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A Large Language Model-Based Game Equilibrium Selection Approach for Human-Machine Shared Driving
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DOI：10.1109/tits.2026.3692621.png)
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Abstract
En 中文
Human-machine shared driving (HMSD) has emerged as a crucial transitional paradigm before the widespread adoption of fully autonomous vehicles. However, existing research typically only considers either human-dominated or human-machine equal relationships, neglecting the fact that these two relationships alternate during driving, which leads to a gap between theory and reality. To address this issue, this study proposes a large language model (LLM)-based game equilibrium selection approach for human-machine shared driving authority allocation. Firstly, a game equilibrium selection model is developed to seamlessly transition between Stackelberg equilibrium and Nash equilibrium, addressing human-dominated and human-machine equal relationships, respectively. The selection process is implemented using an LLM, which bases its decisions on scenario understanding. To enhance the LLM’s scenario understanding performance, a set of indicators capturing human-machine conflicts, driver involvement, and collision risks is introduced as prior knowledge. Furthermore, an LLM-based scenario-understanding module is designed to embed knowledge into the LLM and enable it to function effectively within the HMSD system. Finally, a human-in-the-loop experiment is conducted to validate the proposed strategy. The results show that LLMs can understand the provided knowledge, flexibly adapt to different scenarios, and accurately grasp human-machine interactions. Moreover, the proposed strategy effectively reduces human-machine conflicts, better satisfies driver intentions, and reduces driver workload, showcasing the potential of LLM-based decision-making in human-machine interaction.
Keywords:
Large language model
human-machine shared driving
equilibrium selection
Nash equilibrium
Stackelberg equilibrium
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