复杂自动驾驶系统的模块化故障诊断框架
Modular Fault Diagnosis Framework for Complex Autonomous Driving Systems
AI总结：
本文针对由异构组件构成且高度依赖驾驶状态的自动驾驶系统，提出了一种模块化故障诊断框架，包含模块化状态监控元素、状态与依赖感知聚合方法及分类方案，并在自动驾驶穿梭巴士上实现与评估以验证其有效性。
摘要：
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（AD）。不同的参与者、众多的用例以及复杂的异构组件，促使了对系统故障诊断和整体系统完整性的需求。AD系统由许多异构组件组成，每个组件具有不同的功能，并且可能使用不同的算法（例如，基于规则的组件与AI组件）。此外，这些组件受车辆驾驶状态的影响，且高度相互依赖。因此，本文通过提出面向AD系统的模块化故障诊断框架概念来解决这一问题。该概念提出了模块化的状态监控与诊断元素，以及一种状态和依赖感知的聚合方法。我们提出的分类方案允许对故障诊断模块进行分类。该概念在AD穿梭巴士上进行了实现，并进行了评估以展示其能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
