Recover to Predict: Progressive Retrospective Learning for Variable-Length Trajectory Prediction

ArXi:2603.10597v1 Announce Type: cross Trajectory prediction is critical for autonomous driving, enabling safe and efficient planning in dense, dynamic traffic. Most existing methods optimize prediction accuracy under fixed-length observations. However, real-world driving often yields variable-length, incomplete observations, posing a challenge to these methods. A common strategy is to directly map features from incomplete observations to those from complete ones.