Autonomous Driving in a Multi-Lane Highway Environment
Krithika Balasubramanian1, Akhil Kothari2, Vijayakumar Kuppusamy3
1Krithika Balasubramanian*, Associate Professor, in Department of Information Security, at School of Computer Science and Engineering, India.
2Akhil Kothari, Associate Professor, in Department of Information Security, at School of Computer Science and Engineering, India.
3K. Vijayakumar, Associate Professor, in Department of Information Security, at School of Computer Science and Engineering, India.
Manuscript received on May 16, 2020. | Revised Manuscript received on May 21, 2020. | Manuscript published on June 10, 2020. | PP: 633-637 | Volume-9 Issue-8, June 2020. | Retrieval Number: H6550069820/2020©BEIESP | DOI: 10.35940/ijitee.H6186.069820
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Our goal through this paper is to figure out if it is possible to create an autonomous driving environment with a self-governing car with the help of a Q learning algorithm, a variant of Reinforcement Learning. To prepare and test-driving calculations, we convey a reproduced traffic framework simulation. We plan to split the environment around the agent vehicle into 16 states. The Q learning algorithms calculations, which are based on the Bellman’s Equations, will help quantify the quality of each state, helping the agent make the right decisions in the environment to avoid collisions. The World health organization reports highlight that in 2019 there have been over 5 million reported road accidents with approximately 1.5 million causalities and an increase of 167% in road accidents over the last 15 years. Through this paper, we want to push the envelope concerning creating a more secure driving environment and help avoid unfortunate accidents and loss of lives.
Keywords: Backhaul Autonomous driving, Q Learning, Multilane, Reinforcement learning
Scope of the Article: Machine/ Deep Learning with IoT & IoE