Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art

TL;DR

This comprehensive survey reviews recent advances in computer vision for autonomous vehicles, covering datasets, methods, and challenges, and evaluates current state-of-the-art techniques on benchmark datasets.

Contribution

It provides the first extensive overview of problems, datasets, and methods in computer vision for autonomous vehicles, including performance analysis and open research challenges.

Findings

Performance benchmarks on KITTI, MOT, and Cityscapes datasets.

Identification of key open problems and research challenges.

Summary of current state-of-the-art recognition, reconstruction, and scene understanding methods.

Abstract

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.