Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario. However, distinct driving environments usually contain various possible driving maneuvers. Therefore, a intention prediction method that can adapt to different traffic scenarios is needed. To further improve the overall vehicle prediction performance, motion information is usually incorporated with classified intentions. As suggested in some literature, the methods that directly predict possible goal locations can achieve better performance for long-term motion prediction than other approaches due to their automatic incorporation of environment constraints. Moreover, by obtaining the temporal information of the predicted destinations, the optimal trajectories for predicted vehicles as well as the desirable path for ego autonomous vehicle could be easily generated. In this paper, we propose a Semantic based Intention and Motion Prediction (SIMP) method, which can be adapted to any driving scenarios by using semantic defined vehicle behaviors. It utilizes a probabilistic framework based on deep neural network to estimate the intentions, final locations, and the corresponding time information for surrounding vehicles. An exemplar real-world scenario was used to implement and examine the proposed method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Probabilistic Prediction of Vehicle Semantic Intention and Motion


    Beteiligte:
    Hu, Yeping (Autor:in) / Zhan, Wei (Autor:in) / Tomizuka, Masayoshi (Autor:in)


    Erscheinungsdatum :

    01.06.2018


    Format / Umfang :

    2230715 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Probabilistic Prediction of Vehicle Semantic Intention and Motion

    Hu, Yeping / Zhan, Wei / Tomizuka, Masayoshi | ArXiv | 2018

    Freier Zugriff

    PROBABILISTIC PREDICTION OF VEHICLE SEMANTIC INTENTION AND MOTION

    Hu, Yeping / Zhan, Wei / Tomizuka, Masayoshi | British Library Conference Proceedings | 2018



    Vehicle afflux order optimization method based on adjacent vehicle probabilistic motion prediction

    LIU JINQIANG / ZHAO WANZHONG / WANG CHUNYAN et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Host vehicle operation using remote vehicle intention prediction

    ZHAO YUE / MORTAZAVI ALI | Europäisches Patentamt | 2018

    Freier Zugriff