Scenario Difficulty Metrics for Efficient Autonomous Vehicle Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Testing autonomous vehicle components requires a large number of simulations, consuming significant computing resources and time, which can decrease safety and prevent other components from being tested.
Innovation Solution
A machine-learned model determines a difficulty metric for scenarios, allowing a subset of simulations to be selected, reducing the number of simulations needed while maintaining confidence intervals, and ensuring critical scenarios are included.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of simulations are run to test autonomous vehicle components, then the reliability of testing is improved, but the computing resources and time required increase significantly
Solution Approach 1:
The patent changes the parameter of scenario selection from uniform random sampling to difficulty-based sampling. By using a machine-learned model to assign difficulty metrics to scenarios and selecting scenarios above a threshold difficulty level, the system achieves more efficient testing with fewer simulations while maintaining reliability.
Solution Approach 2:
The patent segments the scenario space into different difficulty levels using machine-learned difficulty metrics. Scenarios are divided into easy, medium, and hard categories based on predicted difficulty, allowing the system to focus computational resources on the most challenging and critical scenarios for validation.
2Reliability
If a large number of simulations are run to test autonomous vehicle components, then the confidence in test results is improved, but the time required increases significantly
Solution Approach 1:
The patent changes the sampling parameter from uniform distribution to difficulty-threshold-based selection. By selecting only scenarios with difficulty metrics above a predetermined threshold, the system reduces the number of simulations needed while maintaining statistical confidence in the test results through targeted sampling of critical scenarios.
Solution Approach 2:
The patent performs preliminary action by using a machine-learned model to pre-rank and score scenarios based on their difficulty before actual simulation execution. This preliminary difficulty assessment allows the system to pre-select the most critical scenarios for testing, avoiding unnecessary simulations of easier scenarios.
3Adaptability or versatility
If all scenarios are simulated to ensure comprehensive testing, then the completeness of test coverage is improved, but the computational complexity increases
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different scenario types based on their difficulty metrics. Instead of uniform treatment, the system selectively simulates only those scenarios that exceed a difficulty threshold, applying computational resources locally to the most critical and challenging scenarios rather than uniformly across all scenarios.
Solution Approach 2:
The patent extracts and separates the most critical scenarios from the overall scenario set using machine-learned difficulty metrics. By extracting scenarios above a difficulty threshold and focusing testing efforts on these extracted critical scenarios, the system maintains comprehensive coverage of important cases while reducing overall computational complexity.
Data Source
AI summary
Simulation for testing and/or validating autonomous vehicle functions may comprise sampling a set of scenario data to determine a subset of the scenario data for simulating operation of the autonomous vehicle. Determining to include a first scenario in the subset may be based at least in part on one or more difficulty metrics determined by a machine-learned model for the first scenario.