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: 2118202
• 负责人: Daniel Grosu
• 金额: $ 16.38万
• 依托单位: Wayne State University
• 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 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118202&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.

该项目的重点是互联和自动驾驶汽车（CAV）以及支持其运营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良好扩展，并且被从嵌入式、路边和边缘设备收集的大量数据所淹没。随着越来越多的基础设施和车辆传感器开始收集数据，需要新的技术和方法来提高这些系统的可扩展性。该项目的创新之处在于开发了用于设计和实施CAV可扩展系统的原则、抽象和方法，以及支持CAV运行的智能城市基础设施。该项目的影响是CAV的开发和部署，这将导致更安全，更清洁，更高效的运输。该项目开发：（1）理论模型、框架和软件库，以支持异构CAV平台上可扩展并行算法的设计和实现;（2）高度可扩展的系统，用于将CAV应用程序随机卸载到云/边缘，该系统将执行CAV上的混合关键度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）一个可扩展的、安全的实时协作检测系统，CAV利用来自其车载传感器和邻近车辆的传感数据;（4）可扩展的、自适应的交通信号和CAV轨迹协调协议，通过对交通数据的细粒度传感，同时解决数据量增加带来的计算需求增加;以及（5）编程框架，包括库和接口，该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。

项目成果

Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?

• DOI: 10.1109/tits.2023.3275367
• 发表时间: 2022-09
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Syeda Tanjila Atik;Marco Brocanelli;Daniel Grosu

Real-Time Schedulability Analysis for Overloaded Primary-to-Secondary Processor Systems

过载主从处理器系统的实时可调度性分析

• DOI: 10.1109/hpcc-dss-smartcity-dependsys57074.2022.00289
• 发表时间: 2022
• 期刊: Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys
• 作者: Duncan, Mitchell;Raadia, Fatima;Atik, Syeda Tanjila;Brocanelli, Marco;Fisher, Nathan
• 通讯作者: Fisher, Nathan

Data Sharing-Aware Task Allocation in Edge Computing Systems

边缘计算系统中的数据共享感知任务分配

• DOI: 10.1109/edge53862.2021.00018
• 发表时间: 2021
• 期刊: 2021 IEEE International Conference on Edge Computing (EDGE
• 作者: Rabinia, Sanaz;Mehryar, Haydar;Brocanelli, Marco;Grosu, Daniel
• 通讯作者: Grosu, Daniel