  Human trajectory prediction is a practical task of predicting the future
positions of pedestrians on the road, which typically covers all temporal
ranges from short-term to long-term within a trajectory. However, existing
works attempt to address the entire trajectory prediction with a singular,
uniform training paradigm, neglecting the distinction between short-term and
long-term dynamics in human trajectories. To overcome this limitation, we
introduce a novel Progressive Pretext Task learning (PPT) framework, which
progressively enhances the model's capacity of capturing short-term dynamics
and long-term dependencies for the final entire trajectory prediction.
Specifically, we elaborately design three stages of training tasks in the PPT
framework. In the first stage, the model learns to comprehend the short-term
dynamics through a stepwise next-position prediction task. In the second stage,
the model is further enhanced to understand long-term dependencies through a
destination prediction task. In the final stage, the model aims to address the
entire future trajectory task by taking full advantage of the knowledge from
previous stages. To alleviate the knowledge forgetting, we further apply a
cross-task knowledge distillation. Additionally, we design a Transformer-based
trajectory predictor, which is able to achieve highly efficient two-step
reasoning by integrating a destination-driven prediction strategy and a group
of learnable prompt embeddings. Extensive experiments on popular benchmarks
have demonstrated that our proposed approach achieves state-of-the-art
performance with high efficiency. Code is available at
https://github.com/iSEE-Laboratory/PPT.
