Robotic Simulation Testing Using Boundary-Guided Failure Search
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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 desired safety level, as they often focus on worst-case failures rather than salient failure scenarios, leading to redundant and costly testing.
Innovation Solution
A directed testing method that uses a performance predictor to probabilistically predict pass or fail outcomes for robotic systems, incorporating a hierarchical model to handle non-numerical outputs and guide the search towards salient failure scenarios by mapping performance category boundaries in the parameter space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simulation-based testing is used to evaluate autonomous vehicle safety, then testing efficiency is improved, but the number of simulations required increases significantly to achieve desired safety levels
Solution Approach 1:
The patent transforms the testing approach by changing parameters from uniform random sampling to targeted sampling based on performance category boundaries. By identifying and testing scenarios near decision boundaries where pass/fail outcomes change, the system achieves higher testing efficiency with fewer simulations required to reach desired safety confidence levels.
Solution Approach 2:
The system performs preliminary analysis to identify salient failure scenarios and performance category boundaries before conducting full-scale testing. By pre-characterizing the parameter space and locating critical regions, the testing process can focus computational resources on the most informative scenarios, reducing the total number of simulations needed.
2Ease of manufacture
If traditional random sampling methods are used for scenario testing, then implementation simplicity is maintained, but testing coverage of critical failure scenarios is insufficient
Solution Approach 1:
The patent replaces traditional mechanical random sampling methods with a data-driven approach using performance predictors and boundary identification algorithms. This substitution maintains implementation feasibility while dramatically improving the ability to detect critical failure scenarios by targeting tests at the most informative parameter space regions.
Solution Approach 2:
The system introduces performance predictors and boundary identification mechanisms as intermediaries between the test scenario generator and the evaluation process. These intermediaries analyze scenario outcomes and guide subsequent test generation toward unexplored or critical regions of the parameter space, improving coverage without requiring complete exhaustive testing.
3Reliability
If comprehensive safety testing is performed to guarantee minimum safety levels, then safety confidence is improved, but computational resource requirements increase
Solution Approach 1:
The patent applies partial action by focusing testing efforts on the most critical and informative scenarios rather than attempting exhaustive coverage of all possible scenarios. By identifying and prioritizing tests near performance category boundaries and salient failure modes, the system achieves adequate safety confidence with reduced computational resource consumption.
Solution Approach 2:
The testing system dynamically adapts its behavior based on accumulated test results. As the performance predictor learns from previous outcomes, it dynamically adjusts which scenarios are selected for testing, concentrating computational resources on regions of the parameter space that provide the most information for safety assessment, thereby reducing overall resource requirements.
Data Source
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 multiple performance evaluation rules. A performance predictor is trained to probabilistically predict a pass or fail result for each rule at each point in the parameter space. An overall acquisition function is determined as follows: if a pass outcome is predicted at a given, the performance evaluation rule having the highest probability of an incorrect outcome prediction at determines the acquisition function; whereas, if a fail outcome is predicted at a given point for at least one rule, then the acquisition function is determined by the performance evaluation rule for which a fail outcome is predicted with the lowest probability of an incorrect outcome prediction.