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: 2118491
• 负责人: Zhiqiang Lin
• 金额: $ 4.24万
• 依托单位: Ohio 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 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118491&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.

该项目的重点是连接和自动驾驶汽车（CAVS）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的扩展性不佳，随着应用的越来越多，并且被嵌入式，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架构和车辆传感器开始收集数据，需要采用新颖的技术和方法来提高这些系统的可扩展性。该项目的新颖性是针对CAVS设计和实施的原理，抽象和方法的发展，以及支持CAVS运营的智能城市基础设施。该项目的影响是骑士的开发和部署，这将导致更安全，更清洁，更有效的运输。该项目的开发：（1）理论模型，框架和软件库，以支持异质CAV平台上可扩展并行算法的设计和实现； （2）一个可将CAV应用程序的机会性卸载到云/边缘的高度尺度系统，该系统将执行CAV中的混合批判性调度和任务脱位选择，边缘绩效 - 感知的车辆路径计划以及多跳的安全和私人卸载； （3）可扩展且安全的实时协作检测系统，其中CAVS从其机上传感器和相邻车辆中利用灵敏度数据； （4）可扩展的，自适应的交通信号和CAV-Trajectory协调协议协议通过流量数据的细粒度灵敏度，同时解决了增加数据量增加的计算需求； （5）一个编程框架，包括图书馆和界面，促进了针对CAVS及其支持的智能城市基础设施的可扩展应用程序的开发。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。

项目成果

Towards a TEE-based V2V Protocol for Connected and Autonomous Vehicles

为联网和自动驾驶汽车打造基于 TEE 的 V2V 协议

• 发表时间: 2022
• 期刊: Workshop on Automotive and Autonomous Vehicle Security (AutoSec
• 作者: Jangid, M.;Lin, Z.
• 通讯作者: Lin, Z.

What You See is Not What You Get: Revealing Hidden Memory Mapping for Peripheral Modeling

• DOI: 10.1145/3545948.3545957
• 发表时间: 2022-10
• 期刊: Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses
• 作者: Jun Yeon Won;Haohuang Wen;Zhiqiang Lin

Replay (Far) Away: Exploiting and Fixing Google/Apple Exposure Notification Contact Tracing

远距离重播：利用和修复 Google/Apple 暴露通知接触者追踪

• DOI: 10.56553/popets-2022-0130
• 发表时间: 2022
• 期刊: Proceedings on Privacy Enhancing Technologies
• 作者: Ellis, Christopher;Wen, Haohuang;Lin, Zhiqiang;Arora, Anish
• 通讯作者: Arora, Anish