Road Side Unit Anomaly Detection via Neural Network Grouping
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Solution Overview
Problem
In intelligent transport systems, anomalies in road side units can lead to inefficient data transmission and increased radio resource consumption, affecting system performance due to incorrect signal processing and retransmission requests.
Innovation Solution
A method using an anomaly detection apparatus that receives traffic data from multiple road side units, groups them based on correlation distances, generates input data for an artificial neural network, and detects anomalies by comparing error values with preset thresholds, employing a convolutional autoencoder to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If road side unit transmits data using V2I method, then communication between road side unit and vehicle is enabled, but when road side unit is attacked it may intentionally change and transmit signals causing vehicle to not receive data appropriately
Solution Approach 1:
The patent introduces an anomaly detection apparatus as an intermediary component that monitors traffic data from road side units before they communicate with vehicles. This mediator detects anomalies in road side unit behavior and prevents compromised units from transmitting harmful signals, thereby resolving the contradiction between enabling V2I communication and preventing signal interference from attacked units.
Solution Approach 2:
The patent implements a feedback mechanism where the anomaly detection apparatus continuously monitors traffic data from road side units, analyzes patterns using machine learning models, and provides feedback to identify and isolate compromised units. This feedback loop enables the system to detect and respond to attacks in real-time, maintaining communication reliability while blocking harmful signals.
2Measurement precision
If road side unit determines error occurred intentionally, then it requests retransmission of data, but this causes additional consumption of radio resources and degradation in system performance
Solution Approach 1:
The patent applies preliminary action by having the anomaly detection apparatus identify and flag potentially compromised road side units before they transmit erroneous data that would trigger retransmission requests. By detecting anomalies in advance through pattern analysis of traffic data, the system prevents unnecessary retransmissions and conserves radio resources while maintaining accurate error detection.
3Measurement precision
If anomaly detection apparatus monitors traffic data from multiple road side units, then detection accuracy is improved, but system complexity increases due to data processing and grouping operations
Solution Approach 1:
The patent applies segmentation by dividing the monitoring task into distinct functional modules: a grouping module that clusters road side units based on spatial or functional relationships, a feature extraction module that processes traffic data, and an anomaly detection module that analyzes patterns. This segmentation reduces system complexity by organizing the data processing workflow into manageable, specialized components while maintaining high detection accuracy through collective analysis of multiple road side units.
Data Source
AI summary
A method of detecting an anomaly of a road side unit, which is performed by an anomaly detecting apparatus interworking with one or more road side units, may comprise: receiving traffic data from each of the one or more road side units; performing grouping on the one or more road side units based on the traffic data; generating input data of an artificial neural network based on a result of the performing of the grouping; generating output data of the artificial neural network based on the input data; and detecting an anomaly of each of the one or more road side units based on the output data.