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

• URL: http://arxiv.org/abs/2603.10597v1
• Date: Wed, 11 Mar 2026 09:51:22 GMT
• Title: Recover to Predict: Progressive Retrospective Learning for Variable-Length Trajectory Prediction
• Abstract summary: Trajectory prediction is critical for autonomous driving, enabling safe and efficient planning in dense, dynamic traffic.<n>Most existing methods optimize prediction accuracy under fixed-length observations.<n>A common strategy is to directly map features from incomplete observations to those from complete ones.<n>This one-shot mapping, however, struggles to learn accurate representations for short trajectories due to significant information gaps.<n>We propose a Progressive Retrospective Framework (PRF), which gradually aligns features from incomplete observations with those from complete ones.
• Score: 34.346912198889505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. This one-shot mapping, however, struggles to learn accurate representations for short trajectories due to significant information gaps. To address this issue, we propose a Progressive Retrospective Framework (PRF), which gradually aligns features from incomplete observations with those from complete ones via a cascade of retrospective units. Each unit consists of a Retrospective Distillation Module (RDM) and a Retrospective Prediction Module (RPM), where RDM distills features and RPM recovers previous timesteps using the distilled features. Moreover, we propose a Rolling-Start Training Strategy (RSTS) that enhances data efficiency during PRF training. PRF is plug-and-play with existing methods. Extensive experiments on datasets Argoverse 2 and Argoverse 1 demonstrate the effectiveness of PRF. Code is available at https://github.com/zhouhao94/PRF.

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