Collaborative Research: Scalable Data-Enabled Predictive Control for Heterogeneous Mixed Traffic Systems

协作研究：异构混合流量系统的可扩展数据支持预测控制

• 批准号: 2320698
• 负责人: Zhaojian Li
• 金额: $ 20.2万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320698&HistoricalAwards=false
• 关键词: Collaborative; Research; Scalable; Data; Enabled

This grant will fund research that enables advancements in transportation efficiency and safety through the deployment of virtually connected and automated vehicles among human-driven vehicles, thereby promoting the progress of science and advancing the national prosperity. While the potential benefits for fuel efficiency and road safety from full vehicle automation and vehicle-to-vehicle communication peak in a traffic system without human drivers, mixed traffic scenarios with coexistence between human-driven vehicles and automated vehicles will be the norm in the intermediate term. A major challenge to the control of automated vehicles in such environments is the requirement that the behavior of the human drivers either be reliably described using explicit car-following models or accurately predicted using computationally efficient, data-driven techniques, neither of which is currently possible. This project aims to resolve this challenge by developing a new model-free, data-efficient control and optimization framework that will enable fast decision-making for efficient, robust, and safe coordination of multiple connected and automated vehicles in mixed traffic systems. The results will be disseminated to the research community and the automotive industry through sharing of open-source software code and organization of a workshop with speakers from both academia and industry. These efforts are closely integrated with educational and outreach activities that aim to increase the participation of undergraduate and high-school students in engineering research.This research aims to develop the foundations of efficient and scalable control designs for connected and automated vehicles that can meet real-time computational constraints and guarantee safe performance in mixed traffic, without explicit modeling of the behavior of human-driven vehicles. It accomplishes this outcome by building a data-driven predictive control framework in which system-level cost functions and constraints are synergistically designed to handle unknown and uncertain traffic dynamics directly from input/output data, and adaptive data library updates respond to time-varying traffic conditions. Additionally, it develops algorithms for scalable, online data compression and distributed optimization that exploit cascading system structures to decompose centralized predictive control problems into those of lower dimension without compromising control performance. Extensive simulations and field experiments conducted in collaboration with an industry partner will be used to evaluate the theoretical outcomes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这笔赠款将用于研究，通过在人类驾驶的车辆中部署虚拟连接的自动化车辆来提高运输效率和安全性，从而促进科学进步和国家繁荣。虽然在没有人驾驶的交通系统中，完全车辆自动化和车对车通信对燃油效率和道路安全的潜在好处达到顶峰，但在中期内，人驾驶车辆和自动车辆共存的混合交通场景将是常态。在这种环境中控制自动化车辆的一个主要挑战是要求使用显式的跟车模型可靠地描述人类驾驶员的行为，或者使用计算高效的数据驱动技术准确预测人类驾驶员的行为，这两种方法目前都不可能实现。该项目旨在通过开发一种新的无模型、数据高效的控制和优化框架来解决这一挑战，该框架将使混合交通系统中的多个互联和自动化车辆能够高效、稳健和安全地进行快速决策。这些成果将通过分享开放源码软件代码和组织研讨会向研究界和汽车业传播，研讨会的发言者来自学术界和工业界。这些努力与旨在增加本科生和高中生参与工程研究的教育和推广活动紧密结合在一起。这项研究旨在为互联和自动化车辆开发高效和可扩展的控制设计基础，这些设计可以满足实时计算限制并保证混合交通中的安全性能，而不需要对人类驾驶车辆的行为进行显式建模。它通过构建数据驱动的预测控制框架来实现这一结果，其中系统级成本函数和约束被协同设计以直接根据输入/输出数据来处理未知和不确定的交通动态，并且自适应数据库更新响应于时变的交通状况。此外，它还开发了可扩展的在线数据压缩和分布式优化算法，这些算法利用级联系统结构将集中式预测控制问题分解为低维问题，而不会影响控制性能。与行业合作伙伴合作进行的大量模拟和现场实验将用于评估理论成果。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。