自主车辆运动规划的深度强化学习综述
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles
摘要：
近年来，自动驾驶汽车领域的学术研究在传感器技术、V2X通信、安全、决策、控制甚至法律和标准化规则等多个主题方面都达到了很高的水平。除了经典的控制设计方法外，人工智能和机器学习方法几乎存在于所有这些领域。研究的另一部分集中在运动规划的不同层次，如战略决策，轨迹规划和控制。机器学习本身已经开发了广泛的技术，本文描述了其中一个领域，深度强化学习（DRL）。本文提供了深入了解分层运动规划问题，并介绍了DRL的基础知识。设计这样一个系统的主要元素是环境的建模、建模抽象、状态和感知模型的描述、适当的奖励以及底层神经网络的实现。本文介绍了车辆模型，模拟的可能性和计算要求。不同层次的战略决策和观察模型，例如，提出了连续和离散状态表示、基于网格和基于相机的解决方案。本文调查了根据自动驾驶的不同任务和级别系统化的最新解决方案，例如车辆跟随，车道保持，轨迹跟随，合并或在密集交通中驾驶。最后，开放的问题和未来的挑战进行了讨论。
英文摘要：
Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods are present in almost all of these fields. Another part of research focuses on different layers of Motion Planning, such as strategic decisions, trajectory planning, and control. A wide range of techniques in Machine Learning itself have been developed, and this article describes one of these fields, Deep Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning problem and describes the basics of DRL. The main elements of designing such a system are the modeling of the environment, the modeling abstractions, the description of the state and the perception models, the appropriate rewarding, and the realization of the underlying neural network. The paper describes vehicle models, simulation possibilities and computational requirements. Strategic decisions on different layers and the observation models, e.g., continuous and discrete state representations, grid-based, and camera-based solutions are presented. The paper surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, or driving in dense traffic. Finally, open questions and future challenges are discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
