论文标题
端到端驾驶的调查：建筑和培训方法
A Survey of End-to-End Driving: Architectures and Training Methods
论文作者
论文摘要
自动驾驶对行业和学术界都引起了极大的兴趣。长期以来，已经研究了机器学习方法进行自主驾驶的方法，但主要是在感知的背景下进行的。在本文中，我们对所谓的自动驾驶端到端方法进行了更深入的了解，在该方法中，整个驾驶管道都被单个神经网络替换。我们回顾了端到端驾驶文献中的学习方法，输入和输出方式，网络体系结构和评估方案。分别讨论了可解释性和安全性，因为它们在这种方法中仍然具有挑战性。除了提供有关现有方法的全面概述外，我们还结合了结合端到端自动驾驶系统中最有希望的要素的架构。
Autonomous driving is of great interest to industry and academia alike. The use of machine learning approaches for autonomous driving has long been studied, but mostly in the context of perception. In this paper we take a deeper look on the so called end-to-end approaches for autonomous driving, where the entire driving pipeline is replaced with a single neural network. We review the learning methods, input and output modalities, network architectures and evaluation schemes in end-to-end driving literature. Interpretability and safety are discussed separately, as they remain challenging for this approach. Beyond providing a comprehensive overview of existing methods, we conclude the review with an architecture that combines the most promising elements of the end-to-end autonomous driving systems.
