Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization

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 regressing waypoints 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 general representation for temporally-continuous probabilistic trajectory prediction that regresses polynomial parameterization coefficients. 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.

Authors

• Zhaoen Su
• Chao Wang
• Henggang Cui
• Nemanja Djuric
• Carlos Vallespi-Gonzalez
• David Bradley

Keywords

• Supervised learning
• Predictive models
• Probabilistic logic
• Approximation error
• Trajectory
• Task analysis
• Intelligent robots
• Prediction Probability
• Estimation Error
• Pedestrian
• General Representation
• Trajectory Prediction
• Motion Prediction
• Future Motion
• Linear Interpolation
• Additional Constraints
• Bounding Box
• Autonomous Vehicles
• Polynomial Of Degree
• Polynomial Model
• Prediction Horizon
• Diversity Parameters
• Polynomial Coefficients
• Univariate Distribution
• Lateral Acceleration
• Laplace Distribution
• Low-order Polynomial
• In-house Dataset
• Minimum Acceleration
• Lateral Speed
• Smooth L1 Loss
• Displacement Error
• Intermediate Time Points
• Representative Trajectories
• Robot Trajectory
• Centroid