Tire Grip Estimation Using Sensor Fusion Without Slip Induction
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Solution Overview
Problem
Existing methods for estimating tire grip during vehicle operation are primarily reactive, relying on tire slip measurements during traction or braking, and lack a proactive approach to provide accurate real-time estimates without inducing slip.
Innovation Solution
A method involving a tire-mounted sensor unit that generates data on tire characteristics, which is fused with vehicle and internet data to calculate a friction probability distribution using a grip estimation module, allowing for proactive estimation of peak tire grip levels during vehicle operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive estimation methods using tire slip measurements are employed, then tire grip estimation can be obtained during traction or braking conditions, but the method requires inducing slip through acceleration or braking which complicates the operation and cannot provide grip estimation during normal driving
Solution Approach 1:
The patent replaces the mechanical approach of inducing slip through acceleration or braking with a data processing approach using sensor fusion and machine learning algorithms. The system uses data from tire pressure sensors, temperature sensors, and vehicle dynamics sensors combined with internet data to estimate tire grip through computational models rather than mechanical slip induction.
Solution Approach 2:
The system performs preliminary data collection and processing during normal driving conditions, continuously monitoring tire parameters and road conditions to prepare grip estimation data before it is actually needed. This allows the system to provide proactive grip estimates without waiting for slip conditions to occur.
2Measurement precision
If laboratory or track testing is used to measure peak grip level, then accurate grip measurements can be obtained under controlled conditions, but the measurements are not available in real-time during actual vehicle operation
Solution Approach 1:
The system enables the vehicle itself to continuously monitor and estimate its own tire grip conditions using onboard sensors and processing capabilities. The tire-mounted sensor units and vehicle systems work together to provide self-diagnosis and real-time grip estimation without requiring external laboratory or track testing infrastructure.
Solution Approach 2:
The system continuously collects and processes tire parameter data, road condition data, and vehicle dynamics data in advance to prepare real-time grip estimates. This preliminary data preparation ensures that accurate grip information is available immediately when needed during actual vehicle operation, eliminating the time delay associated with scheduled laboratory testing.
3Loss of information
If multiple data sources including internet data are integrated, then the comprehensiveness of the estimation improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent divides the data processing system into modular components: tire-mounted sensor units that collect local tire data, vehicle systems that process vehicle dynamics data, and a grip estimation module that integrates all data sources including internet data. This segmentation allows each module to handle specific data types independently, reducing overall system complexity while maintaining information completeness.
Data Source
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AI summary
A method for estimating a grip of a tire (12) supporting a vehicle (14) is disclosed. The method comprises the steps of: generating a first set of data (82) from a tire-mounted sensor unit (26); generating a second set of data (84) from the tire-mounted sensor unit (26) and from data obtained from the vehicle (14); generating a third set of data (86) from data obtained from the vehicle (14) and from the internet; providing a grip estimation module (50); receiving the first, second and third sets of data (82, 84, 86) in the grip estimation module (50); calculating a friction probability distribution (88) with the grip estimation module (50) using the first, second and third sets of data (82, 84, 86); and inputting the friction probability distribution (88) into at least one vehicle system.