IJBTT

Deep Reinforcement Learning for Computerized Steering Angle Control of Pollution-Free Autonomous Vehicle

	International Journal of Engineering Trends and Technology (IJETT)
	© 2021 by IJETT Journal
	Volume-69 Issue-4
	Year of Publication : 2021
	Authors : M.Karthikeyan, S.Sathiamoorthy
	DOI :  10.14445/22315381/IJETT-V69I4P228

Citation 

MLA Style: M.Karthikeyan, S.Sathiamoorthy  "Deep Reinforcement Learning for Computerized Steering Angle Control of Pollution-Free Autonomous Vehicle" International Journal of Engineering Trends and Technology 69.4(2021):204-208. 

APA Style:M.Karthikeyan, S.Sathiamoorthy. Deep Reinforcement Learning for Computerized Steering Angle Control of Pollution-Free Autonomous Vehicle  International Journal of Engineering Trends and Technology, 69(4),204-208.

Abstract
Manpower cost is the major expense in Industrial and domestic applications, and hence the whole world is moving towards automation with the help of Artificial Intelligence (AI). AI techniques have a major role in making the process automated and advanced in modern industrial requirements. Smart devices, Smart vehicles, Smart home, Smart Factory, Smart home appliances, etc., are working with automated process based on the principle of artificial intelligence, and hence in this paper, one of the advanced AI techniques is chosen for the automated vehicle (AV) where steering angle is controlled in order to keep the vehicle within the lane. In this paper, an adaptive deep reinforcement learning algorithm for autonomous vehicles is presented, and the results have been analyzed. In this paper deep Q learning algorithm is used to control the steering angle of an autonomous vehicle. A transition model estimator is also developed to emulate the learning process using neural networks. This model helped this research work to utilize the available test data efficiently. This paper mainly focused on the objectives (i) Optimal learning policy as an adaptive learning system, (ii) Markov decision process (MDP) as a learning process in the learning system, and (iii) Numerical simulation of Deep Reinforcement algorithm with autonomous vehicle model. Continuation of this work would be the final stage of autonomous vehicle development.

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Keywords
Autonomous vehicle, Deep Reinforcement Algorithm, Steering Angle Control, Transition Model Estimator, and Adaptive learning.