用大模型语言能力提升轨迹预测，让系统更懂语义与推理。
Trajectory Prediction Meets Large Language Models: A Survey
- 将大模型语言能力融入轨迹预测五种新范式
- 通过语义理解提升预测场景认知与可解释性
- 适合关注智能驾驶、具身智能的科研人员
大语言模型（LLMs）的进展激发了将语言驱动技术引入轨迹预测的热潮。借助其语义理解和推理能力，LLMs 正重塑自动驾驶系统对轨迹的感知、建模与预测方式。本文全面综述这一新兴领域，将近期研究归纳为五个方向：(1) 基于语言建模范式的轨迹预测，(2) 使用预训练语言模型直接预测轨迹，(3) 语言引导的场景理解用于轨迹预测，(4) 语言驱动的数据生成用于轨迹预测，(5) 语言基的推理与可解释性分析。针对每类方法，分析代表性工作，提炼核心设计思路，并指出开放挑战。本综述连接自然语言处理与轨迹预测，提供语言如何增强轨迹预测的统一视角。
原文摘要 · Abstract (English)
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.
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