Dissimilarity-Based Selection of Steer-by-Wire Simulation Models
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Solution Overview
Problem
Existing methods fail to systematically select representative product samples for serial products, particularly steering systems, to characterize model uncertainties due to manufacturing tolerances and aging, which are crucial for simulation-based approval and controller design in steer-by-wire and highly automated driving systems.
Innovation Solution
A computer-implemented method using dissimilarity metrics (gap, v-gap, L2 metrics) to calculate distances between parameterized simulation models, selecting a predetermined number of representative models that adequately cover the Operational Design Domain (ODD) with quantifiable residual uncertainty, enabling systematic characterization of model uncertainties and controller design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional expert-based sample selection methods are used, then the selection process is simple and fast, but the selected samples do not adequately represent the entire Operational Design Domain (ODD) and model uncertainties cannot be systematically characterized
Solution Approach 1:
The patent transforms the sample selection process from subjective expert judgment to objective parameter-based selection. By defining selection criteria through parameter ranges (minimum, maximum, nominal values) and using dissimilarity metrics to quantify representativeness, the method systematically selects samples that adequately cover the ODD while maintaining computational feasibility.
Solution Approach 2:
The patent replaces the manual expert-based selection mechanism with an automated computational system. The method uses algorithms to calculate dissimilarity metrics between samples and the ODD, automatically identifying representative samples without human intervention, thereby increasing precision while managing complexity through automation.
2Measurement precision
If more product samples are selected to characterize model uncertainties, then the characterization accuracy improves, but the number of samples required becomes excessively large and the process becomes impractical
Solution Approach 1:
The patent applies partial action by selecting only the minimum necessary number of samples (typically 3-5) that are sufficient to characterize model uncertainties. Instead of requiring comprehensive coverage of all possible parameter combinations, the method identifies a representative subset that provides adequate characterization, avoiding excessive sampling while maintaining accuracy.
Solution Approach 2:
The patent changes the approach from sampling all possible product variants to selecting a optimized subset based on parameter analysis. By using dissimilarity metrics and representativeness criteria, the method determines the optimal number and composition of samples needed for uncertainty characterization, reducing the quantity from potentially thousands to a manageable small number.
3Measurement precision
If simulation models include all product parameters to accurately represent real behavior, then the model accuracy improves, but the model complexity increases and computational efficiency decreases
Solution Approach 1:
The patent extracts only the essential parameters from the complete product parameter set. By identifying and selecting only those parameters that significantly impact the operational behavior and uncertainty characterization, the method creates simplified simulation models that maintain accuracy while reducing complexity and improving computational efficiency.
Solution Approach 2:
The patent changes the model from including all possible parameters to including only selected critical parameters. Through parameter analysis and dissimilarity metric evaluation, the method determines which parameters are necessary for accurate representation, excluding redundant parameters that would increase complexity without improving accuracy.
Data Source
AI summary
A computer-implemented method for determining representative parameterized simulation models of a parameterizable simulation model for a product, in particular a steer-by-wire steering system and/or a steering system for highly automated driving, is disclosed. The method includes (i) calculating a dissimilarity metric based on a pair of a plurality of pairs of parameterized simulation models, respectively, wherein each pair results in a distance, thereby resulting in a plurality of distances, optionally wherein the dissimilarity metric is based on a gap metric, a v-gap metric and/or an L2 metric, and (ii) selecting a predetermined number of the parameterized simulation models based on the plurality of distances, wherein a plurality of representative parameterized simulation models results.