Avoiding collisions with vulnerable road users (VRUs) using sensor-based early recognition of critical situations is one of the manifold opportunities provided by the current development in the field of intelligent vehicles. As, especially, pedestrians and cyclists are very agile and have a variety of movement options, modeling their behavior in traffic scenes becomes a challenging task. In this paper, we propose movement models based on machine learning methods, in particular, artificial neural networks, in order to classify the current motion state and to predict the future trajectory of the VRUs. Both model types are also combined to enable the application of specifically trained motion predictors based on a continuously updated pseudo probabilistic state classification. Furthermore, the architecture is used to evaluate motion-specific physical models for starting and stopping and video-based pedestrian motion classification. A comprehensive dataset consisting of a total of 1068 pedestrian and 494 cyclist scenes acquired at an urban intersection is used for optimization, training, and evaluation of the different models. The results show substantially higher classification rates and the ability, through the machine learning approaches, to earlier recognize motion state changes than by the way of interacting multiple model (IMM) Kalman filtering. The trajectory prediction quality has also been improved for all kinds of test scenes, especially when starting and stopping motions are included. Here, 37% and 41% fewer position errors were achieved on average, respectively.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Intentions of Vulnerable Road Users—Detection and Forecasting by Means of Machine Learning


    Beteiligte:


    Erscheinungsdatum :

    2020-07-01


    Format / Umfang :

    3359042 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Vulnerable Road Users: Cyclist

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


    Vulnerable Road Users Detection Using V2X Communications

    Anaya, Jose J. / Talavera, Edgar / Gimenez, David et al. | IEEE | 2015


    VULNERABLE, YET SUSTAINABLE ``MOBILITY FOR VULNERABLE ROAD USERS WORLDWIDE''

    Beroud, B. / Van Den Noort, P. / World Road Association | British Library Conference Proceedings | 2007


    Advanced protection for vulnerable road users

    Moxey,E. / Johnson,N. / McCarthy,M.G. et al. | Kraftfahrwesen | 2005


    TECHNIQUES FOR DETECTING VULNERABLE ROAD USERS

    BALASUBRAMANIAN ANANTHARAMAN / VASSILOVSKI DAN / MARSH GENE WESLEY et al. | Europäisches Patentamt | 2023

    Freier Zugriff