For safe and comfortable automated driving in the urban domain, especially in complex geometries as intersections, the prediction of surrounding traffic participants is fundamental. Several works in this field focus on predicting the behavior of vulnerable road users (VRU) at crossings. However, no approaches were found dealing with predicting the interaction between turning vehicles giving right of way or cooperating with VRU, which is substantial for the trajectory planning of following vehicles. Infrastructural sensor data from an intersection in Germany enables the development of a prediction concept for vehicles interacting with VRU. Our studies show that the original criteria for classifying an interaction between vehicles and VRU—the post-encroachment time (PET)—is not suitable as ground truth criteria for the aimed prediction. Instead, a clustering-based labelling approach with k-means shows promising results in trajectory pattern distinction. For the ensuing pattern classification, a framework is established for comparing three different supervised learning algorithms (Logistic Regression, Neural Networks, and Support Vector Machines [SVM]) with varying features and prediction horizons with best results of the logistic regression validated at a fivefold cross-validation. The classification builds the basis for a succeeding promising trajectory prediction.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Clustering-Based Trajectory Prediction of Vehicles Interacting with Vulnerable Road Users


    Weitere Titelangaben:

    Sae Intl. J Cav


    Beteiligte:
    Sonka, Adrian (Autor:in) / Henze, Roman (Autor:in) / Thal, Silvia (Autor:in)


    Erscheinungsdatum :

    2021-08-19


    Format / Umfang :

    11 pages




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch





    Pose Based Trajectory Forecast of Vulnerable Road Users

    Kress, Viktor / Zernetsch, Stefan / Doll, Konrad et al. | IEEE | 2019


    Reducing Conflict Between Vulnerable Road Users and Automated Vehicles

    Owens, Justin M. / Greene-Roesel, Ryan / Habibovic, Azra et al. | Springer Verlag | 2017


    Automated Vehicles & Vulnerable Road Users: Representing the Under-Represented

    Owens, Justin M. / Clamann, Michael / Aylor, David et al. | Springer Verlag | 2020


    Vulnerable Road Users: Cyclist

    Slop, M. / Vag-och transport-forskningsinstitutet | British Library Conference Proceedings | 1992