The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration and motion control algorithms. We investigate both the modular perception-planning-action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources and computational hardware. The comparison presented in this survey helps to gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices

中文翻译：

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自动驾驶深度学习技术综述

过去十年见证了自动驾驶汽车技术的飞速发展，这主要得益于深度学习和人工智能领域的进步。本文的目的是调查自动驾驶中使用的深度学习技术的当前最先进技术。我们首先介绍基于 AI 的自动驾驶架构、卷积和循环神经网络，以及深度强化学习范式。这些方法构成了调查驾驶场景感知、路径规划、行为仲裁和运动控制算法的基础。我们研究了模块化感知-规划-行动管道，其中每个模块都是使用深度学习方法构建的，以及 End2End 系统，它将感官信息直接映射到转向命令。此外，我们解决了当前在设计自动驾驶 AI 架构时遇到的挑战，例如安全性、训练数据源和计算硬件。本次调查中的比较有助于深入了解深度学习和人工智能方法在自动驾驶方面的优势和局限性，并协助进行设计选择