End-to-End Learning-based Self-Driving Control Imitating Human Driving

Abstract

In recent years, End-to-End learning-based selfdriving cars have been actively researched. Unlike conventional methods, neural networks are trained to drive like human drivers by mapping directly from sensory data to control commands. In this paper, we propose a neural network architecture for recognizing visual information and controlling the steering and speed of the vehicle like humans.