SOURCE-LINKED INTELLIGENCE

Uncertainty-Aware Trajectory Forecasting from Imperfect Tracking

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Most trajectory forecasting models are trained on clean annotated histories, and are often evaluated under the same idealized assumption, although practical deployments rely on trajectories produced by imperfect multi-object trackers. The real-world observations exhibit localization jitter, missed or unstable detections, and data-association ambiguity, which are usually either ignored or removed through denoising. This paper instead treats tracking-derived reliability cues as an informative signal to be propagated to the predictor. We propose a plug-in uncertainty-aware formulation in which ea

Read original source ↗ Open in workspace

recordType

paper

region

Global