# Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization

arXiv: https://arxiv.org/abs/2011.00399 · HTML: https://mlsanity.com/paper/2011.00399/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Improving Out-of-Distribution Generalization of Trajectory Prediction for Autonomous Driving via Polynomial Representations](https://mlsanity.com/paper/2407.13431/) (2024)
- [An Empirical Bayes Analysis of Object Trajectory Representation Models](https://mlsanity.com/paper/2211.01696/) (2022)
- [Convolutions for Spatial Interaction Modeling](https://mlsanity.com/paper/2104.07182/) (2021)

## Abstract (arXiv)

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.