LLM-Driven Functional Chain Generation for Vehicle Simulation
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Solution Overview
Problem
Current vehicle testing methods lack a scalable and standardized simulation framework for end-to-end validation of Advanced Driver Assistance Systems (ADAS) functionalities, requiring manual intervention and being device/technology non-agnostic, which complicates the testing and validation process due to diverse electrical and electronic components across different vehicle manufacturers.
Innovation Solution
A method and system using a Large Language Model (LLM) to generate functional chains of test devices in simulation environments by determining input and output ports and dependencies from natural language inputs, enabling the creation of device and technology-agnostic simulation platforms for integrated end-to-end vehicle functionality testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used for vehicle functionality validation, then testing can be performed with existing tools, but the process becomes time-consuming and lacks scalability
Solution Approach 1:
The system enables self-service automation where the LLM automatically generates functional chain configurations from natural language inputs without requiring manual setup. The system autonomously compiles code, configures simulation environments, and executes test cases, eliminating the need for manual intervention while maintaining validation accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-configuring simulation environments and pre-compiling code before actual testing begins. The LLM generates complete functional chain configurations in advance based on natural language inputs, so that when testing starts, everything is already prepared and ready to execute automatically.
2Productivity
If standardized simulation framework is implemented, then scalability and automation are improved, but device compatibility across different manufacturers becomes more difficult to achieve
Solution Approach 1:
The system achieves universality by creating a standardized functional chain configuration format that can work across different device types and manufacturers. The LLM generates configurations using universal interfaces and abstractions that map to specific device implementations, allowing the same framework to accommodate diverse hardware while maintaining automated scalability.
Solution Approach 2:
The system introduces an intermediary layer of functional chain configurations that sits between the standardized simulation framework and specific device implementations. This intermediary configuration layer translates universal test requirements into device-specific settings, enabling both standardization and adaptability simultaneously.
3Reliability
If complex functional chains are built manually, then precise control over test configurations is achieved, but the complexity of setup and configuration increases significantly
Solution Approach 1:
The system replaces the manual mechanical process of configuring functional chains with an automated intelligent system. The LLM substitutes human operators in the configuration process, automatically generating precise test configurations from natural language inputs without requiring manual setup of complex connections between test devices.
Solution Approach 2:
The system creates reusable templates and configurations that can be copied and adapted for different test scenarios. Once a functional chain configuration is generated for one test case, it can be replicated and modified for similar tests, reducing the complexity of setting up complex functional chains repeatedly.
Data Source
AI summary
A method and system of generating functional chains of test devices of vehicles in simulation environments, is disclosed. A processor receives a natural language input and a set of functional executable elements of a set of test devices corresponding to a functionality of a vehicle from a user device. One or more input ports, one or more output ports, and a set of dependencies are determined for each of the set of functional executable elements from a plurality of dependencies stored in a database based on the natural language input, using a Large Language Model (LLM). A functional chain is generated of the set of functional executable elements based on the one or more input ports, the one or more output ports, and the set of dependencies.