Co-existence between autonomous vehicles (AVs) and human-driven vehicles expected in the next few decades poses a problem for AVs to infer human drivers’ intents nearby and cope with them safely and efficiently. To address this issue, we develop a light-weight deep learning model for a connected autonomous vehicle (CAV) to infer intents in a safety-critical case of lane changes made by human-driven vehicles. Through experiments with the real trajectory dataset NGSIM, we show that a simple Multi-Layer Perceptron (MLP) model can predict lane change events with high accuracy comparable with more sophisticated models. The model is intentionally designed to work with the simplest 3-vehicle topology to foster real-time execution on the resource-constrained computing platforms on AVs. Still, the model achieves 85% accuracy over 5 to 8 seconds prediction horizons so that AVs can have enough time to prepare for an upcoming lane change event.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Inferring Human Driver Intent in Partial Deployment of Connected Autonomous Vehicles: the Lane Change Case


    Beteiligte:
    Na, Jonghwan (Autor:in) / Lee, Hojeong (Autor:in) / Kim, Hyogon (Autor:in)


    Erscheinungsdatum :

    2023-06-01


    Format / Umfang :

    1257865 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Predicting driver lane change intent using HCRF

    Wen, Yu / Zhang, Xuetao / Wang, Fei et al. | IEEE | 2015


    Lane change scheduling for connected and autonomous vehicles

    Atagoziev, Maksat / Güran Schmidt, Ece / Schmidt, Klaus Werner | Elsevier | 2022


    Lane Change Driver Assistance System for Online Operation Optimization of Connected Vehicles

    Aubeck, Franz / Oetermann, Tobias / Birmes, Georg et al. | IEEE | 2019



    PROACTIVE LANE CHANGE FOR AUTONOMOUS VEHICLES

    WANG LEI / WU YUJIA / ZHAO YUFEI et al. | Europäisches Patentamt | 2022

    Freier Zugriff