Road Surface Condition Estimation Using Tire Vibration Segmentation
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Solution Overview
Problem
Conventional methods for estimating road surface conditions during vehicular travel cannot distinguish between freshly-fallen snow and sherbet-like snow, limiting finer classification of snowy road conditions.
Innovation Solution
A method that divides tire vibration time-series waveforms into specific regions and uses discriminant functions to estimate road surface conditions based on band values within defined frequency ranges, allowing differentiation between snowy, sherbet-like snowy, and other road surface types using acceleration sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods using tire vibrations or sound pressure levels are used, then road surface conditions can be determined as high μ or low μ surfaces, but finer classification of snowy road conditions (such as freshly-fallen snow vs. sherbet-like snow) cannot be achieved
Solution Approach 1:
The patent divides the tire vibration waveform into five distinct regions (pre-leading-edge region R1, leading-edge region R2, pre-trailing-edge region R3, trailing-edge region R4, and post-trailing-edge region R5) based on contact patch timing. By analyzing band values in each region separately, the system achieves finer classification of snowy road conditions without requiring additional sensors or complex equipment.
2Measurement precision
If multiple frequency bands and regions are analyzed, then finer classification of snowy road conditions becomes possible, but calculation complexity increases
Solution Approach 1:
The patent applies different frequency band analyses to different regions of the tire vibration waveform. Specifically, it calculates band values P11 (2-8 kHz) and P12 (0.5-1.5 kHz) for the pre-leading-edge region, P21 (1-3 kHz) for the leading-edge region, and P51 (1-4 kHz) for the post-trailing-edge region. This localized frequency analysis enables precise differentiation between freshly-fallen snow and sherbet-like snow by comparing the relationships among these region-specific band values.
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 estimation of road surface conditions, including finer classification of snowy roads, improving vehicular safety by providing precise feedback for advanced control systems.
Implementation Method 1
an acceleration sensor installed within the tire
Data Source
Figure 1
Figure 2(a)~2(c)
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AI summary
A method capable of estimating a snowy road surface condition during vehicular travel in finer classification. In this method, tire vibrations in the circumferential direction, road surface temperature (T), and tire-generated sound are detected by an acceleration sensor, a road surface thermometer, and a microphone, respectively. Then band values P11, P12 and P13 for a pre-leading-edge region (R1), band values P21, P22 and P23 for a leading-edge region (R2), band values P31, P32 and P33 for a pre-trailing-edge region (R3), band values P41 and P42 for a trailing-edge region (R4), and band values P51, P52 and P53 for a post-trailing-edge region (R5) are calculated from the tire vibration data. A sound pressure level ratio (Q) = (PA/PB) , which is the ratio of a band power value (PA) of a low frequency band to a band power value (PB) of a high frequency band, is calculated from data on the tire-generated sound. And a road surface condition is estimated, using the band values (Pij), road surface temperature data (T), sound pressure level ratio (Q), and wheel speed data.