Research on Decision-making of Autonomous Driving in Highway Environment Based on Knowledge and Large Language Model

Authors

Xiang Wang, Department of computer science and technology, Dalian University of Technology, Liaoning 116081, China
Guozhen Tan, Department of computer science and technology, Dalian University of Technology, Liaoning 116081, China

Abstract

Abstract: Aiming at the lack of continuous learning and interpretability of current autonomous driving system, a decision model with cognition, generalization and learning ability is proposed. The model utilizes large language model (LLM) and attention mechanisms to understand and explain driving scenes. the system can accumulate and learn from driving experiences, continuously improving its decisionmaking ability. In a simulation environment, the closed-loop test decision model is applied in high-speed scenarios.The simulation results show that the success rate of the knowledge-driven model is 7% and 4% higher than those of the rule-based and data-driven methods. Additionally, the model exhibits generalization and interpretability, thereby enhancing the reliability and safety of the automatic driving system.

Recommended Citation

Wang, Xiang and Tan, Guozhen (2025) "Research on Decision-making of Autonomous Driving in Highway Environment Based on Knowledge and Large Language Model," Journal of System Simulation: Vol. 37: Iss. 5, Article 12.
DOI: 10.16182/j.issn1004731x.joss.24-0065
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss5/12

First Page

1246

Last Page

1255

CLC

TP391.9

Recommended Citation

Wang Xiang, Tan Guozhen. Research on Decision-making of Autonomous Driving in Highway Environment Based on Knowledge and Large Language Model[J]. Journal of System Simulation, 2025, 37(5): 1246-1255.

DOI

10.16182/j.issn1004731x.joss.24-0065