Collaborative Research: PPoSS: Planning: Scaling Autonomous Vehicle Systems at the Edge: from On-Board Processing to Cloud Infrastructure

合作研究：PPoSS：规划：扩展边缘自主车辆系统：从车载处理到云基础设施

• 批准号: 2118234
• 负责人: Viswanath Nagarajan
• 金额: $ 4.38万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118234&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Scaling

The focus of this project is on Connected and Autonomous Vehicles (CAVs) and the smart city infrastructure supporting their operation. Current CAV and smart city infrastructure systems do not scale well with the increasing number of applications and are overwhelmed with massive amounts of data collected from embedded, roadside, and edge devices. As more infrastructure and vehicle sensors begin to collect data, novel techniques and methodologies are required to improve the scalability of these systems. The project’s novelties are developments of principles, abstractions, and methodologies for the design and implementation of scalable systems for CAVs and the smart city infrastructure supporting the operation of CAVs. The project's impacts are in the development and deployment of CAVs which will lead to a safer, cleaner, and more efficient transportation. This project develops: (1) theoretical models, frameworks, and software libraries to support the design and implementation of scalable parallel algorithms on heterogeneous CAV platforms; (2) a highly-scalable system for opportunistic offloading of CAV applications to the cloud/edge that will perform on-CAV mixed-criticality scheduling and task-offloading selection, edge-performance-aware vehicle path planning, and multi-hop secure and private offloading; (3) a scalable and secure real-time collaborative detection system in which CAVs leverage sensing data from their on-board sensors and neighboring vehicles; (4) scalable, adaptive traffic-signal and CAV-trajectory coordination protocols via fine-grained sensing of traffic data while addressing the increased computation demands of increased volumes of data; and (5) a programming framework, including libraries and interfaces, that facilitates the development of scalable applications for CAVs and their supporting smart city infrastructure.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.

该项目的重点是联网和自动驾驶车辆 (CAV) 以及支持其运行的智能城市基础设施。当前的 CAV 和智能城市基础设施系统无法随着应用程序数量的增加而很好地扩展，并且被从收集的大量数据淹没。随着越来越多的基础设施和车辆传感器开始收集数据，需要新颖的技术和方法来提高这些系统的可扩展性，该项目的新颖之处在于设计和实现的原理、抽象和方法的发展。的CAV 的可扩展系统以及支持 CAV 运行的智能城市基础设施 该项目的影响在于 CAV 的开发和部署，这将带来更安全、更清洁、更高效的交通。支持异构 CAV 平台上可扩展并行算法的设计和实现的框架和软件库；(2) 一个高度可扩展的系统，用于将 CAV 应用程序机会性卸载到将执行的云/边缘； CAV 混合关键性调度和任务卸载选择、边缘性能感知车辆路径规划以及多跳安全和私有卸载；(3) 一个可扩展且安全的实时协作检测系统，其中 CAV 利用传感数据来自车载传感器和邻近车辆；(4) 通过细粒度的交通数据传感实现可扩展、自适应的交通信号和 CAV 轨迹协调协议，同时满足数据量增加带来的计算需求； (5) 一个编程框架，包括库和接口，促进 CAV 及其支持的智能城市基础设施的可扩展应用程序的开发。该奖项是 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。