The proliferation of autonomous vehicles (AVs) research and technologies have the potential of advancing intelligent transportation systems to new horizons. One of the main issues that relate to the AV operation is the need to enable these vehicles to comprehend the behavior of road users the same way as human drives would normally do. For this purpose, significant research efforts have focused on the issue of enabling the AVs to predict the intention of pedestrians especially as relates to road-crossing. This is done by interpreting some attributes of the pedestrians' behaviors as they approach the areas where they can potentially attempt to cross the street. In this study, we introduce a novel architecture of a pedestrian intention prediction model. This architecture includes the processing of the collected data in two parallel paths. In these two paths, image and non-image data are processed via two types of transformer-based models and then fused into an intention prediction classifier. Experimental simulations show that the proposed technique produces significantly better pedestrians' intention prediction results, as compared to leading models from the literature.


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    Titel :

    An Optimized Hybrid Transformer-Based Technique for Real-Time Pedestrian Intention Estimation in Autonomous Vehicles


    Beteiligte:


    Erscheinungsdatum :

    16.10.2023


    Format / Umfang :

    1359531 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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