An explainable hybrid deep learning-enabled intelligent fault detection and diagnosis approach for automotive software systems validation
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 de...
| Authors | Mohammad Abboush, Ehab Ghannoum, Andreas Rausch |
|---|---|
| Primary category | cs.SE |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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