Abstract
Deep Learning algorithms can facilitate and strengthen sophisticated ADAS/Autonomous vehicle technologies to a large extent. Vision based perception functions powered by modern sensor networks in combination with Convolutional Neural Network (CNN) models can provide cutting-edge results in computer vision problems. The paper encompasses different types of deep learning architectures, some popular vehicular datasets and the most viable deep learning libraries for implementing the autonomous driving algorithms. In this paper, convolutional model algorithm is designed using Keras to train the dataset and is evaluated which provided considerable accuracy rate to implement them in future object detection algorithms. CNNs can provide state of the art solutions for the problems related to perception related tasks such as classifying objects, traffic environment understanding and taking appropriate actions.
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