In the last couple of years, Autonomous Vehicles (AVs) and self-driving cars began to migrate from laboratory development and testing conditions to driving on public roads. Their deployment in our environmental landscape offers a decrease in road accidents and traffic congestions, as well as an improvement of our mobility in overcrowded cities. The main drivers behind this automotive revolution are the advances in artificial intelligence and deep learning.
An autonomous vehicle is an intelligent agent which observes its environment, makes decisions and performs actions based on these decisions. The driving functions map sensory input to control output and are implemented either as modular perception-planning-action pipelines, End2End or Deep Reinforcement Learning systems which directly map observations to driving commands (turn left, turn right, accelerate, decelerate). In a modular pipeline, the main problem is divided into smaller sub-problems, where each module is designed to solve a specific task and deliver the outcome as input to the adjoining component.
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