  Being able to safely operate for extended periods of time in dynamic
environments is a critical capability for autonomous systems. This generally
involves the prediction and understanding of motion patterns of dynamic
entities, such as vehicles and people, in the surroundings. Many motion
prediction methods in the literature can learn a function, mapping position and
time to potential trajectories taken by people or other dynamic entities.
However, these predictions depend only on previously observed trajectories, and
do not explicitly take into consideration the environment. Trends of motion
obtained in one environment are typically specific to that environment, and are
not used to better predict motion in other environments. In this paper, we
address the problem of generating likely motion dynamics conditioned on the
environment, represented as an occupancy map. We introduce the Occupancy
Conditional Trajectory Network (OCTNet) framework, capable of generalising the
previously observed motion in known environments, to generate trajectories in
new environments where no observations of motion has not been observed. OCTNet
encodes trajectories as a fixed-sized vector of parameters and utilises neural
networks to learn conditional distributions over parameters. We empirically
demonstrate our method's ability to generate complex multi-modal trajectory
patterns in different environments.
