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

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助，该项目将开发新的建模，控制和优化方法，用于连接和自动驾驶车辆在人类主导的交通中运行，以提高城市交通系统的效率和可持续性，同时尊重个体驾驶员的独特行为和社会规范。研究的重要性体现在以下两个方面。首先，城市交通系统的效率低下，几十年来造成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来显著提高能源效率、排放性能和交通管理。其次，在可预见的未来，人类驾驶员仍将是道路上的大多数运营商。由此产生的混合交通，其中连接和自动驾驶车辆和人类驾驶员通过频繁的交互共享道路，需要在社会兼容的背景下对人类驾驶员的行为进行详细建模。拟议的研究可以产生社会经济激励措施，例如提高城市交通系统的效率，促进可持续交通的技术接受，从而减轻国家的能源和环境问题。该项目的科学成果将推进控制理论、优化、人类行为研究和机器学习等融合研究领域。该项目将涉及一个跨学科的学生团队，通过在德克萨斯理工大学的实践研究机会，该大学一直积极参与服务传统上代表性不足的STEM学生群体，为公平和包容的教育和社会成果做出贡献。该项目通过严格的理论处理和实验活动重点关注三个目标：（i）在现实和常见的交通场景中表征人类驾驶行为。(ii)量化各种驾驶行为的影响，特别是与互联和自动驾驶车辆互动时，对城市交通系统的效率和可持续性的影响。(iii)为以人为中心的互联和自动化车辆设计符合社会要求的控制和优化方案，以实现最低的能源消耗和排放。一个渐进的开发，验证和测试计划将实施高保真车辆和交通动力学模型，虚拟驾驶模拟中的人体测试，以及在设备齐全的线控车辆上的现场运动规划。该研究预计将在以人为中心的网络物理系统的数据丰富建模，控制和优化的更广泛领域中产生正式的方法和工具，这些方法和工具可以产生超越本项目中考虑的特定应用的变革性影响，例如机器人，共享自治，该奖项反映了NSF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。

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.