Autonomous System Testing via Probabilistic Model Sampling
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Solution Overview
Problem
Existing testing frameworks for autonomous systems face challenges in covering the wide range of real-world scenarios due to the infinite variations in continuous parameters, leading to limitations in finite number of concrete tests that can be executed, often resorting to discretization of parameters which may not accurately represent the system's performance across the entire parameter space.
Innovation Solution
A method utilizing a probabilistic model and kernel to generate samples from raw parameter values, allowing for continuous sampling and iterative testing to estimate the system's performance across the entire parameter space, prioritizing tests on the boundary of passing and failing, and updating the model based on evaluation results to refine the testing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If continuous parameters are used to represent all possible scenario variations, then the testing framework can cover the entire parameter space, but the number of tests becomes infinite and cannot be executed within finite computation budget
Solution Approach 1:
The patent uses a probabilistic model to generate synthetic test samples that copy the essential characteristics of continuous parameter variations without actually executing all possible tests. The model learns from a finite set of evaluated samples and generates representative samples that capture the behavior across the entire parameter space, effectively copying the outcomes of infinite tests through a finite number of evaluations.
Solution Approach 2:
The patent replaces the mechanical execution of infinite tests with a probabilistic modeling approach. Instead of mechanically running every possible test case, the system uses a learned probability distribution to simulate and predict test outcomes, substituting physical test execution with computational modeling to handle the infinite parameter space.
2Productivity
If parameters are discretized into buckets or bins, then the number of tests becomes finite and executable, but the testing framework loses accuracy by limiting parameters to artificial discrete values
Solution Approach 1:
The patent changes the fundamental representation of parameters from discrete buckets to continuous probability distributions. Instead of assigning parameters to fixed discrete bins, the system uses learned probability density functions that continuously model parameter variations, allowing for precise performance estimation across the entire continuous parameter space while maintaining finite test execution.
Solution Approach 2:
The patent implements an iterative feedback loop where test results are used to update the probabilistic model. The model generates samples, tests are executed, and the results feed back into refining the probability distribution. This feedback mechanism allows the system to progressively improve its accuracy in estimating performance across the parameter space, achieving both finiteness of tests and precision of measurement.
Data Source
AI summary
A method includes generating a first sample including first raw parameter values of a first modifiable parameters by a probabilistic model and a kernel and executing a first test of a virtual driver of an autonomous system according to the first sample to generate a first evaluation result of multiple evaluation results. The method further includes updating the probabilistic model according to the first evaluation result and training the kernel using the first evaluation result. The method additionally includes generating a second sample including second raw parameter values of the parameters by the probabilistic model and the kernel and executing a second test of a virtual driver of an autonomous system according to the second sample to generate a second evaluation result of the evaluation results. The method further includes presenting the evaluation results.