Pedestrian is the main Vulnerable Road User (VRU) that needs further support when designing enhanced Intelligent Transportation Systems (ITSs). The pedestrian’s perception and motion identification in real time and in different situations are still challenging tasks for the autonomous vehicle designer. Serious efforts have been invested in this field using recent Artificial Intelligence (AI) techniques such as deep and reinforcement learning. However, pedestrian refuge island perception and decision-making have not been investigated so far. In this paper, we propose a model to detect pedestrians and count their numbers in refuge islands using the Pedestrian Location Perception Network (P-LPN) technique. Also, our model uses the Long Short-Term Memory (LSTM) for updating the number of pedestrians over time and predict the desired decision.


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    Titel :

    Deep Learning-Based Modeling of Pedestrian Perception and Decision-Making in Refuge Island for Autonomous Driving


    Weitere Titelangaben:

    EAI/Springer Innovations in Communication and Computing


    Beteiligte:
    Ouaissa, Mariya (Herausgeber:in) / Boulouard, Zakaria (Herausgeber:in) / Ouaissa, Mariyam (Herausgeber:in) / Guermah, Bassma (Herausgeber:in) / Elallid, Badr Ben (Autor:in) / Hamdani, Sara El (Autor:in) / Benamar, Nabil (Autor:in) / Mrani, Nabil (Autor:in)


    Erscheinungsdatum :

    21.02.2022


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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