CPS:GOALI:Synergy: Maneuver and Data Optimization for High Confidence Testing of Future Automotive Cyberphysical Systems

该项目解决了由于正在和预期将先进的、联网的和自动驾驶的汽车引入批量生产而在汽车的高置信度确认和验证方面面临的紧迫挑战。由于此类车辆跨物理和网络领域运行，故障可能发生在传统物理组件、网络组件(即算法、处理器、网络等)中，或两者兼而有之。因此，先进车辆需要接受物理和网络相关故障条件的测试。该项目的目标是开发理论、方法和新工具，以生成和优化测试轨迹和数据输入，以发现未来汽车的物理和网络故障。考虑到目前汽车的批量生产、低成本和广泛的使用条件，其汽车的可靠性和安全性水平是显著的。如果成功，该项目取得的研究进展将使依赖先进的驾驶员辅助技术、连接和自动驾驶的未来车辆实现类似水平的可靠性和安全性。该项目总体上将推进网络物理系统的发展，特别是它们的生命周期管理。网络物理系统的验证和验证理论和方法将得到扩展，以发现异常和故障，特别是使用全面的基于案例和基于优化的技术来生成测试场景。这些理论进展和案例研究将有助于最优控制理论、博弈论、信息论、数据收集和处理、自动驾驶和联网车辆以及汽车控制方面的最先进技术。将制定基于抽样的车辆数据获取和车辆感知数据管理战略，这些战略可更广泛地应用于基于云的车辆预测/条件维护和移动健康监测设备。最后，将在网络物理系统(CPS)黑匣子原型中实现有效的星载数据收集和聚合方法。将致力于开发车辆感知数据管理系统(VDMS)，从而优化使用CPS黑匣子内的数据挖掘和压缩，以积极降低通信和计算成本。随着理论和方法的进步，汽车案例研究将与一个工业合作伙伴(AVL)合作，进行真实的模拟和真实的实验。

This project addresses urgent challenges in high confidence validation and verification of automotive vehicles due to on-going and anticipated introduction of advanced, connected and autonomous vehicles into mass production. Since such vehicles operate across both physical and cyber domains, faults can occur in traditional physical components, in cyber components (i.e., algorithms, processors, networks, etc.), or in both. Thus, advanced vehicles need to be tested for both physical and cyber-related fault conditions. The goal of this project is to develop theory, methods, and novel tools for generating and optimizing test trajectories and data inputs that can uncover both physical and cyber faults of future automotive vehicles. The level of vehicle reliability and safety achieved for current vehicles is remarkable considering their mass production, low cost, and wide range of operating conditions. If successful, the research advances made in this project will enable achieving similar levels of reliability and safety for future vehicles relying on advanced driver assistance technologies, connectivity and autonomy. The project will advance the field of cyber-physical systems, in general, and their lifecycle management, in particular. The validation and verification theory and methodology for cyberphysical systems will be expanded for uncovering anomalies and faults, especially using comprehensive case-based and optimization-based techniques for test scenario generation. The theoretical advances and case studies will contribute to the state-of-the-art in optimal control theory, game theory, information theory, data collection and processing, autonomous and connected vehicles, and automotive control. Sampling-based vehicle data acquisition and vehicle-aware data management strategies will be developed which can be applied more broadly, e.g., to cloud-based vehicle prognostics / conditional maintenance and mobile health-monitoring devices. Finally, approaches for efficient on-board data collection and aggregation will be implemented in a Cyber-physical system (CPS) Black Box prototype. The development of a vehicle-aware data management system (VDMS) will be pursued, leading to optimized use of data mining and compression inside the CPS Black Box to aggressively reduce the communication and computational costs. Synergistically with theoretical and methodological advances, automotive case studies will be undertaken with both realistic simulations and real experiments in collaboration with an industrial partner (AVL).