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CPS: Medium: Edge-Cloud Support for Predictable, Global Situational-Awareness for Autonomous Vehicles

CPS：中：边缘云支持自动驾驶汽车的可预测、全球态势感知

基本信息

批准号：
1837382
负责人：
Gabriel Parmer
金额：
$ 100万
依托单位：
George Washington University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837382&HistoricalAwards=false
关键词：
CPS Medium Edge Cloud Support

项目摘要

The goal of this project is improved situation awareness for autonomous vehicles across many different networks. The approach is new theory and abstractions for systems where potentially moving physical systems join and leave the network at a high rate. Making these kinds of cyber-physical systems (CPS) efficient and safe requires leveraging the sensor information from other proximate vehicles over the network: this will enable vehicles to have much higher situational awareness--effectively seeing around corners. However, computation must be performed fast enough to accurately control the physical system, and coordination over networks makes this even more challenging. The research program is paired with an educational initiative integrated into the extensive mentoring program of the researchers, with an emphasis on involving students of diverse backgrounds.This project investigates CPSEdge, a software platform deployed at the network "edge", which aggregates sensor information from nearby vehicles, and intelligently shares resulting plans of action. CPSEdge leverages its network proximity to vehicles, and is carefully designed to reply to vehicles fast enough to keep up with a quickly changing physical environment. The tools and techniques developed for CPSEdge will offer greater situational awareness to autonomous vehicles, and improve the responsiveness, reliability, and security of the software platforms that manage them. CPSEdge is built on a new process abstraction that is lightweight and can scale up to very large systems, even under significant churn, while providing increased reliability and security. This abstraction is managed by the CPSEdge system to ensure that the requisite computation is conducted in real-time with the physical system. Sensor data will be fused to generate a probabilistic model of the environment, providing global planning for nearby vehicles.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.

该项目的目标是提高自动驾驶汽车在许多不同网络中的态势感知能力。该方法是新的理论和抽象的系统，潜在的移动物理系统加入和离开网络以高速率。要使这些类型的网络物理系统（CPS）高效和安全，需要利用网络上其他邻近车辆的传感器信息：这将使车辆具有更高的态势感知能力-有效地看到角落。然而，计算必须足够快才能精确控制物理系统，而网络上的协调使这一点更具挑战性。该研究计划与教育计划相结合，该计划被整合到研究人员的广泛指导计划中，重点是让不同背景的学生参与进来。该项目研究了CPSEdge，一个部署在网络“边缘”的软件平台，它聚合来自附近车辆的传感器信息，并智能地共享由此产生的行动计划。 CPSEdge利用其与车辆的网络接近度，经过精心设计，能够快速响应车辆，以跟上快速变化的物理环境。为CPSEdge开发的工具和技术将为自动驾驶汽车提供更强的态势感知能力，并提高管理它们的软件平台的响应能力、可靠性和安全性。CPSEdge建立在一个新的流程抽象之上，它是轻量级的，即使在严重的动荡下也可以扩展到非常大的系统，同时提供更高的可靠性和安全性。这种抽象由CPSEdge系统管理，以确保必要的计算在物理系统中实时进行。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进行评估，被认为值得支持。