TPM Sensor Auto-Location via ABS Tooth Count Correlation
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Solution Overview
Problem
Existing tire pressure monitoring systems face challenges in accurately auto-locating tire pressure monitoring sensors on vehicle wheels due to issues with antilock brake system (ABS) data noise, vehicle vibrations, and changes in wheel direction, which can lead to incorrect sensor positioning.
Innovation Solution
The system uses confidence interval analysis to correlate ABS sensor data with radio frequency transmissions from TPM sensors, analyzing the data in a histogram to determine the correct wheel location by identifying a normal distribution pattern, which minimizes the impact of extreme data points and ensures accurate sensor positioning despite changes in wheel direction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ABS data is used for auto-location of TPM sensors, then wheel position identification can be performed, but noise from vehicle vibrations and road noise causes incorrect sensor positioning
Solution Approach 1:
The patent extracts only the useful signal components from ABS data by identifying correlated tooth count patterns between ABS sensors and TPM sensors, while discarding noise components caused by vehicle vibrations and road noise. This extraction process separates the meaningful rotational position information from the harmful noise in the ABS data stream.
Solution Approach 2:
The system uses feedback by continuously monitoring ABS tooth count data and comparing it with TPM sensor transmissions to identify correlated patterns. The feedback loop allows the system to adjust and refine sensor location identification by analyzing the relationship between ABS sensor readings and TPM sensor signals, improving positioning accuracy despite noise interference.
2Measurement precision
If statistical analysis of ABS data is performed to determine wheel location, then auto-location can be achieved, but extreme data points from nuisance factors bias the location determination
Solution Approach 1:
The patent converts the harmful effect of extreme data points caused by nuisance factors into a beneficial filtering mechanism. By analyzing the distribution patterns of ABS tooth count data and identifying outliers that deviate from the normal operational range, the system eliminates biased data points and uses only the reliable correlated data for location determination, thereby improving both accuracy and reliability.
3Device complexity
If ABS tooth count data is analyzed without considering wheel direction changes, then processing is simplified, but counter clockwise movement causes mean shift and incorrect location identification
Solution Approach 1:
The system performs preliminary action by detecting and flagging wheel direction changes before they affect the location determination process. By monitoring the sequence of ABS tooth count data for patterns indicating counter clockwise movement, the system prepares to adjust its analysis approach in advance, preventing mean shift errors from occurring during the correlation analysis between ABS and TPM data.
Data Source
AI summary
A method for determining change of direction of a vehicle includes steps of maintaining a rolling window of ABS data indicative of ABS tooth count and capturing a relevant rolling window of ABS data at the predetermined one-measurement point; storing the rolling window of the ABS data indicative of ABS tooth in a buffer; monitoring the ABS data and detecting a valid stop event which causes the rate of change of ABS tooth count to substantially decrement to zero; and monitoring the ABS data and detecting a valid move event which causes the rate of change of ABS tooth count to substantially increment from zero. The method also includes steps of determining a pre-stop phase relationship between at least two wheels based on the ABS tooth count stored in the buffer immediately prior to the valid stop event; determining a post-start phase relationship between at least two wheels based on the ABS tooth count stored in the buffer immediately subsequent to the valid move event; and correlating the pre-stop phase relationship and the post-start phase relationship to determine change of direction and confidence level.