CAREER: Driving the Future: Models and Control Methods to Coordinate Fleets of Self-Driving Vehicles in Future Transportation Networks

职业：驾驶未来：协调未来交通网络中自动驾驶车队的模型和控制方法

• 批准号: 1454737
• 负责人: Marco Pavone
• 金额: $ 50万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1454737&HistoricalAwards=false
• 关键词: CAREER; Driving; Future; Models; Control

This Faculty Early Career Development (CAREER) Program project advances scientific knowledge on the modeling, analysis, and control of robotic networks consisting of unmanned vehicles autonomously operating in a coordinated fashion to fulfill service requests such as the transportation of people or goods. To work efficiently, such systems must overcome allocation and scheduling challenges that, in practice, can create backups, unacceptable wait times, and detrimental cascade effects. This project will cast the problem within the framework of spatial queuing theory, and investigate theoretical models and real-time control methods to optimally allocate vehicles to service requests. Theory and control algorithms will be applied for the design, system-wide control, and economic assessment of autonomous mobility-on-demand systems. Such systems represent a transformative, rapidly developing mode of transportation where electric, self-driving shuttles transport urban passengers and provide a mobility option to people unable or unwilling to drive. The results of this project will benefit the U.S. economy by fostering clean and efficient future transportation systems and addressing 21st century mobility needs. More broadly, this research is applicable to a large class of robotic coordination problems and will positively impact several critical sectors including automated supply chains and logistics and national security. Experiments on full-scale autonomous shuttles will help broaden the participation of underrepresented groups in research and catalyze engineering education on cyber-physical systems. Current methods for controlling robotic networks are limited, particularly with respect to predictive accuracy and control synthesis with formal performance guarantees. Spatial queuing theory considers dynamic systems consisting of (i) spatially-localized queues that collect service requests generated by an exogenous dynamical process, and (ii) robotic service vehicles traveling among queues in a given network topology. As such, spatial queuing theory models a large variety of robotic coordination problems, with autonomous mobility-on-demand systems as a relevant example. The project will advance knowledge in the field by leveraging recent algorithmic techniques from stochastic network optimization to generate provably-correct tools for the modeling, analysis, and control of spatial queuing systems of increasing complexity and realism. Specifically, this award supports fundamental research to 1) advance the theory of spatial queuing systems, by devising methods for tractable analyses in complex setups, 2) generate control methods with performance guarantees for the optimal assignment of robotic vehicles to service requests, and 3) apply theory and control methods to the control of autonomous mobility-on-demand systems, through case studies and the deployment of algorithms on full scale test beds.

这项教师早期职业发展（职业）计划项目在机器人网络的建模，分析和控制方面促进了由无人驾驶汽车组成的机器人网络的科学知识，以协调的方式自动运行，以满足人们的服务请求，例如人民或商品的运输。 为了有效地工作，此类系统必须克服分配和调度挑战，实际上可以创造备份，不可接受的等待时间和有害的级联效应。该项目将在空间排队理论的框架内提出问题，并研究理论模型和实时控制方法，以最佳地分配车辆以服务请求。理论和控制算法将应用于对按需系统自主行动能力的设计，全系统控制和经济评估。这样的系统代表了一种变革性的，快速发展的运输方式，电动，自动驾驶班车运输城市乘客，并为无法或不愿意驾驶的人们提供出行选择。该项目的结果将通过促进清洁有效的未来运输系统并满足21世纪的流动需求，从而使美国经济受益。更广泛地说，这项研究适用于大量机器人协调问题，将对多个关键部门产生积极影响，包括自动供应链，物流以及国家安全。全面自动班车的实验将有助于扩大代表性不足的群体参与研究，并催化网络物理系统的工程教育。当前控制机器人网络的方法是有限的，尤其是在预测精度和控制综合方面具有正式性能保证。空间排队理论考虑了由（i）（i）空间定位的队列组成的动态系统，这些系统收集了由外源动力学过程产生的服务请求，以及（ii）给定网络拓扑中排队中的机器人服务工具。因此，空间排队理论模拟了各种各样的机器人协调问题，并以按需系统的自动迁移率作为相关的例子。该项目将通过利用最新的算法技术从随机网络优化来推进该领域的知识，从而生成可证明的校正工具，以建模，分析和控制空间排队的复杂性和现实主义的空间排队系统。具体而言，该奖项支持基本研究至1）通过为复杂设置中的可拖动分析设计方法来推进空间排队系统的理论，2）生成具有性能保证的控制方法，以确保机器人车辆最佳分配到服务请求中，以及3）将理论和控制方法应用于自动式流动性量表的全面量表，并通过案例进行录音量表，并通过案例进行详细范围。

项目成果

On the Co-Design of AV-Enabled Mobility Systems

论自动驾驶移动系统的协同设计

• 发表时间: 2020
• 期刊: IEEE International Conference on Intelligent Transportation Systems
• 作者: Zardini, Gioele;Lanzetti, Nicolas;Salazar, Mauro;Censi, Andrea;Frazzoli, Emilio;Pavone, Marco
• 通讯作者: Pavone, Marco

Markets for Efficient Public Good Allocation with Social Distancing

保持社交距离的有效公共物品配置市场

• 发表时间: 2020
• 期刊: Conference of Web and Internet Economics
• 作者: Jalota, Devansh;Pavone, Marco;Qi, Qi;Ye, Yinyu
• 通讯作者: Ye, Yinyu

When Efficiency meets Equity in Congestion Pricing and Revenue Refunding Schemes

当拥堵收费和收入返还计划中效率与公平相遇时

• DOI: 10.1145/3465416.3483296
• 发表时间: 2021
• 期刊: and Optimization
• 作者: Jalota, Devansh;Solovey, Kiril;Gopalakrishnan, Karthik;Zoepf, Stephen;Balakrishnan, Hamsa;Pavone, Marco
• 通讯作者: Pavone, Marco

Real-Time Control of Mixed Fleets in Mobility-on-Demand Systems

按需移动系统中混合车队的实时控制

• DOI: 10.1109/itsc48978.2021.9564770
• 发表时间: 2021
• 期刊: Intelligent Transportation Systems Conference
• 作者: Yang, Kaidi;Tsao, Matthew W.;Xu, Xin;Pavone, Marco
• 通讯作者: Pavone, Marco

Analysis and Control of Autonomous Mobility-on-Demand Systems

• DOI: 10.1146/annurev-control-042920-012811
• 发表时间: 2021-06
• 期刊: Annu. Rev. Control. Robotics Auton. Syst.
• 作者: G. Zardini;Nicolas Lanzetti;M. Pavone;E. Frazzoli