Vehicle Model Correctness Quantification Using Error Thresholding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for quantifying the correctness of vehicle models are inefficient, requiring large amounts of data and being unsuitable for real-time applications, which is critical for autonomous and semi-autonomous vehicles to ensure safe maneuvers and risk assessment.
Innovation Solution
The method involves obtaining prediction errors by evaluating a vehicle model over a range of input parameters, determining a threshold for errors that follow a Generalized Pareto Distribution, parameterizing this distribution, and using it to quantify model correctness, allowing for reduced data requirements and real-time analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to quantify model correctness, then comprehensive model verification is achieved, but large amounts of data are required and real-time analysis is not feasible
Solution Approach 1:
The patent extracts only the essential information needed for model correctness quantification by focusing on prediction errors exceeding a dynamically determined threshold. Instead of analyzing all available data, the method identifies and processes only the critical error values that provide meaningful insight into model accuracy, thereby reducing data requirements while maintaining verification comprehensiveness
Solution Approach 2:
The patent introduces dynamic thresholding where the threshold ζ is determined adaptively based on the distribution of prediction errors. This dynamic approach allows the method to adjust to different operating conditions and data characteristics, enabling real-time analysis while maintaining statistical rigor in model correctness assessment
2Measurement precision
If comprehensive data is collected for model analysis, then model correctness is accurately quantified, but development and testing time increase
Solution Approach 1:
The method extracts only the necessary error information above a determined threshold, eliminating the need to process entire datasets during development and testing. This selective extraction significantly reduces computational overhead and time requirements while preserving the essential information needed for accurate model correctness assessment
Solution Approach 2:
The patent performs preliminary determination of the threshold ζ and identification of exceedances before conducting the full model correctness analysis. This preliminary action prepares the data in advance, filtering out irrelevant information and organizing only the critical error values needed for subsequent processing, thereby reducing overall analysis time
3Reliability
If real-time model verification is implemented, then safer vehicle operations are enabled, but computational complexity increases
Solution Approach 1:
The patent extracts only the critical error information that exceeds the determined threshold, reducing the computational burden of real-time verification. By focusing only on significant deviations rather than processing all prediction errors, the method enables real-time safety verification with reduced computational complexity
Solution Approach 2:
The patent transforms the model verification problem by changing the parameter focus from analyzing all error values to analyzing only exceedances above a dynamically determined threshold. This parameter transformation simplifies the computational task while maintaining the reliability needed for safe vehicle operations
Data Source
AI summary
A method for quantifying correctness of a vehicle model f(⋅). The method comprises obtaining prediction errors (A) by evaluating the model f(⋅) over a range of input parameters, determining a threshold ζ such that the prediction errors in excess of the threshold ζ, {εi: εi≤ζ}, follow a Generalized Pareto Distribution, GDP, and parameterizing a GDP based on the prediction errors in excess of the threshold ζ {εi: εi≤}. The method then quantifies correctness of the vehicle model f(⋅) based on the parameterized GDP.