Fault diagnosis is crucial for complex autonomous mobile systems, especially for modern-day autonomous driving (AD). Different actors, numerous use cases, and complex heterogeneous components motivate a fault diagnosis of the system and overall system integrity. AD systems are composed of many heterogeneous components, each with different functionality and possibly using a different algorithm (e.g., rule-based vs. AI components). In addition, these components are subject to the vehicle's driving state and are highly dependent. This paper, therefore, faces this problem by presenting the concept of a modular fault diagnosis framework for AD systems. The concept suggests modular state monitoring and diagnosis elements, together with a state- and dependency-aware aggregation method. Our proposed classification scheme allows for the categorization of the fault diagnosis modules. The concept is implemented on AD shuttle buses and evaluated to demonstrate its capabilities.

翻译：故障诊断对于复杂的自主移动系统至关重要，尤其是对于现代自动驾驶系统。不同的参与主体、众多的应用场景以及复杂的异构组件，促使了对系统及整体系统完整性的故障诊断需求。自动驾驶系统由众多异构组件构成，每个组件具有不同的功能，并可能采用不同的算法（例如基于规则的组件与人工智能组件）。此外，这些组件受车辆行驶状态的影响，且彼此高度依赖。因此，本文通过提出一种面向自动驾驶系统的模块化故障诊断框架概念来应对这一问题。该概念提出了模块化的状态监测与诊断单元，以及一种状态感知且考虑依赖关系的聚合方法。我们提出的分类方案允许对故障诊断模块进行归类。该概念已在自动驾驶接驳巴士上实现并经过评估，以验证其能力。