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

CPS:GOALI:Synergy：未来汽车网络物理系统高可信度测试的机动和数据优化

基本信息

批准号：
1544844
负责人：
Ilya Kolmanovsky
金额：
$ 77.5万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-10-01 至 2020-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544844&HistoricalAwards=false
关键词：
CPS GOALI Synergy Maneuver Data

项目摘要

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 &#64257;eld of cyber-physical systems, in general, and their lifecycle management, in particular. The validation and veri&#64257;cation 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).

该项目解决了由于正在进行和预期的先进、联网和自动驾驶汽车的大规模生产而在汽车高置信度验证和验证方面面临的紧迫挑战。由于这样的交通工具跨物理域和网络域两者操作，因此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），或者两者都有。因此，先进的车辆需要测试物理和网络相关的故障条件。该项目的目标是开发理论、方法和新工具，用于生成和优化测试轨迹和数据输入，从而发现未来汽车的物理和网络故障。考虑到其大规模生产、低成本和广泛的操作条件，当前车辆实现的车辆可靠性和安全性水平是显著的。如果成功，该项目的研究进展将使未来车辆依靠先进的驾驶员辅助技术，连接性和自主性实现类似的可靠性和安全性水平。该项目将推进网络物理系统领域，特别是其生命周期管理。确认和验证&#64257;将扩展信息物理系统的阳离子理论和方法，以发现异常和故障，特别是使用基于案例和基于优化的综合技术生成测试场景。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的最新发展。将开发基于采样的车辆数据采集和车辆感知数据管理策略，这些策略可以更广泛地应用，例如，到基于云的车辆性能/条件维护和移动的健康监测设备。最后，有效的机载数据收集和汇总方法将在网络物理系统（CPS）黑匣子原型中实施。将继续开发车辆感知数据管理系统（VDMS），从而优化CPS黑匣子内的数据挖掘和压缩，以大幅降低通信和计算成本。与理论和方法的进步协同，汽车案例研究将与工业合作伙伴（AVL）合作进行逼真的模拟和真实的实验。