Advancements in data-driven machine learning have emerged as a pivotal element in supporting automotive software systems (ASSs) engineering across various levels of the V-development process. Duringsystemverificationandvalidation,theintegrationofanintelligent fault detection anddiagnosis (FDD) model with test recordings analysis process serves as a powerful tool for efficiency ensuring functional safety. However, the lack of interpretability of the black-box FDD models developed not only hinders understanding of the cause underlying the prediction, but also prevents the model from being adapted based on the prediction result. This, in turn, increases the computational cost required for developingacomplexFDDmodelandlimitsconfidenceinreal-timesafety-criticalapplications.To address this challenge, a novel explainable method for fault detection, identification, and localization is proposed in this article with the aim of providing a clear understanding of the logic behind the prediction outcome. To this end, a hybrid 1dCNN-GRU-based intelligent model was developed to analyze the recordings from the real-time validation process of ASSs. The employment of explainable AI techniques, i.e., IGs, DeepLIFT, Gradient SHAP, and DeepLIFT SHAP, was instrumental in enabling model adaptation and facilitating the root cause analysis (RCA). The proposed approach is applied to the real time dataset collected during a virtual test drive performed by the user on hardware in the loop system.

翻译：数据驱动机器学习的进步已成为支持汽车软件系统（ASSs）在V型开发流程各层级工程实践中的关键要素。在系统验证与确认阶段，将智能故障检测与诊断（FDD）模型与测试记录分析流程相集成，是确保功能安全效率的强大工具。然而，现有黑盒FDD模型缺乏可解释性，不仅阻碍了对预测背后原因的理解，也使得模型无法基于预测结果进行适应性调整。这进而增加了开发复杂FDD模型所需的计算成本，并限制了其在实时安全关键应用中的可信度。为应对这一挑战，本文提出了一种新颖的可解释故障检测、识别与定位方法，旨在清晰阐释预测结果背后的逻辑。为此，我们开发了一种基于混合1dCNN-GRU的智能模型，用于分析ASSs实时验证过程中的记录数据。通过采用可解释人工智能技术（即积分梯度、DeepLIFT、梯度SHAP和DeepLIFT SHAP），该方法有效实现了模型自适应并促进了根本原因分析（RCA）。所提方法已应用于用户在硬件在环系统上进行虚拟测试驾驶时采集的实时数据集。