Virtual Scenario Generation for Autonomous Machine Coverage Testing
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Solution Overview
Problem
Conventional systems for testing autonomous vehicles fail to provide an intuitive method for defining a wide range of scenarios and variations, leading to incomplete and error-prone manual scenario creation, which hinders effective verification and validation of safety-critical system behavior.
Innovation Solution
A declarative description approach using first-order logic allows for automated generation of virtual environment scenarios, enabling the system to create meaningful representatives and variations without manual coding, and automatically fill in undefined criteria, thereby providing an end-to-end solution for scenario definition and evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If scenarios are manually defined and coded in conventional systems, then specific test cases can be created, but the ability to comprehensively cover all possible scenarios and variations is limited and error-prone
Solution Approach 1:
The system automatically generates scenarios and variations by having the computational engine interpret high-level test objectives and autonomously create detailed scenario instances, eliminating the need for manual coding of each scenario while ensuring comprehensive coverage
Solution Approach 2:
The system performs preliminary analysis of test objectives and safety-critical requirements to pre-generate comprehensive scenario libraries that cover all possible test cases before actual testing begins, ensuring complete coverage without manual intervention during testing
2Productivity
If each scenario variation is manually defined, then specific test cases can be created, but the scalability to test millions of scenarios is hindered
Solution Approach 1:
The computational engine automatically generates millions of scenario variations by interpreting high-level test objectives and autonomously creating detailed scenario instances, enabling rapid scenario generation without manual coding effort
Solution Approach 2:
The system generates scenario variations by systematically changing parameters such as weather conditions, traffic patterns, vehicle states, and environmental factors based on predefined ranges and distributions, enabling scalable generation of millions of scenarios from a single test objective
3Reliability
If conventional module-level testing is used, then individual components can be verified, but end-to-end evaluation of safety-critical system behavior is ineffective
Solution Approach 1:
The simulation framework serves multiple functions simultaneously: it generates scenarios, executes end-to-end tests, collects performance data, and validates safety-critical behavior across the entire autonomous vehicle system, replacing multiple separate testing processes with a unified approach
Data Source
AI summary
In various examples, scenarios may be defined using a declarative description—e.g., defining a behavior of interest—that the present system may convert into a procedural description for generating one or more instances and/or variations of a scenario for testing an autonomous or semi-autonomous machine in a virtual environment. The system may execute observers or evaluators for testing the performance and accuracy of the machine and may compute coverage of various elements based on the generated virtual scenarios, and may feed the results back to the system to generate additional instances and/or variations where the coverage or accuracy is below a desired level. As a result, the system may include an end-to-end framework for generating scenarios in virtual environments, testing and validating the scenarios themselves, and/or testing and validating the underlying autonomous or semi-autonomous systems of the machine—all based on a declarative description.