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

职业：自主移动生态系统的多尺度多群体平均场博弈论框架

基本信息

批准号：
1943998
负责人：
Xuan Di
金额：
$ 58.41万
依托单位：
Columbia University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943998&HistoricalAwards=false
关键词：
CAREER Multi scale population Mean

项目摘要

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.

该学院早期职业发展 (CAREER) 拨款将通过协助开发自动驾驶车辆 (AV) 的控制方法来改善个人福祉并提高美国的经济竞争力，预计将提高交通安全和效率。然而，在不久的将来，自动驾驶汽车将在混合交通的公共道路上运行，并且必须管理与人类驾驶车辆 (HV) 的复杂交互。该奖项支持将导致自动驾驶汽车在混合交通条件下运行的控制范例的研究。当交通密集且安全的运营需要有效的自动跟车和变道控制，该项目预计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实现有利于社会的平衡所需的控制。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 测试平台，从根本上重新设计交通工程课程。这项研究的成果将由来自学术界、公共机构和自动驾驶行业的精选领导人组成的咨询委员会进行评估。开发了一个基于博弈论、动态控制、数据科学和交通工程领域的新建模框架，使用平均场博弈论方法来表征混合交通系统的动态行为并检查与此相关的最优策略。该框架为多主体模拟平台的开发提供了严格的基础，作为交通生态系统发展的一部分，为政策和实践提供信息。该研究涉及博弈论和人工智能方法的艺术技巧。自动驾驶控制文献中的一个重要空白，其中自动驾驶汽车本质上被建模为人类驾驶员，可以“反应”更快，“看得”更远，并且更好地“了解”道路环境。该奖项反映了 NSF 的法定使命，并被认为是值得的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来提供支持。