Real-time Management of Large Fleets of Self-Driving Vehicles Using Virtual Cyber Tracks

高速公路、道路、交通控制系统等交通基础设施必须同时容纳自动驾驶汽车（sdv）和手动驾驶汽车（mdv），这只是时间问题。在几乎所有大都市地区都存在的大规模系统中，问题是我们如何高效、可靠和安全地完成这一任务？这个项目旨在产生设计这样一个系统所需的基础知识。更具体地说，该项目将研究和开发决策模型和算法，以及相应的决策支持系统，以实时管理现有基础设施上的大型sdv和mdv车队，而无需建造特殊的道路或导道。该项目将假设sdv是网络连接的，并且通过计算和通信的网络机制，可以有效地指导这些sdv，无论是单独的还是排的，在我们的交通基础设施上，而不会牺牲移动的舒适性、安全性和效率。决策支持架构的基本概念是(a)实时控制单个sdv的方向、速度和停车，(b)将sdv分组成排，(c)利用道路上的网络虚拟轨道概念，以短而均匀的前进方式移动sdv，以及(d)在道路上提供交通信号，并在虚拟轨道上进行阻塞控制（队列大小和调度），以最大限度地提高吞吐量和其他理想的交通性能指标。除了为实时操作sdv开发的工具外，该项目还将帮助运输机构在道路基础设施扩建有限和道路容量有限的情况下有效地满足日益增长的运输需求。最后，研究和工具将整合到计算机科学、运筹学和运输工程课程的现有和新的课程和实验室中。该项目将解决超大规模自动驾驶代理网络的基础知识，以满足时间和空间分布的旅行者需求。目标是在新的共享SDV网络环境下，建立一套基于多智能体的综合出行优化和控制的新模型。它将研究一种新颖的基于网络轨道的概念和方法，最佳地提供实时指导，以满足sdv的时间和空间分布的旅客需求（从起点到中间排，再到目的地），可能导致新的大规模非线性优化方法，包括车辆动力学和安全/舒适考虑。通过充分利用连接sdv的分布式计算能力，sdv调度和操作系统可以同时对现有高速公路和街道上的单个sdv和队列进行路由和控制。该项目还将基于时空网络轨道网络建模框架来表示有约束的物理交通系统，开发实时算法，用于主动控制交通供应基础设施（如交通信号、匝道仪表、交通信息和拥堵收费），从而优化sdv和mdv的延误和其他性能指标。该项目将集成并行计算和分层系统控制，以及广泛的实时车辆路线/调度算法，包括队列车辆路线、块控制和时间表，以确保SDV运行的安全性、效率和可靠性。该项目还将研究使用云计算、大数据管理和并行计算工具大规模部署sdv的计算可追溯性。该项目将开发从连接的SDV收集蒸汽数据的协议，并为SDV车队管理、分布式计算和有效的物流。

It is only a matter of time before the transportation infrastructure of freeways, roads, and traffic control systems must accommodate self-driving vehicles (SDVs) at the same time as manually driven vehicles (MDVs). In large scale systems that exist in nearly all metropolitan areas, the question is how can we efficiently, reliably and safely accomplish this? This project aims to generate fundamental knowledge needed to design such a system. More specifically, the project will study and develop decision models and algorithms, and attendant decision-support systems to manage, in real time, large fleets of SDVs and MDVs on the current infrastructure, without the need to construct special roads or guideways. This project will assume that SDVs are cyber-connected and that, through cyber mechanisms of computing and communication, it is possible to guide these SDVs, both individually and in platoons, on our transportation infrastructure efficiently, without sacrificing comfort, safety and efficiency in mobility. The fundamental concepts in the decision-support architecture are (a) controlling directions, speeds and stops to individual SDVs in real-time, (b) grouping SDVs in platoons, (c) moving SDVs in platoons, with short and uniform headways, using the concept of cyber-enabled virtual tracks on the roads, and (d) providing traffic signals on the roads and blocking control (platooning sizing and dispatching) on the virtual tracks to maximize throughput and other desirable traffic performance measures. In addition to the tools developed for operating SDVs in real time, this project will help transportation agencies to efficiently satisfy increasing transportation demand with limited road infrastructure expansion and constrained road capacity. Finally, the research and tools will be integrated into the current and new courses and laboratories for computer science, operations research, and transportation engineering courses.This project will address fundamental knowledge in networking self-driving agents at extremely large scales to meet temporally and spatially distributed traveler demand. The goal is to develop a set of new models for integrated traveler mobility optimization and multi-agent-based control under the new environment of shared SDV networks. It will investigate a novel cyber-track based concept and methods that optimally provide real-time guidance to meet temporally and spatially distributed traveler demand for SDVs (from origins, to intermediate platoons, to destinations), possibly leading to new large scale nonlinear optimization methods that include vehicular dynamics and safety/comfort consideration. By taking full advantage of distributed computing power associated with connected SDVs, the dispatching and operating system for SDVs will simultaneously route and control individual SDVs and platoons on existing highways and streets. Based on a space-time cyber track network modeling framework for representing physical transportation system with constraints, the project will also develop real-time algorithms for proactive control of traffic supply infrastructure (e.g., traffic signals, ramp meters, and traffic information and congestion pricing) that optimizes delays and other performance metrics for both SDVs and MDVs. The project will integrate parallel computing and hierarchical system control, as well as a wide range of real-time vehicle routing/scheduling algorithms, including vehicle routing for platooning, block control and timetabling, to ensure the safety, efficiency and reliability of SDV operations. The project will also study the computational tractability of large scale deployment of SDVs using tools of cloud computing, big data management and parallel computation. The project will develop protocols of collecting steaming data from connected SDVs and managing, distributed computing, and effective logistics for SDV fleets.