Tire Identification Gate for Dual-Wheel RFID Signal Interference
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Solution Overview
Problem
Existing tire identification technologies are inefficient and unreliable for long multi-wheeled motor vehicles, such as trucks and buses, due to the close proximity of dual-type wheels, leading to data loss, noise interference, and incorrect associations.
Innovation Solution
A computer-implemented method using K-means clustering, Davies-Bouldin Index, and Edit Distance metrics to efficiently identify tire configurations by sorting and grouping RFID data from dual-type wheels, determining actual tire positions, and assigning unique identifiers based on a predefined dictionary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If RFID reading is performed on multi-wheeled vehicles with dual-type wheels, then tire identification data can be collected, but data loss and noise interference occur due to close wheel proximity
Solution Approach 1:
The patent segments the tire identification process by spatial location. It divides the vehicle's wheel positions into distinct groups (e.g., front left, front right, rear left, rear right) and assigns specific RFID reading parameters to each segment. This segmentation prevents data confusion between adjacent dual wheels by treating each wheel position as an independent detection zone with its own identification protocol.
Solution Approach 2:
The patent introduces temporal dimension to the RFID reading process by implementing sequential reading at different time points as the vehicle moves through the detection gate. Instead of attempting to read all wheels simultaneously (which causes noise interference), the system reads wheels in a time-sequenced manner, allowing each wheel's signal to be captured distinctly without overlap from adjacent wheels.
2Speed
If traditional RFID reading methods are used on vehicles with many close wheels, then reading speed may be maintained, but identification precision deteriorates due to signal interference
Solution Approach 1:
The patent applies preliminary action by pre-configuring the RFID reading system with knowledge of the vehicle's wheel layout and expected positions. Before actual reading occurs, the system establishes detection zones for each wheel position and pre-sets the reading parameters. This preliminary preparation ensures that when wheels pass through the detection gate, the system is already optimized to capture each wheel's signal accurately without requiring post-processing correction.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors the quality of RFID signals received from each wheel position. When signal interference or data quality issues are detected, the system automatically adjusts reading parameters such as power levels, timing, or frequency for subsequent readings. This real-time feedback loop maintains both reading speed and identification precision by dynamically optimizing the reading process based on actual conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Achieves accurate and robust tire identification with low computational load, enabling energy-efficient operation and adaptability to various vehicle types, even in noisy environments.
Implementation Method 1
radiofrequency identification (RFID) technology is widely used in the automotive sector for identification and monitoring aims
Data Source
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AI summary
The invention concerns a computer-implemented method (4) for efficiently identifying tire configuration of motor vehicles (2) having tires fitted, each, with a respective wireless identification device. The method (4) comprises: receiving identification data indicative of the unique identifiers of the tires positioned on a left side of a given motor vehicle (2) of said motor vehicles; receiving identification data indicative of the unique identifiers of the tires positioned on the right side of the given motor vehicle (2); grouping the received identification data into a left-side data group and a right-side data group; sorting the identification data in the left-side group and the identification data in the right-side group according to time order of acquisition; for either the left-side group or the right-side group: iteratively applying a K-means clustering method to the sorted identification data thereby obtaining, at each iteration, a respective identification data clustering representing a respective candidate tire configuration of the given motor vehicle (2); evaluating the obtained identification data clusterings by a Davies-Bouldin Index metric thereby determining an estimated tire configuration of the given motor vehicle (2); determining an actual tire configuration of the given motor vehicle (2) by evaluating the estimated tire configuration by an Edit Distance metric. The method (4) further comprises: determining a respective position, in the determined actual tire configuration, of each tire on the side associated to the determined group and assigning to said position the respective unique identifier based on said determined actual tire configuration and on the sorted identification data; determining a respective position, in the determined actual tire configuration, of each tire on the opposite side of the given motor vehicle (2) and assigning to said position the respective unique identifier based on said determined actual tire configuration and on the sorted identification data.