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

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不能很好地随着应用程序数量的增加而扩展，并且从嵌入式、路边和边缘设备收集的海量数据不堪重负。随着更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为Cavs和支持Cavs运营的智能城市基础设施设计和实施可扩展系统的原则、抽象和方法的发展。该项目的影响在于Cavs的开发和部署，这将导致更安全、更清洁和更高效的交通。该项目开发了：(1)理论模型、框架和软件库，支持在不同CAV平台上设计和实现可扩展的并行算法；(2)高度可扩展的系统，用于机会性地将CAV应用卸载到云/边缘，执行CAV上的混合临界调度和任务卸载选择、边缘性能感知的车辆路径规划以及多跳安全和私有卸载；(3)可扩展和安全的实时协同检测系统，其中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，通过细粒度的交通数据传感，同时满足不断增加的数据量的日益增长的计算需求；以及(5)一个编程框架，包括库和接口，促进为CAV及其支持的智能城市基础设施开发可扩展的应用程序。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.