Physics-related and model-based vessel trajectory prediction is highly accurate but requires specific knowledge of the vessel under consideration which is not always practical. Machine learning-based trajectory prediction models do not require expert knowledge, but rely on the implicit knowledge extracted from massive amounts of data. Several deep learning (DL) methods for vessel trajectory prediction have recently been suggested. The DL models developed typically only process information about the (dis)location of vessels defined with respect to a global reference system. In the context of inland navigation, this can be problematic, since without knowledge of the limited navigable space, irrealistic trajectories are likely to be determined. If spatial constraintes are introduced, e.g., by implementing an additional submodule to process map data, however, overall complexity increases. Instead of processing the vessel displacement information on the one hand and the spatial information on the other hand, the paper proposes the merging of both information. Here, fairway-related and navigation-related displacement information are used directly. In this way, the previously proposed context-sensitive Classification Transformer (CSCT) shows an improved spatial awareness. Additionally, the CSCT is adapted to assess the model uncertainty by enabling dropout during inference. This approach is trained on different inland waterways to analyze its generalizability. As the improved CSCT obtains lower prediction errors and enables to estimate the trustworthiness of each prediction, it is more suitable for safety-critical applications in inland navigation than previously developed models.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Improved Context-Sensitive Transformer Model for Inland Vessel Trajectory Prediction


    Contributors:


    Publication date :

    2023-09-24


    Size :

    3197173 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Vessel trajectory prediction in curving channel of inland river

    Xiaopeng, Tong / Xu, Chen / Lingzhi, Sang et al. | IEEE | 2015


    Short-term Inland Vessel Trajectory Prediction with Encoder-Decoder Models

    Donandt, Kathrin / Böttger, Karim / Söffker, Dirk | HENRY – Federal Waterways Engineering and Research Institute (BAW) | 2022

    Free access

    Short-term Inland Vessel Trajectory Prediction with Encoder-Decoder Models

    Donandt, Kathrin / Bottger, Karim / Soffker, Dirk | IEEE | 2022


    AGENT TRAJECTORY PREDICTION USING CONTEXT-SENSITIVE FUSION

    VARADARAJAN BALAKRISHNAN / HEFNY AHMED SAID MOHAMMED / SAPP BENJAMIN et al. | European Patent Office | 2022

    Free access