Pedestrian behavior prediction is essential to enable safe and efficient driving of intelligent vehicles on urban traffic environment. This article presents a novel framework for pedestrian trajectory prediction, which integrates Dynamic Bayesian network and Sequence-to-Sequence model through an adaptive online weighting method. Dynamic Bayesian network utilizes environmental features and kinematic information to infer the pedestrian's motion intentions through probabilistic reasoning. Sequence-to-Sequence model views trajectory predictions as sequence generation tasks, in which the future trajectories are generated relying on the observed trajectories. A real-world pedestrian motion dataset is employed for model validations and it is also enlarged through data augmentation techniques to enable training of data-driven approaches. We compare our model with several typical baselines methods and results show that our model outperforms those baselines. The average error and the final destination error with one-second prediction are 0.04m and 0.10m in crossing scenarios, and 0.06m and 0.17m in stopping scenarios, respectively. The study expects to provide guidelines for the decision-making of intelligent vehicles in order to protect vulnerable road users.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Pedestrian Trajectory Prediction Combining Probabilistic Reasoning and Sequence Learning


    Beteiligte:
    Li, Yang (Autor:in) / Lu, Xiao-Yun (Autor:in) / Wang, Jianqiang (Autor:in) / Li, Keqiang (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.09.2020


    Format / Umfang :

    5089979 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Pedestrian trajectory prediction method and device

    ZHENG WEN / LIU CHUANG / XU GUANGYU et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    PEDESTRIAN TRAJECTORY PREDICTION WITH LEARNING-BASED APPROACHES: A COMPARATIVE STUDY

    Li, Yang / Xin, Long / Yu, Dameng et al. | British Library Conference Proceedings | 2019