Road Surface State Detection Using Slip Ratio Confidence Intervals
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Solution Overview
Problem
Existing methods for determining a road surface state, such as those described in JP 2002-362345A and DE102016203545 A1, require a threshold value for parameter determination, which limits their effectiveness when the road surface conditions change frequently, preventing accurate assessment of road surface state if the correlation coefficient does not reach a predetermined value.
Innovation Solution
A method that involves sequentially acquiring rotational speeds and driving forces of vehicle tires, calculating a slip ratio, and determining a road surface state by calculating a confidence interval width for the relationship between the slip ratio and driving force based on a large number of data sets in a predetermined zone, allowing for road surface state determination without relying on a threshold value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a threshold value for correlation coefficient is used to determine road surface state, then the determination can be simplified, but the system cannot acquire appropriate regression coefficient when the correlation coefficient does not reach the predetermined value
Solution Approach 1:
The patent changes the determination parameter from correlation coefficient threshold to confidence interval width. Instead of requiring the correlation coefficient to exceed a predetermined threshold, the system calculates the confidence interval width of the regression coefficient and compares it against a threshold. This parameter change allows the system to reliably determine road surface state even when correlation coefficients are low, as long as the confidence interval is sufficiently narrow, thereby resolving the contradiction between operational simplicity and determination reliability.
2Measurement precision
If regression coefficient is updated only when correlation coefficient reaches predetermined value, then data quality is ensured, but the system cannot adapt to frequently changing road surface conditions
Solution Approach 1:
The patent changes the update condition parameter from correlation coefficient threshold to confidence interval width. The regression coefficient is updated when the confidence interval width falls below a predetermined threshold, rather than requiring the correlation coefficient to exceed a fixed value. This allows the system to adapt to changing road surface conditions more rapidly while maintaining data quality, as the confidence interval approach is more sensitive to the actual precision of the regression relationship.
Solution Approach 2:
The patent introduces dynamic adaptation by continuously monitoring the confidence interval width and adjusting the regression coefficient update timing accordingly. Unlike the static correlation coefficient threshold approach, the confidence interval method dynamically responds to changes in data quality and road surface conditions, allowing the system to adapt its determination process to frequently changing environments while maintaining measurement precision.
3Adaptability or versatility
If confidence interval width is used instead of correlation coefficient threshold, then road surface state can be determined without acquiring threshold value during travel, but the calculation complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the confidence interval width threshold value during system initialization or in advance. This predetermined threshold is then used for real-time road surface state determination without requiring threshold acquisition during vehicle travel. The confidence interval width calculation itself is performed in real-time, but the comparison threshold is established beforehand, reducing the complexity of real-time operations while maintaining the adaptability benefits of the confidence interval approach.
Data Source
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AI summary
A determining method for determining a state of a road surface includes: sequentially acquiring a rotational speed of tires mounted on the vehicle, sequentially acquiring a driving force of the vehicle, calculating a slip ratio based on the sequentially acquired rotational speed of the tires, calculating a regression equation and a confidence interval width for a relationship between the slip ratio and the driving force, based on data sets of the slip ratio and the driving force in a predetermined zone, and determining a state of the road surface on which the vehicle travels, based on the confidence interval width calculated for the predetermined zone.