Den-TP: A Density-Balanced Data Curation and Evaluation Framework for Trajectory Prediction

ArXi:2409.17385v4 Announce Type: replace Trajectory prediction in autonomous driving has traditionally been studied from a model-centric perspective. However, existing datasets exhibit a strong long-tail distribution in scenario density, where common low-density cases dominate and safety-critical high-density cases are severely underrepresented. This imbalance limits model robustness and hides failure modes when standard evaluations average errors across all scenarios.