  Trajectory prediction is a critical part of many AI applications, for
example, the safe operation of autonomous vehicles. However, current methods
are prone to making inconsistent and physically unrealistic predictions. We
leverage insights from fluid dynamics to overcome this limitation by
considering internal symmetry in real-world trajectories. We propose a novel
model, Equivariant Continous COnvolution (ECCO) for improved trajectory
prediction. ECCO uses rotationally-equivariant continuous convolutions to embed
the symmetries of the system. On both vehicle and pedestrian trajectory
datasets, ECCO attains competitive accuracy with significantly fewer
parameters. It is also more sample efficient, generalizing automatically from
few data points in any orientation. Lastly, ECCO improves generalization with
equivariance, resulting in more physically consistent predictions. Our method
provides a fresh perspective towards increasing trust and transparency in deep
learning models.

Machine Learning;Artificial Intelligence;Robotics