Predicting the motion of dynamic agents is a critical task for guaranteeing the safety of autonomous systems. A particular challenge is that motion prediction algorithms should obey dynamics constraints and quantify prediction uncertainty as a measure of confidence. We present a physics-constrained approach for motion prediction which uses a surrogate dynamical model to ensure that predicted trajectories are dynamically feasible. We propose a two-step integration consisting of intent and trajectory prediction subject to dynamics constraints. We also construct prediction regions that quantify uncertainty and are tailored for autonomous driving by using conformal prediction, a popular statistical tool. Physics Constrained Motion Prediction achieves a 41% better ADE, 56% better FDE, and 19% better IoU over a baseline in experiments using an autonomous racing dataset.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Physics Constrained Motion Prediction with Uncertainty Quantification


    Contributors:


    Publication date :

    2023-06-04


    Size :

    7357040 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Uncertainty Quantification of Tiltrotor Download Prediction

    Khurana, Manas / Jayaraman, Buvana / Wissink, Andrew M. et al. | AIAA | 2023


    Uncertainty Quantification of Tiltrotor Download Prediction

    Khurana, Manas / Jayaraman, Buvana / Wissink, Andrew M. et al. | TIBKAT | 2023


    Fusing Physics and Machine Learning in Uncertainty Quantification

    Geoffrey Bomarito / P Leser / J Warner et al. | NTRS



    Uncertainty Quantification in Trajectory Prediction for Aircraft Operations

    Sankararaman, Shankar / Daigle, Matthew | AIAA | 2017