Unmanned Moving Object Anomaly Detection Using Message ID Sequences
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Solution Overview
Problem
Existing anomaly detection methods for unmanned moving objects, such as drones and autonomous vehicles, are inefficient due to the high overhead required for processing and combining various sensor data, making it difficult for humans to promptly respond to anomalies.
Innovation Solution
A method using a deep learning framework that detects anomalies by analyzing a message ID sequence through a pre-trained neural network model, processing packet data collected from the unmanned moving object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data processing methods are used for anomaly detection, then detection capability is achieved, but data processing overhead becomes large
Solution Approach 1:
The patent extracts only the message ID field from packet data, separating it from the complete sensor data processing approach. By focusing solely on message ID sequences rather than processing all sensor data, the system achieves anomaly detection with significantly reduced computational overhead while maintaining detection capability
Solution Approach 2:
The patent uses message ID sequences as a simplified copy or representation of the actual system state, rather than processing the complete sensor data sets. This substitution allows anomaly detection through a lighter data structure that preserves essential patterns without the burden of full sensor data processing
2Reliability
If sensor data processing is performed, then anomaly detection is possible, but response time is delayed due to extensive processing
Solution Approach 1:
By extracting only the message ID component from packet data, the system eliminates time-consuming processing of redundant sensor information. This extraction approach maintains anomaly detection accuracy by focusing on the sequential patterns that indicate system state changes, while dramatically reducing processing time for faster response
Solution Approach 2:
The system performs preliminary processing by extracting and storing message ID sequences in advance, organizing them for efficient pattern matching. This preliminary organization of data enables rapid anomaly detection when needed, reducing response time without sacrificing detection accuracy
Data Source
AI summary
Disclosed is a method of detecting anomaly of an unmanned moving object by using a message ID sequence, and the method includes: collecting packet data generated in the unmanned moving object; pre-processing the collected packet data; and detecting an anomaly of the unmanned moving object based on a message ID pattern of the packet data by inputting the pre-processed packet data to a pre-trained neural network model.