Asynchronous Test Batch Evaluation for Vehicle Scenario Sampling
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Solution Overview
Problem
Existing vehicle simulation and testing methods are limited by their reliance on specialized scenario definition formats, lack of extensibility, and unrealistic closed-loop environments, leading to inefficiencies in scenario generation, coverage calculation, and performance evaluation, particularly in autonomous vehicle testing.
Innovation Solution
Implementing a scenario-agnostic internal representation of parameter spaces that map between open and custom representations, enabling parallelized and asynchronous evaluations to optimize information gain, and utilizing a unified parameter space for efficient scenario generation and testing across various modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If specialized scenario definition formats are used for vehicle simulation and testing, then scenario generation and testing can be performed, but the system lacks extensibility and efficiency in scenario generation, coverage calculation, and performance evaluation
Solution Approach 1:
The patent implements a unified parameter space representation that can represent multiple scenario formats (OSC, HIL, custom) through a single internal model structure. This universal representation enables the system to handle different scenario formats without requiring separate processing logic for each format, thereby improving extensibility while maintaining manageable complexity.
Solution Approach 2:
The patent introduces an internal parameter space as an intermediary layer between external scenario formats and the testing engine. This intermediary representation acts as a standardized intermediate form that simplifies the conversion and processing of various scenario formats, reducing the overall system complexity while enabling support for multiple formats.
2Productivity
If traditional sequential testing methods are used, then testing can be performed, but the computational efficiency and information gain rate are limited
Solution Approach 1:
The patent performs preliminary computation of the information gain metric before executing the full test batch. By evaluating the potential information gain of each test case in advance, the system can identify and prioritize the most valuable tests, enabling more efficient use of testing resources and reducing the time required to achieve meaningful test coverage.
Solution Approach 2:
The patent implements dynamic adjustment of test batches based on asynchronous evaluation results. The system can modify subsequent test batches in real-time based on information gained from previously completed tests, allowing the testing process to adapt and optimize its trajectory toward achieving test objectives more efficiently.
3Measurement precision
If complete test batches are waited for before evaluation, then accurate performance metrics can be calculated, but the loss of time increases due to inability to utilize intermediate results
Solution Approach 1:
The patent implements a feedback mechanism where asynchronous evaluation results from completed test cases are continuously fed back into the system. This feedback allows the system to update performance metrics and adjust subsequent testing in real-time, rather than waiting for complete batch results, thereby reducing waiting time while maintaining metric accuracy through continuous updates.
Solution Approach 2:
The system performs preliminary evaluation of individual test cases as they complete, rather than waiting for the entire batch. This preliminary evaluation of intermediate results allows the system to begin computing performance metrics before all tests are complete, reducing the effective waiting time while maintaining measurement accuracy through incremental computation.
Data Source
AI summary
The disclosure relates to a device for vehicle testing that is configured to perform operations including testing a first test sample batch comprising a plurality of first test samples to output a plurality of first test sample results. identifying one or more asynchronous test sample results of the plurality of first test samples before the first test sample batch is complete. computing a parametric metric based on the one or more asynchronous test sample results. and adjusting a second test sample batch based on the parametric metric.