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

职业：用于协作车辆效率和安全系统的多分辨率模型和上下文感知信息网络

• 批准号: 1453125
• 负责人: Yaser Fallah
• 金额: $ 42.28万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-15 至 2016-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453125&HistoricalAwards=false
• 关键词: CAREER; Multi; Resolution; Model; Context

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.

美国道路上每年发生约 30,000 人死亡和 220 万人受伤。由于交通拥堵造成的巨大经济损失使问题变得更加复杂。协作车辆效率和安全（CVES）系统的进步有望显着降低运输的人力和经济成本。然而，此类系统的大规模部署受到重大技术和科学差距的阻碍，特别是在实现协作车辆的实时和高精度态势感知方面。该职业项目旨在通过开发用于协调控制自动化系统的基本信息网络方法来缩小这些差距。这些方法将基于建模知识传播的创新概念。此外，该项目的教育部分将自动化网络系统设计的跨学科网络物理系统（CPS）科目整合到研究生和本科生培训模块中。为了实现稳健运行，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。该项目通过开发模型和上下文感知的多分辨率信息网络方法来满足对强大的以控制为导向的态势感知的关键需求。该方法旨在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 CVES 动态模型通过网络进行高保真同步。多分辨率网络概念是通过可扩展的模型表示来实现的。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。结果是 CVES 对网络服务变化的鲁棒性。 CVES 的成功部署，即使是部分部署，也将通过减少交通事故和提高效率来带来显着的社会效益。该项目将通过解决可扩展性挑战来实现大规模 CVES 部署。此外，该项目开发的方法对于新兴的自动驾驶汽车至关重要，这些汽车也有望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其他CPS领域。该项目的教育部分将针对智能交通和能源系统领域的 CPS 研究人员和工程师进行培训。