Abstract—Future cars are anticipated to be driverless; point-to-point transportation services capable of avoiding fatalities. To achieve this goal, auto-manufacturers have been investing to realize the potential autonomous driving. In this regard, we present a self-driving model car capable of autonomous driving using object-detection as a primary means of steering, on a track made of colored cones. This paper goes through the process of fabricating a model vehicle, from its embedded hardware platform, to the end-to-end ML pipeline necessary for automated data acquisition and model-training, thereby allowing a Deep Learning model to derive input from the hardware platform to control the car’s movements. This guides the car autonomously and adapts well to real-time tracks without manual feature-extraction. This paper presents a Computer Vision model that learns from video data and involves Image Processing, Augmentation, Behavioral Cloning and a Convolutional Neural Network model. The Darknet architecture is used to detect objects through a video segment and convert it into a 3D navigable path. Finally, the paper touches upon the conclusion, results and scope of future improvement in the technique used. Keywords—autonomous, self-driving, computer vision, YOLO, object detection, embedded hardware


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    Titel :

    Design and Development of an Autonomous Car using Object Detection with YOLOv4


    Beteiligte:
    Rishabh Chopda (Autor:in) / Saket Pradhan (Autor:in) / Anuj Goenka (Autor:in)

    Erscheinungsdatum :

    2022-01-15


    Anmerkungen:

    oai:zenodo.org:5854560



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




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