Deep Learning for Autonomous Driving System

Self-driving cars have developed rapidly in the last decade, owing to advances in deep learning. The primary purpose of this research work is to provide an overview on the implementation of deep learning applications in autonomous driving systems. This research work has been initiated by analyzing the self-driving architectures that use deep learning and neural network combinations, as well as the deep reinforcement learning method These methods form the basis for self-driving scene perception, path planning, and algorithm behavior regulated by motion. Also, this research work analyzes how self-driving architecture is perceived, as well as path planning by implying that each module will be built using deep learning technologies and end-to-end systems. This permits all the self-driving directives to be mapped to the sensory data right away. Also, this research work studies the current challenges involved in designing the self-driving cars with AI-based designs. For example: safety standards, training data and computational hardware. The proposed research study also helps in determining the advantages and disadvantages of deep learning and AI techniques for developing autonomous driving systems.