Virtual Self Driving Car using the Techniques of Image Processing and Deep Neural Networks
R. Poonkuzhali1, Vineet Kumar Singh2
1R. Poonkuzhali*, School of Electroncis Engineering, VIT university, Vellore, Tamilnadu, India.
2Vineet Kumar Singh, B.Tech School of Electroncis Engineering, VIT university, Vellore, Tamilnadu, India.
Manuscript received on April 11, 2020. | Revised Manuscript received on May 15, 2020. | Manuscript published on June 30, 2020. | PP: 190-195 | Volume-9 Issue-5, June 2020. | Retrieval Number: D9070049420/2020©BEIESP | DOI: 10.35940/ijeat.D9070.069520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Self-driving cars come with both confronts and openings. Many tech gigantic companies like Google, Tesla, Apple and many more are funding billions of dollars for the implementation of a driverless car. In this modern era of automation, every human need has been driven towards things to be automated. From automated traffic control to automated home, everything comes up to the rescue of human to provide a comfortable and relaxing lifestyle. After almost automating everything now mankind has moved to automate the transportation, starting with automating the vehicles. With this the first step taken is to devise a self-driving car or a driverless car, with an aim to provide human with relaxed driving. Ever since the idea immersed, every year Google redefines the model to meet the need. In this paper, an open source simulator by Udacity, known as Self driving Car Engineer has been used for collecting the dataset and executing the neural net implemented using Python in association with packages like Keras, OpenCV etc.
Keywords: Neural Networks, Feature Extraction, Image Augmentation, Behavioral Cloning