Diagnosing Vehicles Using Automotive Batteries as Physical Root-of-Trust

使用汽车电池作为物理信任根来诊断车辆

基本信息

批准号：
2231759
负责人：
Liang He
金额：
$ 55.49万
依托单位：
University of Colorado at Denver-Downtown Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2026-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2231759&HistoricalAwards=false
关键词：
Diagnosing Vehicles Using Automotive Batteries

项目摘要

Cyberization is the foundation of automated and intelligent vehicles, requiring the deployment of ever-increasing onboard sensing, communication, and computing services. However, vehicle cyberization also creates new problems, including the potential for software bugs, security vulnerabilities, and erroneous sensor readings, which can degrade vehicle reliability and disrupt the automotive industry. For optimal performance, vehicle diagnostics must consider the interactions across the physical and cyber domains, the reliance on vulnerable in-vehicle networks, and the inability to address unknown anomalies. This award will support fundamental research to address the cyber anomalies of vehicles using automotive batteries. The approach will leverage topics from different disciplines, including vehicular systems, battery management, data analysis, and graph theory. The results will augment current vehicles’ diagnostic ability and thus benefit all parties in the automotive ecosystem, from automakers to car owners. As such, the results from this research will benefit the U.S. economy and society. In addition, this muti-disciplinary research will help broaden student participation in engineering and computing, especially from underrepresented groups. The battery-enabled diagnostic system can overcome all the above-stated limitations of existing solutions, with the advantages of being trustworthy, universally applicable to all vehicles, and reliable throughout the vehicle life. However, some scientific barriers are yet to be overcome to realize the full application potential. To this end, this research will model the dependency between vehicle operation and battery power using a cyber-physical approach, abstract the vehicle based on these dependencies using a 2-layer graph model, and use the graph to guide the diagnostics of vehicle anomalies with four progressive steps: a) detect anomalies using the battery as a root-of-trust, b) verify the detected anomalies to reduce false alarm, c) identify the faulty vehicle module via graph decomposition, and d) mitigate anomalies to reduce their negative impacts on vehicle operation via information recovery.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.

网络化是自动化和智能车辆的基础，需要部署越来越多的车载感官，通信和计算服务。但是，车辆网络化还会产生新的问题，包括软件错误，安全漏洞和错误的传感器读数的潜力，这会降低车辆的可靠性并破坏汽车行业。为了获得最佳性能，车辆诊断必须考虑物理和网络域之间的相互作用，对脆弱的车载网络的依赖以及无法解决未知异常的相互作用。该奖项将支持基础研究，以解决使用汽车电池的网络异常。该方法将利用来自不同学科的主题，包括车辆系统，电池管理，数据分析和图理论。结果将增强当前车辆的诊断能力，从而使从汽车制造商到汽车所有者的汽车生态系统中的所有各方受益。因此，这项研究的结果将使美国经济和社会受益。此外，这项相互学科的研究将有助于扩大学生参与工程和计算的参与，尤其是来自代表性不足的群体。支持电池的诊断系统可以克服现有解决方案的所有上述限制，具有值得信赖的优势，普遍适用于所有车辆，并且在整个车辆寿命中可靠。但是，某些科学障碍尚未克服，以实现全面的应用潜力。为此，这项研究将使用网络物理方法对车辆操作和电池电量之间的依赖进行建模，使用2层图形模型根据这些依赖性抽象车辆，并使用图形指导具有四个渐进步骤的车辆异常的诊断： d）减轻异常情况，以减少通过信息恢复对车辆运营的负面影响。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被视为通过评估而被视为珍贵的支持。