Intelligent Transportation using Deep Learning
Vijay Bhanudas Gujar
Mr. Vijay Bhanudasd Gujar, CSE, Dnyanshree Institute of Engineering and Technology, Satara, Maharashtra, India.
Manuscript received on December 14, 2019. | Revised Manuscript received on December 23, 2019. | Manuscript published on January 10, 2020. | PP: 1619-1625 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8455019320/2020©BEIESP | DOI: 10.35940/ijitee.C8455.019320
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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: The goal of this paper is to advance intelligent transportation program through the creation of a data collection system, a Convolutional Neural Network (CNN) model for intelligent transportation, and a simulator to test the trained CNN model. The data collection system collects data from a vehicle steering wheel angle, speed, and images of the road from three separate angles at the time of the data collection. A CNN model is then trained with the collected data. The trained CNN model is then tested on a simulator to evaluate its effectiveness.
Keywords: Convolutional Neural Network (CNN), Data Collection System, Deep Learning, Neural Network, Simulation
Scope of the Article: Deep Learning