Reduced Infrastructure Cost in Transportation Systems through Intelligent Signal Processing

通过智能信号处理降低交通系统的基础设施成本

• 批准号: 0729237
• 负责人: Kannan Ramchandran
• 金额: $ 60万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0729237&HistoricalAwards=false
• 关键词: Reduced; Infrastructure; Cost; Transportation; Systems

Rapid advances in computing and communicating technologies, combined with inexpensive access to local area wireless networks, have the potential to revolutionize our nation's vehicular networks. The fundamental bottleneck to the timely deployment of these systems is the the infrastructure needed to support them. This proposal seeks to understand infrastructure requirements and explore architectures driven by considerations of cost, scalability, and robustness. In particular, weopportunistically utilize devices such as cellular phones, WiFi networks, and GPS receivers which, though originally deployed for unrelated applications, can significantly enhance the operation of vehicular networks.This proposal focuses on the systems theory needed to realize this, focusing on an interdisciplinary attack around intelligent distributed signal processing, communications, control and networking. The theoretical advances resulting from this work will in turn provide guidance on the design of new protocols for vehicular networks, particularly in rural areas and the developing world, where cost limits the growth of transportation systems.This proposal seeks to develop both the theoretical foundations and novel constructive designs for next-generation intelligent transportation systems that require minimum cost infrastructure. Motivated by the considerations of cost, scalability, robustness, and reliability, the proposed research thrusts include:(i) high-capacity communication based on multihop relaying and random linear network coding for data distribution in vehicular ad hoc networks;(ii) reliable estimation of traffic field information for congestion management based on parsimonious mobile sampling of probe vehicles, and distributed sensing, compressing, and aggregation of data based on accurate traffic-flow models such as METANET; and (iii) robust low-latency estimation for critical safety and collision avoidance based on investigation of concurrent, real-time distributedestimation of multiple dynamical systems over a shared multi-access wireless channel.

计算和通信技术的快速发展，加上廉价的本地无线网络接入，有可能彻底改变我们国家的车载网络。及时部署这些系统的根本瓶颈是支持它们所需的基础设施。该建议试图理解基础设施需求，并探索由成本、可伸缩性和健壮性等因素驱动的体系结构。特别是，我们利用手机、WiFi网络和GPS接收器等设备，尽管这些设备最初是为不相关的应用而部署的，但它们可以显著增强车载网络的运行。本方案着重于实现这一目标所需的系统理论，着重于围绕智能分布式信号处理、通信、控制和网络的跨学科攻击。这项工作产生的理论进展将反过来为车辆网络的新协议设计提供指导，特别是在成本限制运输系统增长的农村地区和发展中国家。本提案旨在为需要最低成本基础设施的下一代智能交通系统发展理论基础和新颖的建设性设计。基于成本、可扩展性、鲁棒性和可靠性的考虑，提出的研究重点包括：(1)基于多跳中继和随机线性网络编码的车辆自组织网络数据分发的大容量通信；（ii）基于探测车辆的简约移动采样，以及基于METANET等精确交通流模型的分布式感知、压缩和数据聚合，为拥堵管理提供可靠的交通现场信息估计；（iii）基于对共享多接入无线信道上多个动态系统的并发、实时分布式估计的研究，对关键安全和避免碰撞的鲁棒低延迟估计。