Trajectory Prediction Meets Large Language Models: A Survey
It addresses the problem of enhancing trajectory prediction with language-driven techniques for researchers and practitioners in autonomous systems, but it is incremental as a survey rather than a novel method.
This survey tackles the integration of large language models (LLMs) into trajectory prediction for autonomous systems, providing a comprehensive overview by categorizing recent work into five directions and analyzing methods, design choices, and challenges.
Recent advances in large language models (LLMs) have sparked growing interest in integrating language-driven techniques into trajectory prediction. By leveraging their semantic and reasoning capabilities, LLMs are reshaping how autonomous systems perceive, model, and predict trajectories. This survey provides a comprehensive overview of this emerging field, categorizing recent work into five directions: (1) Trajectory prediction via language modeling paradigms, (2) Direct trajectory prediction with pretrained language models, (3) Language-guided scene understanding for trajectory prediction, (4) Language-driven data generation for trajectory prediction, (5) Language-based reasoning and interpretability for trajectory prediction. For each, we analyze representative methods, highlight core design choices, and identify open challenges. This survey bridges natural language processing and trajectory prediction, offering a unified perspective on how language can enrich trajectory prediction.