  Forecasting pedestrian trajectories in dynamic scenes remains a critical
problem in various applications, such as autonomous driving and socially aware
robots. Such forecasting is challenging due to human-human and human-object
interactions and future uncertainties caused by human randomness. Generative
model-based methods handle future uncertainties by sampling a latent variable.
However, few studies explored the generation of the latent variable. In this
work, we propose the Trajectory Predictor with Pseudo Oracle (TPPO), which is a
generative model-based trajectory predictor. The first pseudo oracle is
pedestrians' moving directions, and the second one is the latent variable
estimated from ground truth trajectories. A social attention module is used to
aggregate neighbors' interactions based on the correlation between pedestrians'
moving directions and future trajectories. This correlation is inspired by the
fact that pedestrians' future trajectories are often influenced by pedestrians
in front. A latent variable predictor is proposed to estimate latent variable
distributions from observed and ground-truth trajectories. Moreover, the gap
between these two distributions is minimized during training. Therefore, the
latent variable predictor can estimate the latent variable from observed
trajectories to approximate that estimated from ground-truth trajectories. We
compare the performance of TPPO with related methods on several public
datasets. Results demonstrate that TPPO outperforms state-of-the-art methods
with low average and final displacement errors. The ablation study shows that
the prediction performance will not dramatically decrease as sampling times
decline during tests.
