Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings

Theodor Westny; David Axelsson; Björn Olofsson; Erik Frisk

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arXiv · 2606.30777

Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings

Theodor Westny·David Axelsson·Björn Olofsson·Erik Frisk

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Abstract

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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Theodor Westny, David Axelsson, Björn Olofsson, Erik Frisk. 2026-06-29. Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings. https://arxiv.org/abs/2606.30777

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