lead to delays and safety risks due to manual processing.
Innovation Solution
A semantic parser system that utilizes a Speech-to-Text module and a question-and-answer (Q/A) module to convert ATC audio into text and interpret flight commands, enabling automatic control of aircraft based on ATC communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing of ATC radio communications is used, then system complexity is low, but productivity is reduced and safety risks increase
Solution Approach 1:
The semantic parser is divided into distinct modules: audio signal reception module, speech-to-text conversion module, question-answering module, and flight command determination module. Each module handles a specific aspect of the processing pipeline, improving overall productivity while managing complexity through functional separation.
Solution Approach 2:
The patent introduces intermediate representations such as utterance hypotheses and linguistic hypotheses as mediators between audio input and final flight commands. These intermediaries enable systematic processing and validation at each stage, enhancing both speed and reliability of command interpretation.
2Measurement precision
If grammar rules are used for ATC communication parsing, then device complexity is reduced, but measurement precision of flight commands deteriorates
Solution Approach 1:
The patent replaces traditional grammar-rule-based mechanical parsing systems with neural network-based natural language processing. The Q/A module uses pre-trained language models to understand and interpret ATC communications, significantly improving measurement precision of flight commands by capturing nuanced linguistic patterns that rigid grammar rules cannot handle.
3Productivity
If automated speech recognition is implemented, then productivity improves, but reliability of command interpretation may worsen due to noise and ambiguity
Solution Approach 1:
The system performs preliminary actions by generating multiple utterance hypotheses with associated scores before final interpretation. The Q/A module then evaluates these hypotheses in context, allowing the system to recover from initial recognition errors and maintain high reliability even in noisy ATC environments.
Solution Approach 2:
The patent implements feedback mechanisms where the Q/A module evaluates utterance hypotheses and can request clarification or re-parsing when confidence is low. This feedback loop continuously improves reliability by validating interpretations against contextual expectations and correcting errors before final command execution.
Data Source
AI summary
The method S200 can include: at an aircraft, receiving an audio utterance from air traffic control S210, converting the audio utterance to text, determining commands from the text using a question-and-answer model S240, and optionally controlling the aircraft based on the commands S250. The method functions to automatically interpret flight commands from the air traffic control (ATC) stream.