通过隐空间场景表征量化轨迹数据集间的可迁移性。
Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings
- 用分布度量学习数据集的隐式表征，评估其相似性。
- 24个数据集实验显示，迁移分数与跨域性能强相关。
- 为模型预训练和数据集选择提供实证指导。
轨迹数据集的日益丰富推动了数据驱动运动预测的重大进展。然而，模型在某一数据集上训练后，常因场景布局、智能体行为和感知条件差异而难以泛化。本文提出一种框架，通过分布度量学习数据集的隐式表征并量化其相似性。该大规模研究涵盖24个主流数据集，包括最广泛使用的运动预测基准，结果表明生成的迁移分数与跨数据集模型性能高度相关。研究为数据集选择、预训练及大规模基础模型构建提供了实用指导，助力实现更通用、鲁棒的预测系统。
原文摘要 · Abstract (English)
The growing availability of trajectory datasets has fueled major advances in data-driven motion prediction. Yet, models trained on one dataset often fail to generalize beyond their training domain as a result of differences in scene layouts, agent behaviors, and sensing conditions. A framework that learns latent representations of datasets and quantifies their similarity using distributional metrics is presented. This large-scale study covers 24 major datasets, including the most widely used motion-prediction benchmarks, and shows that the resulting transferability scores strongly correlate with cross-dataset model performance. The results provide practical guidance for dataset selection, pretraining, and large-scale foundation models for motion prediction, paving the way toward more generalizable and robust predictive systems.
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