AI-Driven Intelligent Fault Detection and Diagnosis for Automotive Software Systems: From Machine Learning to Foundation Models
A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 November 2026 | Viewed by 2017
Editors
Interests: automotive software systems engineering; model-based system testing phases; verification and validation (V&V); hardware-in-the-loop (HIL); real-time simulation; dault detection and diagnosis of complex systems; fault injection test; reliability and safety analysis; machine learning; deep learning; large language models (LLMs); time-series foundation models
Interests: software architecture; model-driven development; process models; software evolution; longevity of software systems; software verification; machine learned models; data-based software engineering; dependable cyber-physical systems
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Special Issue Information
Dear Colleagues,
Technological advances in artificial intelligence are fundamentally reshaping automotive systems, making them increasingly autonomous, intelligent, and interconnected. In modern vehicles, complex software and data-driven control architectures are now core components that enable advanced functions such as advanced driver-assistance systems (ADASs) and automated driving. While these innovations have played a crucial role in improving future mobility in terms of safety, efficiency, and user experience, significant technical challenges have emerged due to increasing complexity. The greater the complexity of software-intensive automotive systems, the higher the probability of faults occurring in sensors, actuators, and control units, potentially compromising safety-critical functions. Ensuring the safety and reliability of such systems during both manufacturing and operation phases therefore requires advanced fault detection and diagnosis (FDD) methods that go beyond conventional rule- or model-based approaches. These methods should be capable of handling the scale, heterogeneity, and dynamic nature of automotive data.
In recent years, AI-driven approaches for FDD have gained considerable attention from both the scientific community and industry, leading to remarkable progress reported in the literature. Despite these advances, critical challenges remain in terms of the reliability, generalisability, and explainability of such methods, particularly when applied to complex, safety-critical automotive systems.
The aim of this Special Issue is to bring together cutting-edge research contributions that address theoretical developments and innovative engineering applications, as well as advanced tools and platforms for fault detection, isolation, identification, prognosis, and analysis in both conventional and electric automotive systems. We particularly welcome submissions exploring the design and implementation of AI-driven FDD frameworks that leverage machine learning (ML), deep learning (DL), and large language models (LLMs) to enhance the safety, robustness, and efficiency of next-generation automotive technologies during both manufacturing and operation phases. Topics of interest include, but are not limited to, the following:
- ML- and DL-based methods for fault detection and diagnosis of automotive sensors, actuators, and electronic control units (ECUs).
- Application of foundation models and LLMs for analysing time-series and multimodal automotive data.
- Scalable frameworks for FDD in electric, hybrid, and autonomous vehicles.
- Explainability and interpretability of AI-based diagnostic models.
- Robustness and safety assurance of AI-driven FDD methods under ISO 26262 and related standards.
- AI-based behaviour analysis of automotive systems during validation process.
- Fault-tolerant control and predictive maintenance enabled by AI-driven approach.
- Benchmarking and comparative studies on real-world automotive datasets.
Dr. Mohammad Abboush
Prof. Dr. Andreas Rausch
Guest Editors
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Keywords
- safety and reliability
- electric, hybrid, and autonomous vehicles
- verification and validation of automotive systems
- predictive maintenance
- fault detection and diagnosis (FDD)
- fault isolation, identification and prognosis
- explainable AI (XAI)
- fault-tolerant control (FTC)
- large language models (LLMs), deep learning (DL), machine learning (ML)
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