SDVDiag：利用基于上下文的因果挖掘进行联网车辆功能诊断
SDVDiag: Using Context-Aware Causality Mining for the Diagnosis of Connected Vehicle Functions
AI总结：
本文提出一种多模态方法，结合人类反馈和系统信息，提升联网车辆功能诊断的精度与可解释性。
摘要：
现实中的联网车辆功能实施正在稳步扩展，但其分布式性质和底层云、边缘和网络基础设施的复杂性使可靠运行充满挑战。快速诊断问题并理解导致故障的错误链对于减少停机时间至关重要。然而，诊断这些系统仍主要依赖手动操作，因为自动化分析技术大多数据驱动，难以处理隐藏关系和上下文信息。本文通过引入一种多模态方法，将人类反馈和系统特定信息整合到因果分析过程中。利用人类反馈强化学习持续训练因果挖掘模型并融入专家知识。其他模块利用分布式跟踪数据修剪假阳性因果链接，并注入领域特定关系以进一步细化因果图。评估通过在联网车辆测试场中运行的自动化代驾应用进行。结果表明，因果边检测的精度从14%提升至100%，与纯数据驱动方法相比，系统可解释性得到改善，凸显了在联网车辆领域系统操作员的潜力。
英文摘要：
Real-world implementations of connected vehicle functions are spreading steadily, yet operating these functions reliably remains challenging due to their distributed nature and the complexity of the underlying cloud, edge, and networking infrastructure. Quick diagnosis of problems and understanding the error chains that lead to failures is essential for reducing downtime. However, diagnosing these systems is still largely performed manually, as automated analysis techniques are predominantly data-driven and struggle with hidden relationships and the integration of context information. This paper addresses this gap by introducing a multimodal approach that integrates human feedback and system-specific information into the causal analysis process. Reinforcement Learning from Human Feedback is employed to continuously train a causality mining model while incorporating expert knowledge. Additional modules leverage distributed tracing data to prune false-positive causal links and enable the injection of domain-specific relationships to further refine the causal graph.Evaluation is performed using an automated valet parking application operated in a connected vehicle test field. Results demonstrate a significant increase in precision from 14\% to 100\% for the detection of causal edges and improved system interpretability compared to purely data-driven approaches, highlighting the potential for system operators in the connected vehicle domain.
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。
