  A commonly-used representation for motion prediction of actors is a sequence
of waypoints (comprising positions and orientations) for each actor at discrete
future time-points. While this approach is simple and flexible, it can exhibit
unrealistic higher-order derivatives (such as acceleration) and approximation
errors at intermediate time steps. To address this issue we propose a simple
and general representation for temporally continuous probabilistic trajectory
prediction that is based on polynomial trajectory parameterization. We evaluate
the proposed representation on supervised trajectory prediction tasks using two
large self-driving data sets. The results show realistic higher-order
derivatives and better accuracy at interpolated time-points, as well as the
benefits of the inferred noise distributions over the trajectories. Extensive
experimental studies based on existing state-of-the-art models demonstrate the
effectiveness of the proposed approach relative to other representations in
predicting the future motions of vehicle, bicyclist, and pedestrian traffic
actors.
