Pedestrian crossing intention prediction (PCIP) is crucial for pedestrians' safety in autonomous driving. Existing methods do not use the interaction between pedestrians and cars for their prediction. In this paper, we argue that pedestrians' intentions are highly dependent on their interaction with the environment. Specifically, the trajectories of pedestrians and the dynamic of vehicles jointly affect the entire traffic environment in the future. Therefore, in this paper, we propose a novel pedestrian-vehicle information modulation network (PVIM). Particularly, we first propose a pedestrian-vehicle spatial context (PVSC) that effectively models the spatial dynamics between the pedestrian and ego-vehicle. Second, we design a temporal bilinear attention module that removes temporal redundancy and consolidates temporal correlation for more accurate predictions. We have conducted extensive experiments on the PIE pedestrian action prediction benchmark and have achieved state-of-the-art performance. Specifically, the proposed method achieves an accuracy of 0.91, outperforming the previous best by 2%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Pedestrian-Vehicle Information Modulation for Pedestrian Crossing Intention Prediction


    Contributors:
    Xu, Li (author) / You, Shaodi (author) / He, Gang (author) / Li, Yunsong (author)

    Published in:

    Publication date :

    2025-03-01


    Size :

    21085701 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    PEDESTRIAN CROSSING PREDICTION METHOD AND PEDESTRIAN CROSSING PREDICTION DEVICE

    FANG FANG / TSUCHIYA CHIKAO / TAKEI SHOICHI et al. | European Patent Office | 2023

    Free access

    PIT: Progressive Interaction Transformer for Pedestrian Crossing Intention Prediction

    Zhou, Yuchen / Tan, Guang / Zhong, Rui et al. | IEEE | 2023


    Pedestrian Crossing Intention Prediction at Red-Light Using Pose Estimation

    Zhang, Shile / Abdel-Aty, Mohamed / Wu, Yina et al. | IEEE | 2022


    PEDESTRIAN CROSSING INTENTION ESTIMATION METHOD AND APPARATUS, DEVICE, AND VEHICLE

    FAN SHIWEI / LI FEI / LI XIANGXU | European Patent Office | 2022

    Free access

    Pedestrian crossing vehicle alarm

    KIM JI HUN | European Patent Office | 2021

    Free access