LLM Test Case Generation for Autonomous Vehicle Objective Coverage
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Solution Overview
Problem
Conventional test case preparation for autonomous vehicles is time-consuming and often fails to accurately test all intended objectives, lacking scalability with the growth of autonomous vehicle technology.
Innovation Solution
A system utilizing a language learning model (LLM) to generate test cases based on input data, including user-defined test objectives and expected results, enabling efficient and accurate testing of autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test cases are prepared manually by programmers, then test objectives can be accurately tested, but the preparation process is time-consuming and not scalable
Solution Approach 1:
The system enables self-service test case generation where the LLM automatically creates test cases based on test objectives without requiring manual programming. The LLM processes test objectives and generates corresponding test cases autonomously, eliminating the need for programmers to manually write each test case while maintaining accurate coverage of test objectives.
Solution Approach 2:
The patent replaces the mechanical manual writing process with an automated LLM-based system. Instead of programmers manually creating test cases through conventional writing processes, the LLM uses artificial intelligence to generate test cases automatically, substituting the mechanical manual process with an intelligent automated system that is both time-efficient and scalable.
2Adaptability or versatility
If conventional manual test case preparation is used, then testers can control the testing process, but the system lacks scalability with autonomous vehicle technology growth
Solution Approach 1:
The LLM-based system provides universality by being able to generate test cases for various autonomous vehicle systems and scenarios through a single unified approach. The system can handle different test objectives, vehicle types, and testing scenarios without requiring separate specialized processes for each, thereby achieving scalability while managing complexity through a universal generation framework.
3Reliability
If multiple iterations with modifications are performed, then test objectives can be achieved, but the process becomes more time-consuming
Solution Approach 1:
The system incorporates feedback mechanisms where the LLM analyzes test results and automatically generates modified test cases based on feedback from previous iterations. This feedback loop enables the system to learn from previous failures and successes, generating improved test cases that achieve test objectives more efficiently without requiring as many manual iterations and modifications.
Data Source
AI summary
A system for test case generation is provided. The system includes a user interface configured to receive as input data relating to an autonomous vehicle, test objectives for testing of the autonomous vehicle, and expected results of the testing of the autonomous vehicle. The system includes a database storing the data input into the user interface and data relating to previously derived test cases. The system includes a language learning model (LLM) unit trained on the previously derived test cases and configured to generate a test case based on the input data. The system includes a processing device that executes the LLM unit to analyze the autonomous vehicle, the test objectives for testing of the autonomous vehicle, and the expected results of the testing of the autonomous vehicle, and generates with the LLM unit the test case that meets the test objectives for testing of the autonomous vehicle.