  Recent years have witnessed enormous progress in AI-related fields such as
computer vision, machine learning, and autonomous vehicles. As with any rapidly
growing field, it becomes increasingly difficult to stay up-to-date or enter
the field as a beginner. While several survey papers on particular sub-problems
have appeared, no comprehensive survey on problems, datasets, and methods in
computer vision for autonomous vehicles has been published. This book attempts
to narrow this gap by providing a survey on the state-of-the-art datasets and
techniques. Our survey includes both the historically most relevant literature
as well as the current state of the art on several specific topics, including
recognition, reconstruction, motion estimation, tracking, scene understanding,
and end-to-end learning for autonomous driving. Towards this goal, we analyze
the performance of the state of the art on several challenging benchmarking
datasets, including KITTI, MOT, and Cityscapes. Besides, we discuss open
problems and current research challenges. To ease accessibility and accommodate
missing references, we also provide a website that allows navigating topics as
well as methods and provides additional information.
