  Pedestrian trajectory prediction in dynamic scenes remains a challenging and
critical problem in numerous applications, such as self-driving cars and
socially aware robots. Challenges concentrate on capturing pedestrians' motion
patterns and social interactions, as well as handling the future uncertainties.
Recent studies focus on modeling pedestrians' motion patterns with recurrent
neural networks, capturing social interactions with pooling-based or
graph-based methods, and handling future uncertainties by using random Gaussian
noise as the latent variable. However, they do not integrate specific obstacle
avoidance experience (OAE) that may improve prediction performance. For
example, pedestrians' future trajectories are always influenced by others in
front. Here we propose GTPPO (Graph-based Trajectory Predictor with Pseudo
Oracle), an encoder-decoder-based method conditioned on pedestrians' future
behaviors. Pedestrians' motion patterns are encoded with a long short-term
memory unit, which introduces the temporal attention to highlight specific time
steps. Their interactions are captured by a graph-based attention mechanism,
which draws OAE into the data-driven learning process of graph attention.
Future uncertainties are handled by generating multi-modal outputs with an
informative latent variable. Such a variable is generated by a novel pseudo
oracle predictor, which minimizes the knowledge gap between historical and
ground-truth trajectories. Finally, the GTPPO is evaluated on ETH, UCY and
Stanford Drone datasets, and the results demonstrate state-of-the-art
performance. Besides, the qualitative evaluations show successful cases of
handling sudden motion changes in the future. Such findings indicate that GTPPO
can peek into the future.
