Excellence in Research: Collaborative Research: Real-time Fault Diagnosis for Self-Driving Vehicles

卓越研究：协作研究：自动驾驶车辆的实时故障诊断

• 批准号: 2000320
• 负责人: Daniel Limbrick
• 金额: $ 81万
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2000320&HistoricalAwards=false
• 关键词: Excellence; Research; Collaborative; Real; time

By 2025, driverless cars will be an integral part of daily transportation. Understanding the reliability of self-driving cars is a crucial step to ensuring that the impending ubiquity of self-driving cars causes as few fatalities as possible. Components like actuators, sensors, and computational elements that make up such systems have inherent vulnerabilities to faults due to manufacturing defects, aging, cyberattacks, and environmental factors. Repair and replacement of such components may reduce the risk of fault occurrences, but may be infeasible in terms of cost, safety, and availability. Alternately, certain faults and their false positives may trigger unnecessary repair or cause unnecessary reactions by the vehicle. Therefore, it is necessary to quickly and accurately identify faults in real time. This research will facilitate the development of in-the-field error mitigation techniques, resulting in more reliable autonomous cars. Furthermore, this research will support the technical development and engagement of an underrepresented cohort of graduate and undergraduate students at North Carolina A&T State University and North Carolina Central University through curriculum enhancements and participation in extracurricular activities such as the AutoDrive Challenge, a national self-driving car competition.The proposed work will provide real-time diagnosis of transient, intermittent, and permanent faults that occur in a self-driving car. This analysis will substantially improve the performance and accuracy of fault classification/identification in complex systems. Multi-perspective error detection techniques, including discrete-event system analysis, data-driven analysis, and chip-level analysis, will be combined to diagnose faults in automotive systems. The discrete-event system analysis will detect and isolate a system's fault occurrences from external observation of general behaviors of the system and in the absence of full observation of occurred events. The data-driven analysis will use a novel fuzzy type-2 clustering-based method to detect whether a fault degraded performance. The chip-level analysis will detect when a computational component is malfunctioning based on equivalence checking of logic signals and state traces. The combination of these approaches will facilitate fault diagnosis of automotive systems in real-time and with greater accuracy and speed. The multi-perspective analysis will improve the understanding of how each perspective interacts with the other and has the potential to identify new fault types and patterns. The enhanced awareness created by integrating these three unique methods will facilitate automotive system fault diagnosis in real time with greater accuracy and speed than could be achieved by any of the methods individually.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.

到2025年，无人驾驶汽车将成为日常交通的组成部分。了解自动驾驶汽车的可靠性是确保即将普及的自动驾驶汽车尽可能减少死亡事故的关键一步。构成此类系统的致动器、传感器和计算元件等组件由于制造缺陷、老化、网络攻击和环境因素而具有固有的故障脆弱性。这些部件的维修和更换可以降低故障发生的风险，但是在成本、安全性和可用性方面可能是不可行的。或者，某些故障及其误报可能触发不必要的维修或引起车辆的不必要反应。因此，有必要真实的实时快速准确地识别故障。这项研究将促进现场错误缓解技术的发展，从而产生更可靠的自动汽车。此外，这项研究将支持技术开发和参与北卡罗来纳州AT州立大学和北卡罗来纳州中央大学的研究生和本科生的代表性不足的队列&，通过课程改进和参加课外活动，如自动驾驶挑战赛，一个全国性的自动驾驶汽车比赛。拟议的工作将提供实时诊断的瞬态，间歇性，以及自动驾驶汽车中发生的永久性故障。这种分析将大大提高复杂系统中故障分类/识别的性能和准确性。多角度错误检测技术，包括离散事件系统分析，数据驱动分析和芯片级分析，将被结合起来诊断汽车系统中的故障。离散事件系统分析将从系统的一般行为的外部观察中检测和隔离系统的故障发生，并且在没有对发生的事件进行全面观察的情况下。数据驱动的分析将使用一种新的基于模糊类型2聚类的方法来检测故障是否降低了性能。芯片级分析将基于逻辑信号和状态迹线的等效性检查来检测计算组件何时发生故障。这些方法的结合将有助于汽车系统的实时故障诊断，并具有更高的准确性和速度。多视角分析将提高对每个视角如何与其他视角相互作用的理解，并有可能识别新的故障类型和模式。通过整合这三种独特的方法所产生的增强的意识将促进汽车系统故障诊断在真实的时间更高的准确性和速度比任何单独的方法可以实现。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

Evaluating the Impact of Hardware Faults on Program Execution in a Microkernel Environment

评估硬件故障对微内核环境中程序执行的影响

• DOI: 10.1109/host54066.2022.9840063
• 发表时间: 2022
• 期刊: 2022 IEEE International Symposium on Hardware Oriented Security and Trust (HOST
• 作者: Bekele, Yohannes B.;Limbrick, Daniel B.
• 通讯作者: Limbrick, Daniel B.

A clustering-based active learning method to query informative and representative samples

• DOI: 10.1007/s10489-021-03139-y
• 发表时间: 2022-02
• 期刊: Applied Intelligence
• 影响因子: 5.3
• 作者: Xuyang Yan;Shabnam Nazmi;Biniam Gebru;Mohd M. Anwar;A. Homaifar;M. Sarkar;Kishor Datta Gupta

Rowhammer Attacks on the Raspberry Pi 3B+

Rowhammer 对 Raspberry Pi 3B 的攻击

• 发表时间: 2021
• 期刊: Government Microcircuit Applications & Critical Technologies Conference
• 作者: Bekele, Yohannes;Yiwere, Ahmed;Limbrick, Daniel B.
• 通讯作者: Limbrick, Daniel B.

A Clustering-based Framework for Classifying Data Streams

• DOI: 10.24963/ijcai.2021/448
• 发表时间: 2021-06
• 期刊: ArXiv
• 作者: Xuyang Yan;A. Homaifar;M. Sarkar;Abenezer Girma;E. Tunstel

Pedestrian Detection for Autonomous Cars: Inference Fusion of Deep Neural Networks

• DOI: 10.1109/tits.2022.3210186
• 发表时间: 2022-12
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: M. Islam;vvAbdullah Al Redwan Newaz;A. Karimoddini