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A commonly-used representation for motion prediction of actors is a sequenceof waypoints (comprising positions and orientations) for each actor at discretefuture time-points. While this approach is simple and flexible, it can exhibitunrealistic higher-order derivatives (such as acceleration) and approximationerrors at intermediate time steps. To address this issue we propose a simpleand general representation for temporally continuous probabilistic trajectoryprediction that is based on polynomial trajectory parameterization. We evaluatethe proposed representation on supervised trajectory prediction tasks using twolarge self-driving data sets. The results show realistic higher-orderderivatives and better accuracy at interpolated time-points, as well as thebenefits of the inferred noise distributions over the trajectories. Extensiveexperimental studies based on existing state-of-the-art models demonstrate theeffectiveness of the proposed approach relative to other representations inpredicting the future motions of vehicle, bicyclist, and pedestrian trafficactors.

Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization