The trajectory prediction is significant for the decision-making of autonomous driving vehicles. In this paper, we propose a model to predict the trajectories of target agents around an autonomous vehicle. The main idea of our method is considering the history trajectories of the target agent and the influence of surrounding agents on the target agent. To this end, we encode the target agent history trajectories as an attention mask and construct a social map to encode the interactive relationship between the target agent and its surrounding agents. Given a trajectory sequence, the LSTM networks are firstly utilized to extract the features for all agents, based on which the attention mask and social map are formed. Then, the attention mask and social map are fused to get the fusion feature map, which is processed by the social convolution to obtain a fusion feature representation. Finally, this fusion feature is taken as the input of a variable-length LSTM to predict the trajectory of the target agent. We note that the variable-length LSTM enables our model to handle the case that the number of agents in the sensing scope is highly dynamic in traffic scenes. To verify the effectiveness of our method, we widely compare with several methods on a public dataset, achieving a 20% error decrease. In addition, the model satisfies the real-time requirement with the 32 fps.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism


    Beteiligte:
    Yang, Tao (Autor:in) / Nan, Zhixiong (Autor:in) / Zhang, He (Autor:in) / Chen, Shitao (Autor:in) / Zheng, Nanning (Autor:in)


    Erscheinungsdatum :

    19.10.2020


    Format / Umfang :

    2184369 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    TRAFFIC AGENT TRAJECTORY PREDICTION USING SOCIAL CONVOLUTION AND ATTENTION MECHANISM

    Yang, Tao / Nan, Zhixiong / Zhang, He et al. | British Library Conference Proceedings | 2020


    Traffic flow prediction method of space-time convolution fusion probability sparse attention mechanism

    ZHANG HONG / CHEN LINBIAO / CHEN LINLONG et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    TRAJECTORY PREDICTION FOR AUTONOMOUS VEHICLES USING ATTENTION MECHANISM

    PRONOVOST ETHAN MILLER | Europäisches Patentamt | 2024

    Freier Zugriff

    TRAJECTORY PREDICTION FOR AUTONOMOUS VEHICLES USING ATTENTION MECHANISM

    PRONOVOST ETHAN | Europäisches Patentamt | 2024

    Freier Zugriff

    Traffic flow prediction method based on graph attention convolution network

    ZHENG HONG / ZHANG SIKAI / LIU JIAMOU et al. | Europäisches Patentamt | 2020

    Freier Zugriff