  The prediction of humans' short-term trajectories has advanced significantly
with the use of powerful sequential modeling and rich environment feature
extraction. However, long-term prediction is still a major challenge for the
current methods as the errors could accumulate along the way. Indeed,
consistent and stable prediction far to the end of a trajectory inherently
requires deeper analysis into the overall structure of that trajectory, which
is related to the pedestrian's intention on the destination of the journey. In
this work, we propose to model a hypothetical process that determines
pedestrians' goals and the impact of such process on long-term future
trajectories. We design Goal-driven Trajectory Prediction model - a
dual-channel neural network that realizes such intuition. The two channels of
the network take their dedicated roles and collaborate to generate future
trajectories. Different than conventional goal-conditioned, planning-based
methods, the model architecture is designed to generalize the patterns and work
across different scenes with arbitrary geometrical and semantic structures. The
model is shown to outperform the state-of-the-art in various settings,
especially in large prediction horizons. This result is another evidence for
the effectiveness of adaptive structured representation of visual and
geometrical features in human behavior analysis.
