CAREER: Tackling Congestion in Smart Cities via Data-Driven Optimization-Based Control of Connected and Automated Vehicles

这项教师早期职业发展计划(Career)奖支持使用来自许多联网车辆的实时高频数据来自动控制驾驶和路线决策所需的基础研究。这项研究本质上是跨学科的，因为它需要了解不同领域的研究和教育方法，包括交通系统、运筹学、控制理论、车辆动力学、数据科学和计算机科学。该项目将通过课程开发和研究参与，帮助培训下一代科学家和工程师，使他们具有多学科的观点。PI将通过一系列在线工具和案例研究，通过面向第一代大学生的暑期实习计划和青年发展指导计划，与高中教师和学生密切合作，目标群体是代表性不足的少数族裔。该项目旨在为联网和自动化车辆提供实时、在线和可预测的最优驾驶和路线决策。最终，该项目将有助于解决智能城市的交通拥堵问题。这项研究将填补将实时高频连接车辆数据集成到在线强大的自动驾驶和路线控制系统中的知识空白。具体地说，本研究将(1)通过“预测他人预测”和在线学习的概念，提出基于数据驱动优化的模型预测控制(MPC)模型；(2)在参与者和运力不确定的情况下，集成n人动态路径博弈和非合作连接和自动路径的MPC模型；(3)通过优化队形来平衡合作自动驾驶和非合作自动路径，以提高网络效率。为了解决大规模网络中的计算可伸缩性问题，本研究还将探索虚拟游戏型算法来解决n人动态路由博弈问题，以及利用分布式优化来解决排队编队问题中的计算问题。将进行模拟分析和道路测试，以使用互联和自动化车辆车队对模型进行可行性分析。该奖项反映了NSF的法定使命，并已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。

This Faculty Early Career Development Program (CAREER) award supports fundamental research needed to use real-time high-frequency data from many connected vehicles to automatically control driving and routing decisions. This research is interdisciplinary in nature as it requires the understanding of research and education approaches in various fields including transportation systems, operations research, control theory, vehicle dynamics, data science, and computer science. The project will contribute to training the next generation of scientists and engineers in multidisciplinary perspectives through curriculum development and research involvement. The PI will work closely with high school teachers and students, targeting underrepresented minorities, through a Summer Internship Program for first-generation college students and a Youth Development Mentoring Program using a set of online tools and case studies. The project is to provide real-time, online, and predictive robust optimal driving and routing decisions to connected and automated vehicles. Ultimately, the project will help address traffic congestion in smart cities. This research will fill knowledge gaps in the integration of real-time high-frequency connected vehicle data into online robust automated driving and routing control systems. Specifically, this research will (1) suggest data-driven optimization-based model predictive control (MPC) models through the concepts of "forecasting the forecasts of others" and online learning for cooperative connected and automated driving under traffic uncertainty; (2) integrate n-person dynamic routing games and MPC models for non-cooperative connected and automated routing under player and capacity uncertainties; and (3) balance cooperative automated driving and non-cooperative automated routing through optimizing platoon formation to increase network efficiency. To address computational scalability issues in large scale networks, this research will also explore fictitious play type algorithms to solve the n-person dynamic routing games, as well as leverage distributed optimization to address computational issues in platoon formation problems. Simulation analysis and road testing will be conducted for feasibility analysis of the models using a fleet of connected and automated vehicles.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.