标题： 对比与压缩：学习短轨迹的轻量级嵌入 显示英文标题

标题： Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories

摘要： 能够以准确性和高效性检索语义和方向上相似的短程轨迹，是诸如运动预测和自主导航等下游应用的基础。 然而，现有的方法通常依赖于计算密集型的启发式算法或缺乏可解释性和可控性的潜在锚点表示。 在这项工作中，我们提出了一种新颖的框架，通过利用经过对比三元组损失训练的Transformer编码器来学习短轨迹的固定维度嵌入，强调了轨迹数据判别特征空间的重要性。 我们在对比学习范式内分析了余弦和基于FFT的相似性度量的影响，重点关注捕捉短期操作中特有的细微方向意图。 我们在Argoverse 2数据集上的实证评估表明，由余弦相似性目标塑造的嵌入在语义和方向属性上对轨迹的聚类效果优于基于FFT的基线，在检索任务中表现更佳。 值得注意的是，我们展示了紧凑型Transformer架构（即使使用低维嵌入，例如16维，但定性上甚至可以降至4维）在检索性能（minADE, minFDE）和计算开销之间取得了令人信服的平衡，与实时系统中对可扩展且可解释的运动先验的需求相一致。 由此产生的嵌入为轨迹数据提供了一种紧凑、语义丰富且高效的表示形式，为启发式相似性度量提供了一个稳健的替代方案，并为更透明和可控的运动预测管道铺平了道路。 显示英文摘要

摘要： The ability to retrieve semantically and directionally similar short-range trajectories with both accuracy and efficiency is foundational for downstream applications such as motion forecasting and autonomous navigation. However, prevailing approaches often depend on computationally intensive heuristics or latent anchor representations that lack interpretability and controllability. In this work, we propose a novel framework for learning fixed-dimensional embeddings for short trajectories by leveraging a Transformer encoder trained with a contrastive triplet loss that emphasize the importance of discriminative feature spaces for trajectory data. We analyze the influence of Cosine and FFT-based similarity metrics within the contrastive learning paradigm, with a focus on capturing the nuanced directional intent that characterizes short-term maneuvers. Our empirical evaluation on the Argoverse 2 dataset demonstrates that embeddings shaped by Cosine similarity objectives yield superior clustering of trajectories by both semantic and directional attributes, outperforming FFT-based baselines in retrieval tasks. Notably, we show that compact Transformer architectures, even with low-dimensional embeddings (e.g., 16 dimensions, but qualitatively down to 4), achieve a compelling balance between retrieval performance (minADE, minFDE) and computational overhead, aligning with the growing demand for scalable and interpretable motion priors in real-time systems. The resulting embeddings provide a compact, semantically meaningful, and efficient representation of trajectory data, offering a robust alternative to heuristic similarity measures and paving the way for more transparent and controllable motion forecasting pipelines.