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Accurate trajectory prediction is crucial for safe and efficient autonomousdriving, but handling partial observations presents significant challenges. Toaddress this, we propose a novel trajectory prediction framework called PartialObservations Prediction (POP) for congested urban road scenarios. The frameworkconsists of two stages: self-supervised learning (SSL) and featuredistillation. In SSL, a reconstruction branch reconstructs the hidden historyof partial observations using a mask procedure and reconstruction head. Thefeature distillation stage transfers knowledge from a fully observed teachermodel to a partially observed student model, improving prediction accuracy. POPachieves comparable results to top-performing methods in open-loop experimentsand outperforms the baseline method in closed-loop simulations, includingsafety metrics. Qualitative results illustrate the superiority of POP inproviding reasonable and safe trajectory predictions.

Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions