It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle. This allows the downstream planner to estimate the impact of its decisions. Recent approaches for conditional behavior prediction rely on a regression decoder, meaning that coordinates or polynomial coefficients are regressed. In this work we revisit set-based trajectory prediction, where the probability of each trajectory in a predefined trajectory set is determined by a classification model, and first-time employ it to the task of conditional behavior prediction. We propose RESET, which combines a new metric-driven algorithm for trajectory set generation with a graph-based encoder. For unconditional prediction, RESET achieves comparable performance to a regression-based approach. Due to the nature of set-based approaches, it has the advantageous property of being able to predict a flexible number of trajectories without influencing runtime or complexity. For conditional prediction, RESET achieves reasonable results with late fusion of the planned trajectory, which was not observed for regression-based approaches before. This means that RESET is computationally lightweight to combine with a planner that proposes multiple future plans of the autonomous vehicle, as large parts of the forward pass can be reused.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction




    Publication date :

    2023-06-04


    Size :

    10527649 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Conditional agent trajectory prediction

    MAHJOURIAN REZA / DOWNEY CARLTON MACDONALD / SAPP BENJAMIN et al. | European Patent Office | 2024

    Free access

    CONDITIONAL AGENT TRAJECTORY PREDICTION

    MAHJOURIAN REZA / DOWNEY CARLTON MACDONALD / SAPP BENJAMIN et al. | European Patent Office | 2022

    Free access

    EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Prediction

    Chen, Kehua / Chen, Xianda / Yu, Zihan et al. | IEEE | 2023


    Vehicle Trajectory Prediction Using Intention-based Conditional Variational Autoencoder

    Feng, Xidong / Cen, Zhepeng / Hu, Jianming et al. | IEEE | 2019


    Conditional Wasserstein Auto-Encoder for Interactive Vehicle Trajectory Prediction

    Fei, Cong / He, Xiangkun / Kawahara, Sadahiro et al. | IEEE | 2020