Autonomous Driving using Deep Reinforcement Learning in Urban Environment IJTSRD


Deep Reinforcement Learning has led us to newer possibilities in solving complex control and navigation related tasks. The paper presents Deep Reinforcement Learning autonomous navigation and obstacle avoidance of self driving cars, applied with Deep Q Network to a simulated car an urban environment. The car, using a variety of sensors will be easily able to detect pedestrians, objects will allow the car to slow or stop suddenly. As a computer is far more precise and subject to fewer errors than a human, accident rates may reduce when these vehicles become available to consumers. This autonomous technology would lead to fewer traffic jams and safe road.

BY Hashim Shakil Ansari | Goutam R “Autonomous Driving using Deep Reinforcement Learning in Urban Environment”

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019,

URL: https://www.ijtsrd.com/papers/ijtsrd23442.pdf

Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23442/autonomous-driving-using-deep-reinforcement-learning-in-urban-environment/hashim-shakil-ansari

call for paper Environmental Engineering, international journal Food Engineering, ugc approved journals for engineering




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