Robotic Simulation Testing with Hierarchical Score Prediction
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Solution Overview
Problem
Existing simulation-based testing methods for autonomous vehicles are inefficient and require a large number of simulations to achieve a minimum safety level, as they struggle to effectively identify salient failure scenarios, especially when performance is undefined or non-numerical.
Innovation Solution
A directed search method using a hierarchical Gaussian process model to predict performance scores, incorporating separate classification and regression models to handle numerical and non-numerical outcomes, guiding the search towards salient instances in the parameter space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-fidelity simulation is used to ensure safety testing accuracy, then measurement precision is improved, but computational resources and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by using a classification model to pre-screen parameter space points before full simulation. The classification model quickly identifies points likely to yield salient performance evaluations, allowing the system to perform targeted high-fidelity simulations only on promising candidates rather than exhaustively simulating all parameter combinations.
Solution Approach 2:
The patent segments the evaluation process into two distinct stages: a fast classification stage that filters parameter space points, and a detailed simulation stage that evaluates only selected points. This segmentation allows the system to achieve high measurement precision for salient scenarios while reducing overall time consumption by avoiding unnecessary full simulations.
2Reliability
If the number of simulation instances is increased to guarantee minimum safety levels, then reliability is improved, but computational resources and time consumption increase
Solution Approach 1:
The patent uses preliminary classification to identify parameter space points with high probability of yielding salient performance evaluations. By pre-screening points based on their likelihood to reveal critical safety issues, the system can achieve reliable safety guarantees with fewer actual simulation instances, thereby improving testing efficiency.
Solution Approach 2:
The classification model serves as an intermediary between the parameter space and full simulation. It acts as a filter that directs computational resources toward the most critical test cases, ensuring that safety reliability is maintained while reducing the total number of simulation instances required.
3Productivity
If directed search methods are used to identify salient scenarios, then productivity is improved, but difficulty of detecting and measuring performance increases when outcomes are non-numerical
Solution Approach 1:
The patent segments the performance evaluation into two types: numerical scores for quantifiable metrics and non-numerical scores for qualitative assessments. The classification model is designed to handle both types, assigning appropriate score types based on the nature of the performance metric. This segmentation allows directed search to efficiently identify salient scenarios while accommodating the complexity of non-numerical performance measurements.
Solution Approach 2:
The patent changes the parameter representation by introducing a hierarchical scoring system where performance outcomes can be numerical or non-numerical. The classification model learns to predict both the score value and its type, enabling the directed search to efficiently navigate the parameter space while properly handling the complexity of diverse performance metrics.
4Ease of operation
If random sampling of parameter space is used, then ease of operation is improved, but productivity deteriorates due to large number of simulations required
Solution Approach 1:
The patent maintains the simplicity of random sampling for generating initial parameter space points but adds a preliminary classification step that efficiently identifies salient scenarios among these samples. This preserves ease of operation while dramatically improving productivity by reducing the number of full simulations needed from exhaustive random sampling.
Solution Approach 2:
The patent segments the testing process into random sampling for broad coverage and classification-based filtering for targeted evaluation. This allows the system to maintain the operational simplicity of random sampling while achieving high productivity by focusing computational resources on the most critical identified scenarios.
Data Source
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AI summary
A directed search method is applied to a parameter space of a scenario for testing the performance of a robotic system in simulation. The directed search method is applied based on at least one performance evaluation rule that returns a performance score that can be numerical or non-numerical. A hierarchical score prediction model is constructed as follows. A score classification model is trained to probabilistically predict whether a point in the parameter space will result in a numerical or non-numerical outcome. A score regression model is trained to probabilistically predict a performance score for a given point, given that the score is numerical. The score classification and regression models are used to guide a directed search of the parameter space towards the most salient instances of the scenario.