Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with various open research questions. State-of-the-art approaches regress trajectories either in a one-shot or step-wise manner. Although one-shot approaches are usually preferred for their simplicity, they relinquish powerful self-supervision schemes that can be constructed by chaining multiple time-steps. We address this issue by proposing a middle-ground where multiple trajectory segments are chained together. Our proposed Multi-Branch Self-Supervised Predictor receives additional training on new predictions starting at intermediate future segments. In addition, the model ’imagines’ the latent context and ’predicts the past’ while combining multi-modal trajectories in a tree-like manner. We deliberately keep aspects such as interaction and environment modeling simplistic and nevertheless achieve competitive results on the INTERACTION dataset. Furthermore, we investigate the sparsely explored uncertainty estimation of deterministic predictors. We find positive correlations between the prediction error and two proposed metrics, which might pave way for determining prediction confidence.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision


    Beteiligte:
    Janjos, Faris (Autor:in) / Keller, Max (Autor:in) / Dolgov, Maxim (Autor:in) / Zollner, J. Marius (Autor:in)


    Erscheinungsdatum :

    2023-06-04


    Format / Umfang :

    2127409 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Reciprocal Consistency Prediction Network for Multi-Step Human Trajectory Prediction

    Zhu, Wenjun / Liu, Yanghong / Zhang, Mengyi et al. | IEEE | 2023


    Bridging the Gap: Improving Domain Generalization in Trajectory Prediction

    Wang, Zhibo / Guo, Jiayu / Zhang, Haiqiang et al. | IEEE | 2024


    Vision Based Vehicle Trajectory Supervision

    Chausse, F. / Aufrere, R. / Chapuis, R. et al. | British Library Conference Proceedings | 2000


    Vision based vehicle trajectory supervision

    Chausse, F. / Aufrere, R. / Chapuis, R. | IEEE | 2000


    Multi-agent trajectory prediction

    NARAYANAN SRIRAM NOCHUR / LIU BUYU / MOSLEMI RAMIN et al. | Europäisches Patentamt | 2023

    Freier Zugriff