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.

翻译：近些年来,在与AI有关的领域,如计算机视觉、机器学习和自主车辆等领域取得了巨大进展。与任何迅速增长的领域一样,越来越难跟上或作为初学者进入现场。虽然出现了关于特定次问题的几份调查文件,但没有发表关于自主车辆计算机视觉方面的问题、数据集和方法的全面调查。这本书试图缩小这一差距,办法是提供对最新数据集和技术的调查。我们的调查包括历史上最相关的文献以及若干具体专题的当前最新水平,包括识别、重建、运动估计、跟踪、现场了解和为自主驾驶进行端到端学习。为实现这一目标,我们分析了包括KITTI、MOT和Cityscraps等若干具有挑战性的基准数据集的艺术表现。此外,我们讨论了公开的问题和当前的研究挑战。为了方便查阅和容纳缺失的参考资料,我们还提供了一个网站,可以浏览专题以及方法,并提供补充信息。