CPS: TTP Option: Synergy: Traffic Signal Control with Connected and Autonomous Vehicles in the Traffic Stream

自动驾驶和联网车辆技术的最新进展，加上联邦和州促进广泛使用这些技术的倡议，为提高公路运输的机动性和安全性提供了重要机会。该项目开发信号交叉口控制策略和其他使能传感器机制，通过利用现有的先进技术(连接的车辆和车辆到基础设施的通信、传感器、自动驾驶车辆技术等)，共同优化车辆轨迹和信号控制。交通信号控制是现有交通基础设施的重要组成部分，对交通系统效率、能源消耗和环境影响都有重要影响。除了先进的车辆技术外，制定的战略还考虑到传统车辆在交通流中的存在，以促进在混合车辆环境中过渡到这些新战略。该项目还开发和使用模拟工具来评估这些策略，并提供可在实践中使用的工具，以考虑干线网络中自动化和联网车辆的影响。该项目涉及两个行业合作伙伴(ISS和Econolite)，以帮助促进新产品的开发，以期增加联网和自动驾驶汽车的市场渗透率。该方法将通过佛罗里达大学的模拟、特纳·费尔班克公路研究中心(TFHRC)的现场测试以及也将在现场部署和测试的控制算法进行测试。该项目将支持多名研究生，并将支持创建在线课程。该项目是几个不同学科(优化、传感器、自动化车辆、交通工程)的交叉点，需要产生一个依赖于几个组件的无缝集成的实时工程系统：传感器功能、连接和自动车辆信息通信、信号控制优化策略、丢失和错误信息等。该项目开发和实施优化过程和策略，考虑多个数据源的无缝融合，以及现实世界中不确定和丢失数据的条件下的混合车流(自动、连接和常规车辆)。由于互联车辆和传统车辆的轨迹无法优化或保证，该项目检查了自动车辆的存在对排队的以下车辆的影响。该项目还整合了控制混合车流所需的先进传感技术，以及解决联网和自动驾驶车辆中出现故障的通信问题。

Recent progress in autonomous and connected vehicle technologies coupled with Federal and State initiatives to facilitate their widespread use provide significant opportunities in enhancing mobility and safety for highway transportation. This project develops signalized intersection control strategies and other enabling sensor mechanisms for jointly optimizing vehicle trajectories and signal control by taking advantage of existing advanced technologies (connected vehicles and vehicle to infrastructure communications, sensors, autonomous vehicle technologies, etc.) Traffic signal control is a critical component of the existing transportation infrastructure and it has a significant impact on transportation system efficiency, as well as energy consumption and environmental impacts. In addition to advanced vehicle technologies, the strategies developed consider the presence of conventional vehicles in the traffic stream to facilitate transition to these new strategies in a mixed vehicle environment. The project also develops and uses simulation tools to evaluate these strategies as well as to provide tools that can be used in practice to consider the impacts of automated and connected vehicles in arterial networks. The project involves two industry partners (ISS and Econolite) to help facilitate new product development in anticipation of increased market penetration of connected and autonomous vehicles. The approach will be tested through simulation at University of Florida, through field tests at the Turner Fairbank Highway Research Center (TFHRC) and through the control algorithms that also will be deployed and tested in the field. The project will support multiple graduate students and will support creation of on-line classes. The project is at the intersection of several different disciplines (optimization, sensors, automated vehicles, transportation engineering) required to produce a real-time engineered system that depends on the seamless integration of several components: sensor functionality, connected and autonomous vehicle information communication, signal control optimization strategy, missing and erroneous information, etc. The project develops and implements optimization processes and strategies considering a seamless fusion of multiple data sources, as well as a mixed vehicle stream (autonomous, connected, and conventional vehicles) under real-world conditions of uncertain and missing data. Since trajectories for connected and conventional vehicles cannot be optimized or guaranteed, the project examines the impacts of the presence of automated vehicles on the following vehicles in a queue. The project also integrates advanced sensing technology needed to control a mixed vehicle stream, as well as address malfunctioning communications in connected and autonomous vehicles.