Predicting pedestrian crossing intention to ensure pedestrian safety has garnered significant attention in autonomous driving. Balancing the accuracy and real-time performance of prediction is a challenging endeavor. This article introduces faster pedestrian crossing intention prediction network (Faster-PCPNet), a rapid pedestrian crossing intention prediction network that jointly predicts intention based on pedestrian pose, ego-vehicle speed, pedestrian bounding box, and the novel triple quasi-polar coordinate. Faster-PCPNet incorporates the temporal-channel sharing enhanced topology graph convolution (TCGC) module designed in this article, allowing for in-depth exploration of significant pedestrian crossing action features. This article innovatively establishes a quasi-polar coordinate system, addressing the challenge of efficiently representing pedestrian crossing intention during relative motion between pedestrians and vehicles. Faster-PCPNet achieves accuracy rates of 89% on the JAAD dataset and 94% on the PIE public dataset. The experimental results highlight the real-time performance and reliability of the proposed model on mobile devices in traffic scenarios, effectively mitigating traffic accidents caused by pedestrian crossing. Our code and model are available at https://github.com/zjrcczu/Faster-PCPNet.


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    Title :

    Faster Pedestrian Crossing Intention Prediction Based on Efficient Fusion of Diverse Intention Influencing Factors


    Contributors:
    Yang, Biao (author) / Zhu, Junrui (author) / Hu, Chuan (author) / Yu, Zhitao (author) / Hu, Hongyu (author) / Ni, Rongrong (author)


    Publication date :

    2024-12-01


    Size :

    18353725 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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