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Automotive Engineering ›› 2025, Vol. 47 ›› Issue (4): 587-597.doi: 10.19562/j.chinasae.qcgc.2025.04.001

   

Virtual Simulation Testing Method for Intelligent Vehicle Based on Large Language Model

Bing Zhu,Rui Tang,Jian Zhao,Peixing Zhang(),Wenxu Li,Jiasheng Li,Xuefeng Xu   

  1. Jilin University,National Key Laboratory of Automotive Chassis Integration and Bionics,Changchun 130022
  • Received:2024-06-07 Revised:2024-07-22 Online:2025-04-25 Published:2025-04-18
  • Contact: Peixing Zhang E-mail:zhangpeixing@jlu.edu.cn

Abstract:

In this paper a simulation testing method for intelligent vehicle based on a large language model is proposed to address the issues of heavy reliance on human resources and prominent efficiency bottlenecks in existing scenario based testing methods. Firstly, a simulation testing architecture for intelligent vehicle based on a large language model is designed, and corresponding data and simulation layers are established. On this basis, an intelligent car simulation testing process based on a large language model is constructed. Knowledge mining, model fine-tuning, and knowledge base enhancement retrieval application processes are designed for knowledge question answering tasks. Application paths for scenario type analysis, scenario element generation, and scenario toolchain invocation are designed for scenario generation tasks. For testing and evaluation tasks, a comprehensive application framework for testing scenario analysis, evaluation system construction, and simulation testing execution is designed. Finally, each task is tested. The results show that the testing method proposed in this paper can effectively solve different types of testing tasks and improve testing efficiency.

Key words: intelligent vehicle, simulation testing, large language model, scenario generation, automatic testing