In this paper, we propose an efficient vehicle trajectory prediction framework based on recurrent neural network. Basically, the characteristic of the vehicle's trajectory is different from that of regular moving objects since it is affected by various latent factors including road structure, traffic rules, and driver's intention. Previous state of the art approaches use sophisticated vehicle behavior model describing these factors and derive the complex trajectory prediction algorithm, which requires a system designer to conduct intensive model optimization for practical use. Our approach is data-driven and simple to use in that it learns complex behavior of the vehicles from the massive amount of trajectory data through deep neural network model. The proposed trajectory prediction method employs the recurrent neural network called long short-term memory (LSTM) to analyze the temporal behavior and predict the future coordinate of the surrounding vehicles. The proposed scheme feeds the sequence of vehicles' coordinates obtained from sensor measurements to the LSTM and produces the probabilistic information on the future location of the vehicles over occupancy grid map. The experiments conducted using the data collected from highway driving show that the proposed method can produce reasonably good estimate of future trajectory.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network


    Beteiligte:
    Kim, ByeoungDo (Autor:in) / Kang, Chang Mook (Autor:in) / Kim, Jaekyum (Autor:in) / Lee, Seung Hi (Autor:in) / Chung, Chung Choo (Autor:in) / Choi, Jun Won (Autor:in)


    Erscheinungsdatum :

    01.10.2017


    Format / Umfang :

    916167 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Relational Recurrent Neural Networks For Vehicle Trajectory Prediction

    Messaoud, Kaouther / Yahiaoui, Itheri / Verroust-Blondet, Anne et al. | IEEE | 2019


    Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Driving

    Mo, Xiaoyu / Xing, Yang / Lv, Chen | ArXiv | 2021

    Freier Zugriff


    Vehicle Trajectory Prediction based on LSTM Recurrent Neural Networks

    Ip, Andre / Irio, Luis / Oliveira, Rodolfo | IEEE | 2021


    VEHICLE TRAJECTORY PREDICTION SYSTEM AND METHOD BASED ON MODULAR RECURRENT NEURAL NETWORK ARCHITECTURE

    JUN WON CHOI / PARK SEONG HYEON / BYEONGDO KIM | Europäisches Patentamt | 2019

    Freier Zugriff