Simulation of Autonomous Car using Deep Learning
Rishabh Kumar, Raju Patel, Raghvendra Kumar, Shivam Rathour, Nidhi Gupta
Autonomous car, CNN, Multilayer
In this world of rapid advancement of computer technologies, like CNN, open-cv etc., Deep learning has grown tremendously in the field of artificial intelligence and can be used to automate almost anything, that includes modern technologies. These technologies can be applied to the car so that it requires minimum interaction with driver to run on the road or we can say that to program a car in such way that it drives in self-governing mode. With help of existing simulator, we can use it to generate the enormous amount of data that includes images and csv containing car details. We train the network with generated images (left, right, center) to predict the required steer angle to keep the car on track. This approach decreases down the resolution of images to train the network very rapidly. Before sending the data to network it is preprocessed which is very much beneficial. After the preprocessing data is send to convolutional neural network in form of fixed size batches formed by random collection of images with their corresponding steering angle with in the dataset generated to train and predict the steering angle as a final result. The Model achieved better performance when it is provided even more dataset. Here, we observe many Convolutional neural network architectures to obtain better performance with lesser load.
Article Details
Unique Paper ID: 151837

Publication Volume & Issue: Volume 8, Issue 1

Page(s): 958 - 963
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