自动驾驶交通系统的协同管控技术  

Coordinated management and control of autonomous traffic systems

作　　者：刘晓波[1] 鲁工圆[1] 郑芳芳 李瑞杰 曹鹏 孔悠 聂宇 Xiaobo Liu;Gongyuan Lu;Fangfang Zheng;Ruijie Li;Peng Cao;You Kong;Yu(Marco)Nie(Department of Transportation and Logistics,Southwest Jiaotong University,Chengdu 610031,China;Department of Civil and Environmental Engineering,Northwestern University,Evanston IL 60208,USA)

机构地区：[1]西南交通大学交通运输与物流学院,成都610031 [2]Department of Civil and Environmental Engineering,Northwestern University,Evanston IL 60208,USA

出　　处：《科学通报》2020年第6期434-441,共8页Chinese Science Bulletin

基　　金：国家重点研发计划(2018YFB1601400);国家自然科学基金(61673321,71671147和61903313);四川省青年科技创新研究团队项目(2019JDTD0002)资助。

摘　　要：过去10年自动驾驶技术的持续进步,为交通系统的协同管控技术提升带来了巨大的机遇和挑战.在微观层面,自动驾驶车因其优于人类驾驶员的反应时间和对周边微环境的精准监控,有望大幅提高道路通行能力,减少事故发生率,消除随机驾驶行为对系统稳定性和鲁棒性的影响.在宏观层面,交通系统管理者可以通过控制自动驾驶车的路径、车道,乃至详细轨迹,抑制拥堵的外部性,减少甚至消除无效拥堵,并以此为出发点指导下一代智慧公路系统的规划设计.本文以此为纲,总结了近年来自动驾驶交通系统协同管控技术领域的研究成果.The rapid development of autonomous driving technology in the past decade has brought unprecedented opportunities and challenges to the coordinated management and control of traffic systems. Compared to human drivers, autonomous vehicles(AV) are equipped with shorter reaction time and better situational awareness, promising to increase the throughput of existing road networks, dramatically improve safety and robustness, and reduce the instability of vehicular traffic flow. Moreover, controlling the route, lane and even trajectory of every AV in a network could effectively eliminate excessive congestion caused by "selfish" human travel and driving behaviors. This ability to coordinate the entire AV fleet thus has important implications for planning the next-generation "smart" road systems.In this paper we consider the coordinated management and control of AV fleets from both macroscopic and microscopic perspectives. The former focuses on how coordinating macroscopic travel decisions, such as route, departure time and mode(e.g., ridesharing vs. micro-transit), might help reduce traffic congestion caused by what is widely known as the price of anarchy in transportation. While the underlying idea is simply moving the system from a user equilibrium state to a system optimal one, the challenge is to model the complex interactions between autonomous and human-driven vehicles, to anticipate how human drivers would behave in such an environment, and to design effective, fair and practical schemes to achieve the goal. What we learn from these efforts could potentially impact infrastructure planning, especially in terms of whether and how AVs should be separated from human-driven vehicles on road.On the other hand, because AVs can be programmed to remember and execute all maneuvers in a journey, much like the auto pilot for airplanes and high-speed trains, the entire trajectory of their journey, not just the aforementioned macroscopic travel decisions, may be targeted for the purpose of traffic management. Here, the focus i

关 键 词：自动驾驶 协同管控 安全性 鲁棒性 智慧道路系统 

分 类 号：U463.6[机械工程—车辆工程] U495[交通运输工程—载运工具运用工程]