  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.
