为复杂自动驾驶系统设计模块化故障诊断框架，提升系统可靠性。
Modular Fault Diagnosis Framework for Complex Autonomous Driving Systems
- 采用模块化监控与依赖感知聚合，实现分组件精准诊断
- 在自动驾驶接驳车上验证，支持多类型算法协同诊断
- 适合自动驾驶系统研发与安全团队参考应用
故障诊断对复杂的自主移动系统至关重要，尤其在现代自动驾驶（AD）系统中。由于涉及多种参与者、大量使用场景以及异构组件，系统整体完整性需要有效保障。自动驾驶系统由多个异构组件构成，各具不同功能并可能采用不同算法（如规则驱动与人工智能组件）。同时，这些组件受车辆行驶状态影响，彼此高度依赖。为此，本文提出一种面向自动驾驶系统的模块化故障诊断框架，包含模块化状态监控与诊断单元，以及状态和依赖关系感知的聚合方法。所提出的分类方案可对诊断模块进行系统性归类。该框架已在自动驾驶接驳车上实现并评估，验证了其有效性与实用性。
原文摘要 · Abstract (English)
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.
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