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

到2025年，无人驾驶汽车将成为日常交通不可或缺的一部分。了解自动驾驶汽车的可靠性是确保自动驾驶汽车即将无处不在造成尽可能少的死亡的关键一步。构成此类系统的执行器、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存在固有的缺陷。维修和更换这些部件可能会降低故障发生的风险，但在成本、安全性和可用性方面可能是不可行的。另外，某些故障及其误报可能引发不必要的维修或引起车辆不必要的反应。因此，需要快速、准确地实时识别故障。这项研究将促进现场误差缓解技术的发展，从而实现更可靠的自动驾驶汽车。此外，这项研究将支持北卡罗来纳农工州立大学和北卡罗来纳中央大学的一群代表性不足的研究生和本科生的技术开发和参与，通过课程改进和参与课外活动，如自动驾驶挑战赛，一项全国性的自动驾驶汽车比赛。这项提议的工作将为自动驾驶汽车中发生的短暂、间歇性和永久性故障提供实时诊断。这种分析将大大提高复杂系统故障分类/识别的性能和准确性。多视角错误检测技术，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。数据驱动分析将使用一种新颖的基于模糊2型聚类的方法来检测故障是否会降低性能。芯片级分析将根据逻辑信号和状态跟踪的等效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，并且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意识将有助于汽车系统实时故障诊断，其准确性和速度比任何单独的方法都要高。该奖项反映了美国国家科学基金会的法定使命，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。

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.