End-to-end Autonomous Driving using Deep Learning: A Systematic Review

TL;DR

This paper systematically reviews recent deep learning and reinforcement learning techniques for end-to-end autonomous driving, categorizing approaches, analyzing trends, and discussing open challenges and future research directions.

Contribution

It provides a comprehensive taxonomy and analysis of recent end-to-end autonomous driving methods based on deep learning and reinforcement learning.

Findings

Deep learning techniques dominate recent research.

Taxonomies reveal key approach groupings and trends.

Open challenges include safety, robustness, and real-world deployment.

Abstract

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.