Autonomous vehicles face significant challenges in understanding pedestrian behavior, particularly in urban environments. In such settings, the system must recognize pedestrian intentions and anticipate their actions to achieve safe and intelligent driving. This paper focuses on predicting pedestrian crossings, enabling oncoming vehicles to react to pedestrians in a traffic scene in a timely manner. We investigate the effectiveness of various input features for pedestrian crossing prediction, including human poses, bounding boxes, and ego vehicle speed features. We propose a novel lightweight architecture based on LSTM and attention to accurately identify crossing pedestrians. Our methods are evaluated on two widely used public datasets for pedestrian behavior, PIE and JAAD datasets, and our algorithm achieves a state-of-the-art performance in both datasets by reaching a prediction accuracy of 91% and an F1-score of 84% on the PIE dataset and an accuracy of 67% and an F1-score of 77% on the JAAD Behavior (BEH) split. We make our code available at https://github.com/afnan29alofi/Pedestrian-Intent-Prediction.git


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Pedestrian Safety by Intent Prediction: A Lightweight LSTM-Attention Architecture and Experimental Evaluations with Real-World Datasets


    Contributors:


    Publication date :

    2024-06-02


    Size :

    2712753 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Pedestrian Intent Detection using Skeleton-based Prediction for Road Safety

    Samant, Advait P. / Warhade, Krishna / Gunale, Kishanprasad | IEEE | 2021


    PEDESTRIAN INTENT YIELDING

    MNEIMNEH MAHER / DORSEY ANNE HOBBS / YAN QIAOJING | European Patent Office | 2023

    Free access

    PEDESTRIAN INTENT YIELDING

    MNEIMNEH MAHER / DORSEY ANNE HOBBS / YAN QIAOJING | European Patent Office | 2023

    Free access

    Spatiotemporal relationship reasoning for pedestrian intent prediction

    ADELI-MOSABBEB EHSAN / LEE KUAN / GAIDON ADRIEN et al. | European Patent Office | 2021

    Free access

    SPATIOTEMPORAL RELATIONSHIP REASONING FOR PEDESTRIAN INTENT PREDICTION

    ADELI-MOSABBEB EHSAN / LEE KUAN / GAIDON ADRIEN et al. | European Patent Office | 2021

    Free access