GPT4STP: A Novel Ship Trajectory Prediction Method Based on Pre-trained Large Language Model

Ship trajectory prediction plays a pivotal role in maritime navigation, facilitating efficient traffic management, collision avoidance, and route optimisation, particularly in the development and operation of Maritime Autonomous Surface Ships (MASS). This paper introduces GPT4STP, a novel framework that leverages transformer-based architectures inspired by Large Language Models (LLMs) for accurate and robust ship trajectory forecasting. By incorporating advanced techniques such as instance normalisation, patching, and fine-tuned positional embeddings, GPT4STP effectively captures both local and global spatial-temporal dynamics with exceptional precision and robustness in trajectory data. The model is evaluated using Automatic Identification System (AIS) datasets from two complex maritime regions: the Chengshan Jiao Promontory (CSJ) and Zhoushan Archipelago (ZS). Experimental results demonstrate GPT4STP’s superior performance across key metrics, including Average Displacement Error (ADE), Final Displacement Error (FDE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). Compared to existing methods, GPT4STP achieves remarkable improvements in prediction accuracy and robustness, particularly in complex maritime environments. Beyond its technical achievements, GPT4STP offers significant practical implications for the maritime industry. By enhancing the predictive capabilities of MASS, the framework helps ensure safe and efficient maritime operations, contributing to reduced collision risks, optimised routes, and sustainable navigation. This research underscores the transformative potential of integrating cutting-edge artificial intelligence methodologies, like those inspired by LLMs, into maritime applications. The success of GPT4STP highlights a promising direction for future research, emphasising the role of AI-driven solutions in advancing autonomous maritime systems and improving overall maritime safety and efficiency.