This paper proposes a Recurrent Neural Network (RNN) for cyclist path prediction to learn the effect of contextual cues on the behavior directly in an end- to-end approach, removing the need for any annotations. The proposed RNN incorporates three distinct contextual cues: one related to actions of the cyclist, one related to the location of the cyclist on the road, and one related to the interaction between the cyclist and the egovehicle. The RNN predicts a Gaussian distribution over the future position of the cyclist one second into the future with a higher accuracy, compared to a current state-of-the-art model that is based on dynamic mode annotations, where our model attains an average prediction error of 33 cm one second into the future.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Context-based cyclist path prediction using Recurrent Neural Networks


    Beteiligte:


    Erscheinungsdatum :

    01.06.2019


    Format / Umfang :

    626836 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    CONTEXT-BASED CYCLIST PATH PREDICTION USING RECURRENT NEURAL NETWORKS

    Pool, Ewoud A. I. / Kooij, Julian F. P. / Gavrila, Dariu M. | British Library Conference Proceedings | 2019


    Using Road Topology to Improve Cyclist Path Prediction

    Pool, Ewoud Alexander Ignacz / Kooij, Julian Francisco Pieter / Gavrila, Dariu M. | British Library Conference Proceedings | 2017


    Using road topology to improve cyclist path prediction

    Pool, Ewoud A. I. / Kooij, Julian F. P. / Gavrila, Dariu M. | IEEE | 2017


    Model based cyclist energy prediction

    Goussault, Romain / Chasse, Alexandre / Lippens, Frederic | IEEE | 2017


    Driver Response Time to Cyclist Path Intrusions

    Oliver, Michele / Toxopeus, Ryan / Kodsi, Sam et al. | SAE Technical Papers | 2018