  End-to-end autonomous driving is a fully differentiable machine learning
system that takes raw sensor input data and other metadata as prior information
and directly outputs the ego vehicle's control signals or planned trajectories.
This paper attempts to systematically review all recent Machine Learning-based
techniques to perform this end-to-end task, including, but not limited to,
object detection, semantic scene understanding, object tracking, trajectory
predictions, trajectory planning, vehicle control, social behavior, and
communications. This paper focuses on recent fully differentiable end-to-end
reinforcement learning and deep learning-based techniques. Our paper also
builds taxonomies of the significant approaches by sub-grouping them and
showcasing their research trends. Finally, this survey highlights the open
challenges and points out possible future directions to enlighten further
research on the topic.
