Pedestrian trajectory prediction is one of the key technologies to realize safe and reliable autonomous driving systems. Compared with the traditional bird's-eye view dataset, pedestrian trajectory prediction from the first view can provide direct visual information about pedestrians, which has higher application value. In this study, a multi-modal pedestrian trajectory prediction model with time-aware convolutional network (TACN) is proposed. By transforming the raw trajectory data into a multivariate input format, the model can more precisely capture both the pedestrians' location information and their dynamic changes. At the same time, the designed time-aware convolutional network can extract multi-time scale feature, and effectively deal with the immediancy and burstiness of pedestrian behavior. To deal with the uncertainty of future trajectories, we employ a conditional variational autoencoder (CVAE) to implement multimodal trajectory prediction and generate multiple possible future trajectories. Experimental results demonstrate the superiority of our approach.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Temporal-Aware Convolutional Network for First-Person View Pedestrian Trajectory Prediction


    Contributors:
    Liu, Yanran (author) / Meng, Qingyu (author) / Xie, Hongnan (author) / Guo, Hongyan (author)


    Publication date :

    2024-10-25


    Size :

    8415795 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network

    Song, Xiao / Chen, Kai / Li, Xu et al. | IEEE | 2021


    Social graph convolutional LSTM for pedestrian trajectory prediction

    Yutao Zhou / Huayi Wu / Hongquan Cheng et al. | DOAJ | 2021

    Free access

    Social graph convolutional LSTM for pedestrian trajectory prediction

    Zhou, Yutao / Wu, Huayi / Cheng, Hongquan et al. | Wiley | 2021

    Free access

    Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

    Liu, Yu / Zhang, Yuexin / Li, Kunming et al. | IEEE | 2023