基于假设验证和意图分析的自动驾驶系统分层故障定位
Hierarchical Fault Localization for Autonomous Driving Systems with Hypothesis Validation and Intent Analysis
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中文总结 AI 辅助
针对自动驾驶系统故障难定位问题，提出HINT框架，通过假设验证和意图分析分两阶段进行分层故障定位，无需重新模拟，经在Apollo上评估，该框架在模块级诊断和代码级定位性能强，实际错误端到端准确率达77.8%。
中文摘要 AI 辅助
全面测试对自动驾驶系统的安全和可靠性至关重要。现有技术能检测系统级故障或将其归因于粗粒度模块，但难以在源代码中定位根本原因，调试工作仍很繁重。为此，我们提出了HINT，这是一个基于假设验证和意图分析的用于自动驾驶系统分层故障定位的两阶段框架。第一阶段，HINT将故障触发执行记录转换为多模态抽象并使用因果推理识别责任模块。第二阶段，它重建设计意图和实现行为，然后通过可靠性感知一致性检查定位可疑代码，无需进行代价高昂的重新模拟。我们在Apollo上针对不同故障模式和模块对HINT进行了评估。结果表明，HINT在模块级诊断和代码级定位指标方面实现了最强的整体性能，在实际错误上的端到端Class@5准确率达到77.8%。
英文摘要
Comprehensive testing is essential for the safety and reliability of Autonomous Driving Systems (ADS). Existing techniques can detect system-level failures or attribute them to coarse-grained modules, but they often fall short of localizing the root cause in source code. As a result, debugging remains labor-intensive, requiring developers to connect behavioral violations with complex implementation logic. To address this gap, we present HINT, a two-phase framework for hierarchical ADS fault localization based on hypothesis validation and intent analysis. In Phase I, HINT transforms failure-triggering execution recordings into multi-modal abstractions and uses causal reasoning to identify the responsible module. In Phase II, it reconstructs design-side intent and implementation-side behavior, then localizes suspicious code through reliability-aware consistency checking, without costly re-simulation. We evaluate HINT on Apollo across diverse failure modes and modules. The results show that HINT achieves the strongest overall performance across module-level diagnosis and code-level localization metrics, with 77.8% end-to-end Class@5 accuracy on real-world bugs.
