This paper proposes a framework to recognize driving intentions and to predict driving behaviors of lane changing on the highway by using externally sensable traffic data from the host-vehicle. The framework consists of a driving characteristic estimator and a driving behavior predictor. A driver's implicit driving characteristic information is uniquely determined and detected by proposed the online-estimator. Neural-network based behavior predictor is developed and validated by testing with the real naturalistic traffic data from Next Generation Simulation (NGSIM), which demonstrates the effectiveness in identifying the driving characteristics and transforming into accurate behavior prediction in real-world traffic situations.


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    Titel :

    Driving Intention Recognition and Lane Change Prediction on the Highway


    Beteiligte:
    Han, Teawon (Autor:in) / Jing, Junbo (Autor:in) / Ozguner, Umit (Autor:in)


    Erscheinungsdatum :

    01.06.2019


    Format / Umfang :

    1061696 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Driving Intention Recognition and Lane Change Prediction on the Highway

    Han, Teawon / Jing, Junbo / Ozguner, Umit | ArXiv | 2019

    Freier Zugriff

    DRIVING INTENTION RECOGNITION AND LANE CHANGE PREDICTION ON THE HIGHWAY

    Han, Teawon / Jing, Junbo / Özgüner, Ümit | British Library Conference Proceedings | 2019



    Lane Change Intention Recognition and Vehicle Status Prediction for Autonomous Vehicles

    Yuan, Renteng / Abdel-Aty, Mohamed / Gu, Xin et al. | ArXiv | 2023

    Freier Zugriff