End-to-end Autonomous Driving using Deep Learning: A Systematic Review
It provides a comprehensive overview for researchers and practitioners in autonomous driving, but it is incremental as it synthesizes existing work without introducing new methods.
This paper systematically reviews recent machine learning techniques for end-to-end autonomous driving, focusing on fully differentiable reinforcement learning and deep learning methods, and identifies open challenges and future research directions.
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.