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

• 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

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，需要部署不断增加的车载传感、通信和计算服务。然而，车辆网络化也带来了新的问题，包括潜在的软件漏洞、安全漏洞和错误的传感器读数，这可能会降低车辆的可靠性并扰乱汽车行业。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交互、对脆弱车载网络的依赖以及无法解决未知异常的情况。该奖项将支持基础研究，以解决使用汽车电池的车辆的网络异常。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，数据分析和图论。研究结果将增强当前车辆的诊断能力，从而使汽车生态系统中的所有各方受益，从汽车制造商到车主。因此，这项研究的结果将有利于美国的经济和社会。 此外，这种多学科的研究将有助于扩大学生参与工程和计算，特别是从代表性不足的群体。电池启用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、普遍适用于所有车辆以及在整个车辆寿命期间可靠的优点。然而，一些科学障碍有待克服，以实现充分的应用潜力。为此，本研究将使用网络物理方法对车辆运行和电池电量之间的依赖关系进行建模，使用2层图模型基于这些依赖关系对车辆进行抽象，并使用该图通过四个渐进步骤指导车辆异常的诊断：a）使用电池作为信任根来检测异常，B）验证检测到的异常以减少错误警报，c）通过图分解来识别故障车辆模块，以及d）通过信息恢复缓解异常，以减少其对车辆运行的负面影响。该奖项反映了NSF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。

项目成果

Rethink Physical Security: Protecting Vehicles via Battery-Enabled Sensing and Control [Point of View]

重新思考物理安全：通过电池驱动的传感和控制保护车辆 [观点]

• DOI: 10.1109/jproc.2023.3285166
• 发表时间: 2023
• 期刊: Proceedings of the IEEE
• 影响因子: 20.6
• 作者: He, Liang;Shin, Kang G.
• 通讯作者: Shin, Kang G.