This project constitutes an educational initiative focused on the application of machine learning and programming within the classroom setting. Within this context, we faced the challenge of designing a system that simulates traffic congestion and responds to the gestures of a traffic guard through an AI-driven, micro:bit-based integrated system. The proposed system aims to alleviate real-world traffic congestion responding to the gestures of a traffic guard. The synchronization of traffic lights is orchestrated through Machine Learning (ML) algorithms. This solution targets easing congestion, particularly focusing on school areas representing a substantial leap forward in strategies for managing vehicle movements at critical junctions near educational institutions.


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    Titel :

    Revolutionizing Traffic Management: AI-Driven Micro:bit Integration for Real-Time Traffic Control


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Balogh, Richard (Herausgeber:in) / Obdržálek, David (Herausgeber:in) / Fislake, Martin (Herausgeber:in) / Molas, Lluís (Autor:in) / Cardenas, Martha-Ivon (Autor:in)

    Kongress:

    International Conference on Robotics in Education (RiE) ; 2024 ; Koblenz, Germany April 10, 2024 - April 12, 2024


    Erschienen in:

    Robotics in Education ; Kapitel : 33 ; 379-390


    Erscheinungsdatum :

    27.09.2024


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch