CAREER: Multi-scale Multi-population Mean Field Game-Theoretic Framework for the Autonomous Mobility Ecosystem

这项学院早期职业发展(Career)补助金将通过帮助开发自动驾驶汽车(AV)的控制方法，帮助改善个人福祉和提高美国的经济竞争力。自动驾驶系统有望提高交通安全和效率。然而，在不久的将来，自动驾驶系统将在混合交通的公共道路上运行，并将不得不管理与人驾驶车辆(HV)的复杂交互。该奖项支持的研究将导致自动驾驶车辆在混合交通条件下运行的控制范例，特别是在交通密集且安全运营需要有效的自动跟车和换道控制时。预计该项目将有助于更好地了解未来的交通生态系统，以及引导生态系统走向有利于社会的平衡所需的控制措施。随之而来的教育计划旨在通过新的研究生课程开发和推广计划，利用哥伦比亚附近部署的COSMOS试验台，从根本上重新设计交通工程课程。这项研究的结果将由一个由来自学术界、公共机构和反病毒行业的精选领导人组成的咨询委员会进行评估。本研究从博弈论、动态控制、数据科学和交通工程等领域构建了一个新的建模框架。平均场博弈论方法被用来描述混合交通系统的动态行为，并研究与基础设施规划和技术管制相关的最优策略。这一框架为开发多智能体模拟平台提供了严格的基础，作为交通生态系统发展的一部分，为政策和实践提供信息。该分析框架利用了博弈论和人工智能方法中的最先进技术。这项研究填补了自动驾驶控制文献中的一个重要空白，在这些文献中，自动驾驶基本上被建模为能够更快地“反应”、“看得更远”、更“了解”道路环境的人类司机。这一奖项反映了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。

This Faculty Early Career Development (CAREER) grant will contribute to the improved well-being of individuals and increased U.S. economic competitiveness by assisting in the development of control methods for autonomous vehicles (AV). AVs are anticipated to improve traffic safety and efficiency. In the near future, however, AVs will operate on public roads in mixed traffic and will have to manage complex interactions with human-driven vehicles (HV). This award supports research that will lead to control paradigms for AVs operating in mixed traffic conditions, particularly when traffic is dense and safe operations require effective automated car-following and lane-changing controls. The project is expected to contribute to a better understanding of the future transportation ecosystem and the controls needed to guide the ecosystem toward an equilibrium that benefits society. The accompanying educational plan aims to fundamentally redesign the transportation engineering curricula via new graduate course development and outreach programs, leveraging the COSMOS testbed deployed in Columbia’s neighborhood. The outcomes of this research will be assessed by an advisory committee of select leaders from academia, public agencies, and the AV industry. This research develops a new modeling framework that builds from the fields of game theory, dynamic control, data science, and transportation engineering. Mean-field game-theoretic methods are used to characterize the dynamic behavior of the mixed traffic system and to examine optimal policies associated with infrastructure planning and the regulation of technology. This framework provides a rigorous foundation for the development of a multi-agent simulation platform to inform policy and practice as part of the development of the transportation ecosystem. The analytical framework leverages the state-of-the-art techniques from game theory and AI methods. The research addresses an important gap in the autonomous driving control literature in which AVs are essentially modelled as human drivers that can "react" faster, "see" farther, and "know" the road environment better.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.