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 key stages: self-supervised learning (SSL) and feature distillation. POP first employs SLL to help the model learn to reconstruct history representations, and then utilizes feature distillation as the fine-tuning task to transfer knowledge from the teacher model, which has been pre-trained with complete observations, to the student model, which has only few observations. 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：精确的轨迹预测对于安全高效的自主驾驶至关重要，但处理部分观测数据仍面临重大挑战。为此，我们针对拥堵城市道路场景提出了一种名为"部分观测预测"（POP）的新型轨迹预测框架。该框架包含两个关键阶段：自监督学习（SSL）和特征蒸馏。POP首先通过SSL帮助模型学习重建历史表示，然后利用特征蒸馏作为微调任务，将经过完整观测预训练的教师模型的知识迁移至仅拥有少量观测数据的学生模型。在开环实验中，POP取得了与顶尖方法相当的结果，并在闭环仿真中（包括安全指标）全面超越了基线方法。定性结果表明POP在提供合理且安全的轨迹预测方面具有显著优势。