Autonomous Operation Trajectory Planning for Maglev Train

Abstract

This paper proposes an agent-driven autonomous operation control architecture for maglev transportation system. To address the inherent contradiction between the uncertainty of intelligence and strict security of system, a hierarchical decisionmaking architecture is presented. It comprises three layers: organization, interface and action, where the decision-making domain of maglev agent is confined to the first two layers. Further, to validate both the architectures, a framework simulating agent decision-making process is constructed. It is implemented by the integration of three core components, including large language model (LLM), domain toolset and maglev train dynamics module. The results from testing autonomous operation planning task, demonstrate that maglev agent has established the cognition of operation task, and achieved appropriate decision-makings by leveraging domain-specific knowledge, tools and instructions, and information interaction. Ultimately, the proposed architectures transform the operation task expressed in natural language semantics into executable train operation strategy through multilayer decision conversion.