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.
@article{arxiv.2506.03408,
title = {Trajectory Prediction Meets Large Language Models: A Survey},
author = {Yi Xu and Ruining Yang and Yitian Zhang and Jianglin Lu and Mingyuan Zhang and Yizhou Wang and Lili Su and Yun Fu},
journal= {arXiv preprint arXiv:2506.03408},
year = {2025}
}