Simulation Triage System for Autonomous Vehicle Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The testing of autonomous vehicles generates large amounts of result data that is difficult for humans to parse and review, making it challenging to identify unique or interesting simulation instances that require further analysis.
Innovation Solution
A triage system is implemented to categorize simulation instances based on their results, assigning them to high priority, review, or low priority categories, allowing for the identification and flagging of unique or interesting results for further human review or analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If testing software is used to test autonomous vehicles, then safety and reliability are improved, but large amounts of result data are generated that are difficult for humans to parse and review
Solution Approach 1:
The patent segments the large volume of simulation result data into individual simulation instances that can be evaluated and categorized separately. Each simulation instance is assessed against criteria to identify unique or interesting results, transforming an overwhelming data mass into manageable, discrete units for human review.
2Measurement precision
If all simulation instances are reviewed by humans, then identification of unique results is improved, but time and effort required for review increases significantly
Solution Approach 1:
The patent introduces an automated evaluation system as an intermediary between the simulation software and human reviewers. This intermediary automatically assesses simulation instances against predefined criteria, filtering and categorizing data to highlight only those instances requiring human attention, thereby reducing review time while maintaining identification accuracy.
3Reliability
If detailed analysis of all simulation data is performed, then completeness of review is improved, but complexity of the review process increases
Solution Approach 1:
The patent applies local quality by directing detailed human analysis only to simulation instances that meet specific criteria for uniqueness or interest, rather than requiring uniform detailed review of all data. This selective approach maintains review completeness for critical instances while reducing overall process complexity.
Data Source
AI summary
Techniques associated with categorizing simulation instances associated with simulation testing comprise categorizing simulation instances that produce unique or otherwise interesting results for further testing, review, and validation. In some cases, the system may be configured to determine low period of resource consumption and to schedule simulation testing during those periods of time.