Tire-Mounted Acceleration Sensing for Vehicle-Side Tire Type Detection
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Solution Overview
Problem
Conventional methods for determining tire type are limited as they require external vibration sensors on the road and cannot determine tire type from the vehicle side, making it difficult to improve running safety performance.
Innovation Solution
A method using an acceleration sensor attached to the inner surface of a tire tread to detect acceleration waveforms, extract feature vectors, and determine tire type using machine learning algorithms such as support vector machines, decision trees, or neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a vibration sensor is attached on the road surface to determine tire type, then tire type can be determined, but the system cannot determine tire type from the vehicle side and is limited to specific locations
Solution Approach 1:
Instead of placing the vibration sensor on the road surface to detect tire vibrations (external detection), the patent inverts the approach by attaching the acceleration sensor directly to the tire itself (internal detection). This allows the tire type determination system to move from fixed road locations to any vehicle equipped with the sensor, achieving both accurate measurement and location flexibility.
Solution Approach 2:
The tire type determination system becomes self-sufficient by integrating the acceleration sensor directly onto the tire. The tire itself generates the vibration data needed for classification, eliminating the need for external road-mounted sensors. This self-service approach enables the system to determine tire type anywhere the vehicle travels, not just at predetermined tollgate locations.
2Adaptability or versatility
If an acceleration sensor is attached to the tire to detect vibrations, then tire type can be determined from the vehicle side, but additional sensor attachment and processing complexity is required
Solution Approach 1:
The acceleration sensor attached to the tire serves multiple functions: it detects tire vibrations for type classification, can potentially monitor tire condition, and provides data for vehicle dynamics analysis. This multi-functionality justifies the added complexity by making the sensor attachment worthwhile for various automotive applications beyond just tire type determination.
Solution Approach 2:
The patent replaces the mechanical road surface vibration detection system with an electronic sensor-based system attached to the tire. Instead of using physical road infrastructure (tollgates with vibration sensors), the solution uses electronic acceleration sensors and digital signal processing to achieve the same tire type classification function, reducing mechanical complexity in the overall system.
3Measurement precision
If machine learning algorithms are used to classify tire types from acceleration waveforms, then accurate tire type determination is achieved, but computational processing requirements increase
Solution Approach 1:
The system performs preliminary processing of the acceleration waveform by extracting feature vectors that capture the essential characteristics of tire vibrations. This preprocessing step reduces the complexity of the data before it is fed into the machine learning algorithm, allowing accurate classification with reduced computational requirements during actual operation.
Solution Approach 2:
The patent transforms the raw acceleration waveform data into a different parameter space by extracting feature vectors (such as frequency domain characteristics, time-domain statistics, or spectral features). This parameter transformation reduces the dimensionality and complexity of the input data for the machine learning classifier, enabling accurate tire type determination with lower computational power requirements.
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
Enables accurate determination of tire type from the vehicle side, improving running safety performance by allowing for timely adjustments in vehicle control, such as ABS operation timing.
Implementation Method 1
a vibration sensor that measures vibration in a biaxial direction or a triaxial direction is attached on the road surface
Implementation Method 2
an acceleration sensor attached to an inner surface of a tire tread
Data Source
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Figure 5
AI summary
In order to provide a method and a device for determining a tire type of a running tire from a vehicle side, when determining the tire type, which is the type of the tire, from an output of an acceleration sensor attached to an inner surface side of a tire tread of the tire mounted on a vehicle, a feature vector is extracted from an acceleration waveform detected from the output of the acceleration sensor, and the tire type of the tire is determined, by a machine learning algorithm, from the extracted feature vector. The tire type is determined on the basis of the extracted feature vector and a determination model in which a feature vector obtained in advance for each tire type has been configured as learning data.