CAREER: Multi-Resolution Model and Context Aware Information Networking for Cooperative Vehicle Efficiency and Safety Systems

美国每年约有3万人死亡，220万人受伤。由于交通堵塞，这一问题还造成了巨大的经济损失。协同车辆效率和安全（CVES）系统的进步有望显着降低运输的人力和经济成本。然而，这种系统的大规模部署受到重大技术和科学差距的阻碍，特别是在实现合作车辆的实时和高精度态势感知方面。这个CAREER项目旨在通过开发基本的信息网络方法来协调控制自动化系统，从而缩小这些差距。这些方法将基于模型化知识传播的创新概念。此外，该项目的教育部分将关于自动联网系统设计的跨学科网络物理系统科目纳入研究生和本科生培训单元。为了实现稳健的运行，CVES系统要求每辆车都能可靠地实时了解其他协调车辆的状态。该项目通过开发一种模型和环境感知的多分辨率信息网络方法，解决了对强大的面向控制的态势感知的迫切需要。该方法是开发模型通信及其衍生的多分辨率网络的新概念。上下文感知模型通信依赖于模型的传输和同步（例如，随机混合系统结构和参数）而不是原始测量。这允许通过网络对CVES的动态模型进行高保真同步。多分辨率网络概念通过模型的可扩展表示来实现。多分辨率模型允许模型保真度在网络内适应可用的网络资源。其结果是鲁棒性的CVES网络服务的变化。CVES的成功部署，即使是部分成功部署，也将通过减少交通事故和提高效率来提供显着的社会效益。该项目将通过解决其可扩展性挑战来实现大规模CVES部署。此外，该项目中开发的方法对新兴的自动驾驶汽车至关重要，预计这些汽车也将通过通信网络协调其行动。通过动态模型的网络同步进行知识传播的基础研究成果将广泛应用于智能电网等CPS领域。该项目的教育部分将针对CPS研究人员和工程师进行智能交通和能源系统方面的培训。

Every year around 30,000 fatalities and 2.2 million injuries happen on US roads. The problem is compounded with huge economic losses due to traffic congestions. Advances in Cooperative Vehicle Efficiency and Safety (CVES) systems promise to significantly reduce the human and economic cost of transportation. However, large scale deployment of such systems is impeded by significant technical and scientific gaps, especially when it comes to achieving real-time and high accuracy situational awareness for cooperating vehicles. This CAREER project aims at closing these gaps through developing fundamental information networking methodologies for coordinated control of automated systems. These methodologies will be based on the innovative concept of modeled knowledge propagation. In addition, the educational component of this project integrates interdisciplinary Cyber-Physical Systems (CPS) subjects on the design of automated networked systems into graduate and undergraduate training modules. For robust operation, CVES systems require each vehicle to have reliable real-time awareness of the state of other coordinated vehicles. This project addresses the critical need for robust control-oriented situational awareness by developing a multi-resolution information networking methodology that is model- and context-aware. The approach is to develop the novel concepts of model communication and its derived multi-resolution networking. Context-aware model-communication relies on transmission and synchronization of models (e.g., stochastic hybrid system structures and parameters) instead of raw measurements. This allows for high fidelity synchronization of dynamical models of CVES over networks. Multi-resolution networking concept is enabled through scalable representations of models. Multi resolution models allow in-network adaptation of model fidelity to available network resources. The result is robustness of CVES to network service variability. The successful deployment of CVES, even partially, will provide significant societal benefits through reduced traffic accidents and improved efficiency. This project will enable large scale CVES deployment by addressing its scalability challenge. In addition, methodologies developed in this project will be crucial to emerging autonomous vehicles, which are also expected to coordinate their actions over communication networks. The fundamental research outcomes on knowledge propagation through network synchronization of dynamical models will be broadly applicable in other CPS domains such as smart grid. The educational component of this project will target training of CPS researchers and engineers on subjects in intelligent transportation and energy systems.