Learning to Drive by Imitation: An Overview of Deep Behavior Cloning Methods

There is currently a huge interest around autonomous vehicles from both industry and academia. This is mainly due to recent advances in machine learning and deep learning, allowing the development of promising methods for autonomous driving. The gap toward full autonomy is incrementally being reduced with essentially three main existing approaches. First, Modular systems that combine a pipeline of methods with each solving one specific sub-task of driving. Second, Direct Perception techniques that directly estimate affordances (car orientation, distances between lane borders, etc) used to compute control commands through a simple logic. Finally, end-to-end frameworks that automatically map raw sensor data to actuation values. The objective of this paper is to review some recent works focusing on end-to-end deep learning models for lane stable driving, as well as some publicly available real world datasets and open-source simulators that enable the development and evaluation of such methods.