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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 monograph 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.
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