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

这笔拨款将资助研究，通过在人类驾驶的车辆中部署虚拟连接和自动车辆，从而提高运输效率和安全性，从而促进科学进步，促进国家繁荣。虽然在没有人类驾驶员的交通系统中，全自动驾驶和车对车通信对燃油效率和道路安全的潜在好处将达到顶峰，但从中期来看，人类驾驶车辆和自动驾驶车辆共存的混合交通场景将成为常态。在这种环境下控制自动驾驶汽车的一个主要挑战是，人类驾驶员的行为要么需要使用明确的汽车跟随模型来可靠地描述，要么需要使用计算效率高的数据驱动技术来准确预测，而这两种技术目前都不可能实现。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，该框架将为混合交通系统中多辆互联和自动驾驶车辆的高效、稳健和安全协调提供快速决策。研究结果将通过共享开源软件代码和组织由学术界和工业界发言人参加的研讨会，向研究界和汽车行业传播。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。本研究旨在为网联和自动驾驶车辆开发有效和可扩展的控制设计基础，以满足实时计算约束并保证混合交通中的安全性能，而无需对人类驾驶车辆的行为进行显式建模。它通过建立一个数据驱动的预测控制框架来实现这一结果，在这个框架中，系统级成本函数和约束被协同设计，直接从输入/输出数据处理未知和不确定的交通动态，自适应数据库更新响应时变的交通状况。此外，它还开发了可扩展、在线数据压缩和分布式优化的算法，这些算法利用级联系统结构将集中的预测控制问题分解为较低维度的问题，而不影响控制性能。与行业伙伴合作进行的广泛模拟和现场实验将用于评估理论结果。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.