DEEP LEARNING APPROACHES FOR AUTONOMOUS VEHICLE NAVIGATION IN URBAN ENVIRONMENTS
Keywords:
Deep Learning, Reinforcement Learning, Navigation-related ProblemsAbstract
The application of Deep Reinforcement Learning has opened up new avenues for us to explore when it comes to resolving difficult control and navigation-related problems. This article showcases the autonomous navigation and obstacle avoidance capabilities of self-driving autos through the use of Deep Reinforcement Learning. A virtual vehicle operating in a city environment has these features installed after they have been merged with Deep Q Network. This method takes as input data from two types of sensors: one behind the car, which is a video sensor, and another, which is a laser sensor. Furthermore, it builds a model of an affordable high-speed vehicle that can use the same algorithm in real-time, on top of all that. The design integrates a camera and a Hokuyo Lidar sensor in the front of the vehicle. Deep learning algorithms are executed on the basis of sensor inputs by means of an embedded graphics processing unit (Nvidia-TX2).
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Copyright (c) 2022 Venkata Sai Swaroop Reddy, Nallapa Redd (Author)
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