用思维链让大模型更准预测行人轨迹，还能按需调整路径。
GUIDE-CoT: Goal-driven and User-Informed Dynamic Estimation for Pedestrian Trajectory using Chain-of-Thought
- 用视觉提示+预训练编码器提升目标预测精度
- 通过思维链生成符合目标的真实轨迹，准确率领先
- 支持用户干预路径，适合交互式导航场景
尽管大语言模型在推理任务中表现优异，但在行人轨迹预测中仍面临两大挑战：视觉信息利用不足，以及难以预测完整轨迹。为此，我们提出基于思维链的面向目标与用户引导的动态轨迹估计方法（GUIDE-CoT）。该方法引入两个创新模块：(1) 目标导向的视觉提示，结合视觉提示与预训练视觉编码器，提升目标预测准确率；(2) 基于思维链的大语言模型，用于生成朝向预测目标的合理轨迹。此外，该方法支持可控轨迹生成，可灵活实现用户引导的路径修改。在ETH/UCY基准数据集上的大量实验表明，该方法达到当前最优性能，在轨迹预测精度和适应性方面均有显著提升。代码已公开于https://github.com/ai-kmu/GUIDE-CoT。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) have recently shown impressive results in reasoning tasks, their application to pedestrian trajectory prediction remains challenging due to two key limitations: insufficient use of visual information and the difficulty of predicting entire trajectories. To address these challenges, we propose Goal-driven and User-Informed Dynamic Estimation for pedestrian trajectory using Chain-of-Thought (GUIDE-CoT). Our approach integrates two innovative modules: (1) a goal-oriented visual prompt, which enhances goal prediction accuracy combining visual prompts with a pretrained visual encoder, and (2) a chain-of-thought (CoT) LLM for trajectory generation, which generates realistic trajectories toward the predicted goal. Moreover, our method introduces controllable trajectory generation, allowing for flexible and user-guided modifications to the predicted paths. Through extensive experiments on the ETH/UCY benchmark datasets, our method achieves state-of-the-art performance, delivering both high accuracy and greater adaptability in pedestrian trajectory prediction. Our code is publicly available at https://github.com/ai-kmu/GUIDE-CoT.
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