New framework predicts model transferability across 24 trajectory datasets
A latent embedding method quantifies dataset similarity with high accuracy.
Deep Dive
A framework is introduced that learns latent scene embeddings to quantify cross-dataset transferability for trajectory prediction models. Using distributional metrics across 24 major datasets, the study shows transferability scores strongly correlate with actual model performance. Accepted to ECCV 2026, this work provides practical guidance for dataset selection, pretraining, and building more generalizable foundation models for motion prediction.
Key Points
- Framework uses latent scene embeddings and distributional metrics to quantify dataset similarity.
- Validated across 24 major trajectory prediction datasets (nuScenes, Waymo, Argoverse, etc.).
- Transferability scores strongly correlate with actual cross-dataset model performance.
Why It Matters
Enables smarter dataset selection and pretraining for more robust autonomous driving and robotics prediction models.