The interest in autonomous vehicles has recently increased and as a consequence many companies and researchers have begun working on their own solutions to many of the issues that ensue when a car has to handle complicated decisions on its own. This project looks into the possibility of relegating as many decisions as possible to only one sensor and engine control unit (ECU) — in this work, by letting a Raspberry Pi with a camera attached control a vehicle following a road. To solve this problem, image processing, or more specifically, machine learning’s convolutional neural networks (CNN) are utilized to steer a car by monitoring the path with a single camera. The proposed CNN is designed and implemented using a machine learning library for Python known as Keras. The design of the network is based on the famous Lenet, but has been downscaled to increase computation speed and to reduce memory size while still maintaining a sufficient accuracy. The network was run on the ECU, which in turn was fastened to a RC car together with the camera. For control purposes wires were soldered to the remote controller and connected to the Raspberry Pi. As concerns steering, a simple bang-bang controller was implemented. Glass box testing was used to assess the effectiveness of the code, and to guarantee a continuous evaluation of the results. To satisfy the network’s requirements in terms of both accuracy and computation speed larger experiments were performed. The final experiments showed that the network achieved sufficient accuracy and performance to steer the prototype car in real time tasks, such as following model roads and stopping at the end of the path, as planned. This shows that despite being small with moderate accuracy, this CNN can handle the task of lane-keeping using only the data of one single camera. Since the CNN could do this while running on a small computer such as the Raspberry Pi, it has been observed that using a CNN for a lane-keeping algorithm in an embedded system looks promising. ; På senare tid så ...


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    Titel :

    Embedded Implementation of Lane Keeping Functionality Using CNN


    Beteiligte:
    Bark, Filip (Autor:in)

    Erscheinungsdatum :

    2018-01-01


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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