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Deep Learning for Autonomous Vehicles

An autonomous vehicle can operate itself and perform necessary functions without any human intervention through the ability to sense its surroundings and also referred to as a driver-less vehicle. The benefits of autonomous vehicles are more efficient transportation, reduced crashes, increased productivity, better fuel savings, reduced pollution, and improved traffic flow. Autonomous vehicles are often categorized into six levels such as automation, driver assistance, partial automation, conditional automation, high automation, and full automation. The important levels of self-driving vehicles, such as perception-discovers the environment and obstacles and uses three sensors, namely camera, LiDAR, and RADAR, localization-defines the position of the vehicle, planning-forms the trajectory based on perception and localization, and control-generates steering angle and acceleration value. Autonomous vehicles use neural networks to detect lane lines, segment the ground, and drive. The technique is end-to-end, i.e., feed an image to a neural network that generates a steering angle. Deep learning is suitable for autonomous vehicle control because it handles problems with a complex and dynamic environment and self-optimizes and adapts its behavior by learning in new scenarios. Convolutional neural networks, recurrent neural networks, and deep reinforcement learning are the major deep learning algorithms used for autonomous vehicles. Future research areas of deep learning for the autonomous vehicle are the deep neural networks for lateral and longitudinal vehicle control, an autonomous vehicle with learning driving maneuvers, vehicle road and edge cloud combination for autonomous driving, and many more.