CRII: CPS: Human-Centric Connected and Automated Vehicles for Sustainable Mobility

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公共法律117-2)。该项目将为互联和自动化车辆在人类主导的交通中运行开发新的建模、控制和优化方法，以提高城市交通系统的效率和可持续性，同时尊重个体司机的独特行为和相应的社会规范。这项研究的意义主要体现在以下两个方面。首先，几十年来，城市交通系统的低效导致了大量的燃料浪费和排放。利用车辆日益增长的自主性和连通性，可以通过专门控制和优化车辆操纵和路线，显著提高能效、排放性能和交通管理。其次，在可预见的未来，人类司机仍将是道路上的大多数操作员。由此产生的混合交通中，联网的自动化车辆和人类司机频繁互动地共享道路，需要在社会兼容的背景下对人类司机的行为进行详细的建模。拟议的研究可以产生社会经济激励，如提高城市交通系统的效率，促进对可持续机动性的技术接受，从而缓解国家对能源和环境的担忧。该项目的科学成果将推动控制理论、优化、人类行为研究和机器学习等领域的融合研究。该项目将涉及一个由德克萨斯理工大学学生组成的跨学科团队，通过实践研究机会，该大学历史上一直积极参与为STEM中传统上代表性不足的学生群体服务，为公平和包容的教育和社会结果做出贡献。该项目通过严格的理论处理和实验活动，专注于三个目标：(I)描述现实和常见交通场景中人类驾驶行为的特征。(2)量化各种驾驶行为对城市交通系统的效率和可持续性的影响，特别是在与联网和自动化车辆相互作用时。(3)为以人为中心的联网和自动化车辆设计符合社会要求的控制和优化方案，以最大限度地减少能源消耗和排放。将实施一项渐进的开发、验证和测试计划，包括高保真车辆和交通动力学模型、虚拟驾驶模拟中的人体主体测试，以及在装备齐全的线控车辆上进行现场运动规划。这项研究有望在以人为中心的网络物理系统的数据丰富的建模、控制和优化的更广泛领域产生正式的方法和工具，这些方法和工具可以产生超出本项目考虑的特定应用的变革性影响，如机器人、共享自主性和强化学习。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This project will develop novel modeling, control, and optimization methods for connected and automated vehicles to operate in human-dominated traffic to improve the efficiency and sustainability of the urban transportation system while respecting individual drivers’ unique behaviors and social norms accordingly. The significance of the research is highlighted by the following two needs. First, the inefficiency of the urban transportation system has resulted in substantial fuel waste and emissions over the decades. Leveraging vehicles’ growing autonomy and connectivity, a significant boost of energy efficiency, emission performance, and traffic management can be achieved through dedicated control and optimization of vehicle maneuvers and routes. Second, human drivers will remain the majority of operators on the road in the foreseeable future. The resulting mixed traffic where connected and automated vehicles and human drivers share the road with frequent interactions requires detailed modeling of human drivers’ behaviors in a socially compatible context. The proposed research can generate socioeconomic incentives such as improving the efficiency of the urban transportation system and promoting technology acceptance for sustainable mobility, thereby alleviating the nation’s energetic and environmental concerns. The scientific outcome of the project will advance convergent research areas of control theory, optimization, human behavioral study, and machine learning. The project will involve an interdisciplinary team of students through hands-on research opportunities at Texas Tech University, which has been historically and actively engaged in serving the traditionally underrepresented student body in STEM, contributing towards equitable and inclusive educational and social outcomes.The project focuses on three objectives through a rigorous theoretical treatment and experimental campaign: (i) characterizing human driving behaviors in realistic and common traffic scenarios. (ii) quantifying the impacts of various driving behaviors, especially when interacting with connected and automated vehicles, on the efficiency and sustainability of urban transportation systems. (iii) designing a socially compliant control and optimization scheme of human-centric connected and automated vehicles for minimal energy consumption and emissions. A progressive development, validation, and testing plan will be implemented with a high-fidelity vehicle and traffic dynamics model, human subject tests in virtual driving simulation, and in-field motion planning on a fully equipped drive-by-wire vehicle. The research is expected to yield formal methods and tools in the broader field of data-enriched modeling, control, and optimization of human-centric cyber-physical systems that can have transformative impacts beyond the specific application considered in this project, such as robotics, shared autonomy, and reinforcement learning.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.