论文标题
关于混合自治时代的自动驾驶控制的调查：从基于物理学到AI引导的驾驶政策学习
A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning
论文作者
论文摘要
本文是在混合自主权时代，从人工智能（AI）到自动驾驶汽车（AV）控制的运输工程领域的潜在有用模型和方法的介绍和概述。我们将讨论AI引导方法的最新应用，确定机遇和障碍，提出开放的问题，并帮助建议AI可以在混合自治中发挥作用的基础和领域。我们将自动驾驶汽车（AV）部署的阶段分为四个阶段：纯HVS，以HV为主导的AV量和纯AV。本文主要集中于后三个阶段。这是最初的调查文件，可全面审查交通工程和AI的文献，用于混合交通建模。总结为每个阶段使用的模型，包括游戏理论，深（强化）学习和模仿学习。在审查方法论时，我们主要关注以下研究问题：（1）哪些可扩展的驾驶政策控制着由人类驾驶员和无法控制的AV组成的混合交通中的大量AV？ （2）我们如何估计人类驾驶员行为？ （3）如何在环境中建模不可控制的AV的驾驶行为？ （4）人类驾驶员与自动驾驶汽车之间的相互作用如何？希望本文不仅会激发我们的运输社区重新考虑在数据缩影时代开发的传统模型，而且还可以接触其他学科，尤其是机器人和机器学习，还可以联手建立安全有效的混合交通生态系统。
This paper serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering for autonomous vehicle (AV) control in the era of mixed autonomy. We will discuss state-of-the-art applications of AI-guided methods, identify opportunities and obstacles, raise open questions, and help suggest the building blocks and areas where AI could play a role in mixed autonomy. We divide the stage of autonomous vehicle (AV) deployment into four phases: the pure HVs, the HV-dominated, the AVdominated, and the pure AVs. This paper is primarily focused on the latter three phases. It is the first-of-its-kind survey paper to comprehensively review literature in both transportation engineering and AI for mixed traffic modeling. Models used for each phase are summarized, encompassing game theory, deep (reinforcement) learning, and imitation learning. While reviewing the methodologies, we primarily focus on the following research questions: (1) What scalable driving policies are to control a large number of AVs in mixed traffic comprised of human drivers and uncontrollable AVs? (2) How do we estimate human driver behaviors? (3) How should the driving behavior of uncontrollable AVs be modeled in the environment? (4) How are the interactions between human drivers and autonomous vehicles characterized? Hopefully this paper will not only inspire our transportation community to rethink the conventional models that are developed in the data-shortage era, but also reach out to other disciplines, in particular robotics and machine learning, to join forces towards creating a safe and efficient mixed traffic ecosystem.
