Road Friction Mapping Using Semivariogram Weighting
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Solution Overview
Problem
Current methods for determining road friction coefficients, such as active and passive measurement techniques, are complex and not easily applicable to series vehicles, and existing predictive methods like regression kriging and Gaussian Process-Based Approaches face challenges in providing accurate and reliable friction values, especially in spatial and temporal gaps.
Innovation Solution
A computer-implemented method using semivariogram modeling to determine the spatial and temporal relationships between friction measurement values, enabling the creation of a coefficient of friction map with high precision and accuracy, even in areas with limited data, by weighting friction values based on their temporal and spatial correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If active friction coefficient measurement methods are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses passive measurement by recording vehicle dynamics data (acceleration, yaw rate, steering angle) as a proxy for direct friction measurement. Instead of complex active measurement systems, it copies the vehicle's natural response to steering inputs to infer friction coefficients, thereby reducing device complexity while maintaining measurement capability
Solution Approach 2:
The patent replaces mechanical measurement systems (force sensors, braking mechanisms) with computational methods. It substitutes physical measurement apparatus with algorithms that process existing vehicle sensor data to calculate friction coefficients, eliminating the need for specialized measurement hardware
2Device complexity
If passive measurement methods are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent implements feedback through iterative optimization of the friction coefficient estimation. It uses the vehicle's actual trajectory and dynamics responses as feedback to adjust and refine the estimated friction coefficients, improving measurement precision through continuous validation and adjustment based on observed vehicle behavior
Solution Approach 2:
The patent changes parameters by using multiple vehicle operating conditions and steering scenarios to estimate friction coefficients. It varies the input parameters (steering angle, acceleration, velocity) and uses optimization to find the friction coefficients that best explain the observed vehicle responses across different conditions, thereby improving precision through multi-parameter analysis
3Device complexity
If friction measurement data is collected at discrete positions and times, then data collection complexity is reduced, but measurement precision deteriorates due to spatial and temporal gaps
Solution Approach 1:
The patent performs preliminary action by collecting friction measurement data during normal vehicle operation at various positions and times. It proactively gathers data from multiple sources and uses this pre-collected information to estimate friction coefficients at any required position and time, eliminating the need for targeted measurements at every query point
Solution Approach 2:
The patent introduces an intermediary computational model that interpolates and extrapolates friction coefficients between measured positions and times. It uses a friction coefficient map and optimization algorithms as intermediaries to translate discrete measurements into continuous spatial-temporal friction information, bridging the gaps between measurement points
Data Source
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AI summary
The invention relates to a method for ascertaining a frictional value (μ̑) of a roadway, having the step of providing a data set (14) of frictional measurement values (µ) of the roadway. Each of the frictional measurement values (μ) indicates a frictional coefficient of the roadway at a measurement position (si) at a measurement time (ti). The method is characterized in particular by the following steps: modeling a spatial and/or temporal relationship between at least one sub-quantity of the frictional measurement values (µ) on the basis of a semivariogram; ascertaining a weighting factor (wi) for each frictional measurement value (µ) relative to the time (t0) and roadway position (s0) of the prediction of the sub-quantity of frictional measurement values on the basis of the semivariogram; and ascertaining a frictional value (μ̑) for a roadway position (s0) and/or for a time (t0), thereby forming a weighted average value, wherein the weighted average value correlates to the sum of frictional measurement values (µ) of the sub-quantity of the frictional measurement values weighted with the respective weighting factors (wi). This allows frictional values of a roadway to be ascertained reliably, quickly, and with a high degree of precision.