Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings
For researchers in motion prediction, this provides practical guidance for dataset selection and pretraining to improve model generalization.
The paper introduces a framework that learns latent representations of trajectory datasets and quantifies their similarity using distributional metrics, showing that transferability scores strongly correlate with cross-dataset model performance across 24 major datasets.
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.