Due to high rates of self-inflicted accidents, driving a car is a demanding activity that requires the driver to prepare both physically and mentally. In this sense, driving assistance systems play an important role in preventing distractions and road accidents. In this work, we detect and classify driver distractions using convolutional neural networks trained first with the “State Farm Distracted Driver Detection” and then with a custom database. Furthermore, with the use of the TensorFlow Lite library, the trained model is deployed on a mobile device for real-time use. The best trained model reaches an accuracy of 91% in training and 95% in validation, thus resulting in a highly discriminating model for each of the classes (distractions) to be classified. In other words, 10 different classes have been identified: one of them is “safe driving” and the other 9 correspond to those most responsible for accidents. With the system's implementation on the mobile device, the test accuracy is reduced to 90% however it correctly detects distractions regardless of the class to which they belong. The initial results demonstrated the possibility of adapting an ADAS in a mobile device to detect distractions and generate real-time audible voice alerts.


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    Title :

    Neural Networks for the Detection of Distractions in Drivers




    Publication date :

    2023-11-15


    Size :

    4047839 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English