让自动驾驶模型学会从不完整轨迹中逐步推断未来路径。
Recover to Predict: Progressive Retrospective Learning for Variable-Length Trajectory Prediction
- 通过渐进式回溯模块，分步补齐缺失轨迹信息。
- 在Argoverse数据集上，短轨迹预测误差降低18.7%。
- 适合实际驾驶中观测不完整的场景，可无缝接入现有模型。
轨迹预测对自动驾驶至关重要，有助于在复杂动态交通中实现安全高效的路径规划。现有方法大多在固定长度观测下优化预测精度，但真实驾驶中常出现长度不一、信息不全的观测序列，导致模型表现下降。常见做法是将不完整观测直接映射到完整观测特征，但这种一次性映射难以准确学习短轨迹表示。为此，本文提出渐进式回溯框架（PRF），通过一系列回溯单元，逐步将不完整观测特征对齐至完整观测特征。每个单元包含回溯蒸馏模块（RDM）与回溯预测模块（RPM），其中RDM提取关键特征，RPM利用蒸馏特征恢复前序时间步。此外，提出滚动起始训练策略（RSTS），提升训练数据效率。PRF可即插即用，兼容现有方法。在Argoverse 1和Argoverse 2数据集上的大量实验验证了其有效性。代码已开源。
原文摘要 · Abstract (English)
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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