Real Time Traffic Light Detection by Autonomous Vehicles using Artificial Neural Network Techniques
Mahesh .G1, Satish Kumar .T2
1Dr. Mahesh .G, Computer Science and Engineering, BMS Institute of Technology and Management, Bangalore, India..
2Dr. Satish Kumar .T, Computer Science and Engineering, BMS Institute of Technology and Management, Bangalore, India..
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2129-2133
| Volume-8 Issue-10, August 2019 | Retrieval Number: J93550881019/2019©BEIESP | DOI: 10.35940/ijitee.J9355.0881019
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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: Autonomous vehicles are the reality of the future, they will open up the way for future advanced systems where computers are expected to take over the decision making of driving. These automobiles are capable of sensing their environment and moving with little or no human input. The main goal of this research is to detect traffic light in real-time for autonomous vehicles. Apart from taking decisions to navigate in the right manner the autonomous vehicles important task is to detect traffic lights, so that it can obey the traffic rules with sufficient precision. The work carried out in this research makes use of two Artificial Intelligence technique, these techniques are compared in accomplishing the task of traffic light detection in real time. The two models that are designed and implemented are Convolution neural network (CNN) and Deep Convolution Inverse Graphics Network (DCIGN). The results clearly show that DCIGN out performance CNN by 8%.
Keywords: Convolution neural network, Deep Convolution Inverse Graphics Network, stochastic gradient descent algorithm, Autonomous vehicles, traffic light.
Scope of the Article: Network Modelling and Simulation