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

车联网技术是一种很有前途的解决方案，可以提供可靠的自动驾驶，通过在车辆、智能基础设施（如路边单元）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，实现可靠和安全的互联自动驾驶（CAD）仍然非常具有挑战性。一方面，CAD系统的安全性关键取决于其定时正确性，因为关键的驾驶决策完全依赖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对车载实时感知任务造成额外的延迟，因此CAD系统的时间正确性很容易被不可预测的通信所破坏。本项目旨在将可预测的实时计算引入CAD系统，提出的研究目标是通过(i)建立一个实用的实时任务模型，将外部设备集成到车载感知系统中，该模型可在支持gpu的计算平台上实现；（ii）提出实时任务调度算法及时序验证分析，保证所有实时感知任务在正确的时间完成；（iii）在自动驾驶汽车试验台HydraOne和路边装置Equinox上开发原型CAD系统，以评估所提出解决方案的实时性能。建立CAD系统将是实现全自动驾驶汽车的重大技术突破。本课题特别强调调度算法的设计和系统的实现。实时挂起组任务模型的建立将首次形式化描述自动驾驶车辆与外部设备之间执行的实时工作负载的执行流程。实时任务调度器监督整个系统并确保其时间正确性。新的实时资源分配方法的创建以及验证时间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。提出的研究目标是在物理平台（HydraOne/Equinox）上实现CAD系统，进行室内和室外的模拟研究。特别是，HydraOne/Equinox是现成的平台，可以让专家/研究人员轻松检查他们关于自动驾驶的研究设计。教育方面的努力将致力于(i)开发HydraOne教育工具包，用于本科教育和研究，（ii）设计学士/硕士课程的实践学习课程，（iii）为K-12学生和教师开发夏令营，（iv）扩大计算机和工程的参与，以增强多样性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.