CNN Based Self-driven Autonomous Car

CNN Based Self-driven Autonomous Car
Author(s):
GAGAN KUMAR G, DILEEP V, Sahana GP, Sunil Kumar GR
Keywords:
CNN, SENSOR, MASTER- SLAVE, TRAFFIC SIGNS.
Abstract
According to the Society of Automotive Engineers there are six international standards set to know the level of driving automation. Few start-up companies are experimenting on the autonomous vehicles, these vehicles fail to reach the roads due to many reasons like improper processing of the input data from the sensors, Environmental causes, highly expensive hardware etc. In this paper we are proposing a better solution to process the Input data using High Dynamic Ranging technique, in which multiple input data is processed at least bit rate and the captured image data is acquired completely with less distortion. By using Convolution Neural Network, obstacles and traffic signs can be detected perfectly when compared to the conventional methods. In convolution neural networking every nodes of the system are interconnected to each other in the system by which we obtain a much responsive output compared to the present techniques for data collection. The processing unit (Master device) and the controller unit (Slave device) are set up on a single board, which makes the system function much faster than the other present systems.
Article Details
Unique Paper ID: 155409 Publication Volume & Issue: Volume 9, Issue 1 Page(s): 741 - 744
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