Ship trajectory prediction is an important research topic in ship navigation automation. It can effectively help ship pilots to obtain comprehensive Marine traffic information and reduce the potential collision risk of ships. Compared with pedestrian and vehicle trajectory prediction, due to the existence of AIS system for ships, it is easier to obtain historical data of ships, so it is easier to explore the periodicity of trajectories in time. Therefore, based on the previous research on trajectory prediction, we propose a new history module, which adds the agent's long-term route intention to the model to guide the model to predict the ship trajectory. At the same time, we adopt a data-driven idea and pre-train a State Refinement module to obtain high-dimensional feature representations of velocity. Experiments show that our model is superior to the existing current algorithms in long-term trajectory prediction on self-made data sets.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Ship Trajectory Prediction with Social History LSTM


    Beteiligte:
    Zhao, Wenfeng (Autor:in) / Zhang, Xudong (Autor:in)


    Erscheinungsdatum :

    2023-03-24


    Format / Umfang :

    965142 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    RF and LSTM combined ship trajectory prediction model

    ZHANG CONG / ZHU JISHUAI / DENG MEIHUAN et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Social graph convolutional LSTM for pedestrian trajectory prediction

    Yutao Zhou / Huayi Wu / Hongquan Cheng et al. | DOAJ | 2021

    Freier Zugriff

    Social graph convolutional LSTM for pedestrian trajectory prediction

    Zhou, Yutao / Wu, Huayi / Cheng, Hongquan et al. | Wiley | 2021

    Freier Zugriff

    Aircraft Trajectory Prediction Using Social LSTM Neural Network

    Xu, Zhengfeng / Zeng, Weili / Chen, Lijing et al. | TIBKAT | 2021


    CNN, improved LSTM and attention mechanism-based ship trajectory prediction method and system

    LI XIULAI / LIU BOYI / CHEN MINGRUI et al. | Europäisches Patentamt | 2024

    Freier Zugriff