CRII: CNS: Bringing Predictable Real-time Computing to Connected Autonomous Driving Systems

CRII：CNS：为互联自动驾驶系统带来可预测的实时计算

基本信息

批准号：
2103604
负责人：
Zheng Dong
金额：
$ 17.49万
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：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103604&HistoricalAwards=false
关键词：
CRII CNS Bringing Predictable Real

项目摘要

Connected vehicle technology is a promising solution to provide reliable autonomous driving that will change the traditional transportation system by building stable, interactive wireless communications between vehicles, the smart infrastructures (e.g., the roadside unit), and personal communications devices. However, achieving reliable and safe connected autonomous driving (CAD) is still very challenging. On one hand, the safety of the CAD system hinges critically on its timing correctness, as crucial driving decisions fully depend on the output of the real-time perception system. On the other hand, requesting information from other devices mayl create additional delays for the on-vehicle real-time perception tasks, and thus the timing correctness of the CAD system can be easily violated by unpredictable communications. This project seeks to bring predictable real-time computing to CAD systems, and the goal of the proposed research is to enable the connected autonomous vehicle and exterior devices to perform real-time perception tasks as a whole by (i) establishing a practical real-time task model to integrate exterior devices into the on-vehicle perception system, which can be implemented on the GPU-enabled computing platforms; (ii) proposing real-time task scheduling algorithms and associated timing validation analysis to guarantee that all the real-time perception tasks can complete at the right time; (iii) developing a prototype CAD system on the autonomous vehicle testbed, HydraOne, and the roadside unit, Equinox, to evaluate the real-time performance of the proposed solutions.Building a CAD system will constitute a major technological breakthrough towards realizing fully autonomous vehicles. In particular, this project emphasizes both scheduling algorithm design and system implementation. The establishment of a real-time suspending-gang task model will enable the first-of-its-kind formalization for depicting the executing flow of real-time workloads executed between the autonomous vehicle and the exterior devices. The real-time task scheduler oversees the entire system and ensures its timing correctness. The creation of new real-time resource allocation methods together with the associated analysis for validating timing constraints will drive the scheduling theory towards real applications in future cyber-physical systems. The proposed research aims to realize the CAD system on the physical platforms (HydraOne/Equinox), with indoor and outdoor studies beyond simulation. Especially, HydraOne/Equinox are ready-to-use platforms that will allow experts/researchers to easily examine their research designs regarding autonomous driving. Educational efforts will be devoted to (i) develop the HydraOne Educational Toolkit for undergraduate education and research, (ii) curriculum design for hands-on learning in the BS/MS program, (iii) summer camp development for K-12 students and teachers, (iv) broadening participation in computing and engineering to enhance diversity.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.

联网车辆技术是一种很有前途的解决方案，可提供可靠的自动驾驶，通过在车辆、智能基础设施（例如路边设备）和个人通信设备之间建立稳定的交互式无线通信，将改变传统的交通系统。然而，实现可靠、安全的互联自动驾驶（CAD）仍然非常具有挑战性。一方面，CAD系统的安全性很大程度上取决于其计时的正确性，因为关键的驾驶决策完全取决于实时感知系统的输出。另一方面，从其他设备请求信息可能会给车载实时感知任务带来额外的延迟，因此 CAD 系统的计时正确性很容易被不可预测的通信所破坏。该项目旨在将可预测的实时计算引入 CAD 系统，拟议研究的目标是通过 (i) 建立一个实用的实时感知系统，使连接的自动驾驶车辆和外部设备作为一个整体执行实时感知任务。时间任务模型，将外部设备集成到车载感知系统中，可以在支持GPU的计算平台上实现；（ii）提出实时任务调度算法和相关的时序验证分析，以保证所有实时感知任务都能在正确的时间完成； (iii) 在自动驾驶汽车测试台 HydraOne 和路边单元 Equinox 上开发原型 CAD 系统，以评估所提出解决方案的实时性能。构建 CAD 系统将构成实现全自动驾驶汽车的重大技术突破。特别是，该项目强调调度算法设计和系统实现。实时悬挂组任务模型的建立将首次形式化地描述自动驾驶车辆和外部设备之间执行的实时工作负载的执行流程。实时任务调度程序监督整个系统并确保其计时的正确性。新的实时资源分配方法的创建以及用于验证时序约束的相关分析将推动调度理论在未来网络物理系统中的实际应用。拟议的研究旨在在物理平台（HydraOne/Equinox）上实现 CAD 系统，并进行模拟之外的室内和室外研究。特别是，HydraOne/Equinox 是即用型平台，可让专家/研究人员轻松检查其有关自动驾驶的研究设计。教育工作将致力于 (i) 开发用于本科教育和研究的 HydraOne 教育工具包，(ii) BS/MS 项目实践学习的课程设计，(iii) 为 K-12 学生和教师开发夏令营，(iv) 扩大对计算和工程的参与，以增强多样性。该奖项反映了 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审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

BlueScale: a scalable memory architecture for predictable real-time computing on highly integrated SoCs

BlueScale：可扩展内存架构，用于在高度集成的 SoC 上进行可预测的实时计算

DOI：
10.1145/3489517.3530612
发表时间：
2022
期刊：
The 59th ACM/IEEE Design Automation Conference
影响因子：
0
作者：
Jiang, Zhe;Yang, Kecheng;Audsley, Neil;Fisher, Nathan;Shi, Weisong;Dong, Zheng
通讯作者：
Dong, Zheng

WatchDog: Real-time Vehicle Tracking on Geo-distributed Edge Nodes