Accurate trajectory prediction is crucial for safe and efficient autonomous driving, but handling partial observations presents significant challenges. To address this, we propose a novel trajectory prediction framework called Partial Observations Prediction (POP) for congested urban road scenarios. The framework consists of two stages: self-supervised learning (SSL) and feature distillation. In SSL, a reconstruction branch reconstructs the hidden history of partial observations using a mask procedure and reconstruction head. The feature distillation stage transfers knowledge from a fully observed teacher model to a partially observed student model, improving prediction accuracy. POP achieves comparable results to top-performing methods in open-loop experiments and outperforms the baseline method in closed-loop simulations, including safety metrics. Qualitative results illustrate the superiority of POP in providing reasonable and safe trajectory predictions.

翻译：准确的轨迹预测对于安全高效的自动驾驶至关重要，但处理部分观测场景存在显著挑战。为此，我们针对拥堵城市道路场景提出一种名为部分观测预测（POP）的新型轨迹预测框架。该框架包含两个阶段：自监督学习（SSL）与特征蒸馏。在SSL阶段，通过掩码机制与重建头构建重建分支，对部分观测的隐藏历史轨迹进行重建。特征蒸馏阶段将完全观测教师模型的知识迁移至部分观测学生模型，从而提升预测精度。在开环实验中，POP取得与顶尖方法相当的性能；在包含安全指标的闭环仿真中，POP显著优于基线方法。定性实验结果进一步证明了POP在提供合理且安全轨迹预测方面的优越性。