AI-Driven Automotive Software Systems: Model-Based Design, Functional Safety, and Intelligent Mobility

The automotive industry is undergoing a profound transformation from hardware-centric vehicle development to Software-Defined Vehicles (SDVs), where software has become the primary driver of innovation, functionality, and user experience. Modern vehicles integrate numerous Electronic Control Units (ECUs), high-performance computing platforms, advanced sensors, and intelligent communication networks to support increasingly sophisticated features such as Advanced Driver Assistance Systems (ADAS), autonomous driving, connected services, predictive maintenance, and over-the-air (OTA) software updates. This rapid evolution has significantly increased the complexity of automotive software development, requiring new engineering methodologies that ensure reliability, scalability, security, and compliance with stringent safety standards. Artificial Intelligence (AI) has emerged as a key enabling technology for next-generation automotive systems by facilitating perception, decision-making, predictive analytics, intelligent diagnostics, and adaptive control. AI-powered applications—including computer vision, sensor fusion, driver monitoring, predictive maintenance, intelligent energy management for electric vehicles, and autonomous navigation are becoming fundamental components of intelligent mobility. However, integrating AI into safety-critical automotive systems introduces new challenges in model verification, explainability, cybersecurity, real-time execution, and regulatory compliance.To address these challenges, Model-Based Design (MBD) has become an established development paradigm in the automotive domain. By employing executable models, automatic code generation, simulation, and Hardware-in-the-Loop (HiL) testing, MBD enables early system validation, reduces development time, improves software quality, and enhances traceability throughout the software development lifecycle. When combined with AI-driven development, digital twins, and continuous verification, MBD supports rapid prototyping while maintaining the rigorous engineering practices required for safety-critical systems.Functional safety remains a fundamental requirement in automotive software engineering. International standards such as ISO 26262 provide a systematic framework for hazard analysis, Automotive Safety Integrity Level (ASIL) classification, safety architecture development, verification, and validation of electrical and electronic systems. As AI-enabled functionalities become more prevalent, ensuring dependable and trustworthy operation demands the integration of functional safety, Safety of the Intended Functionality (SOTIF), cybersecurity standards (ISO/SAE 21434), and AI assurance techniques throughout the development process. The convergence of Artificial Intelligence, Model-Based Design, Functional Safety, and Intelligent Mobility is redefining the future of transportation. Emerging technologies such as centralized vehicle computing, zonal architectures, Automotive Ethernet, AUTOSAR Adaptive Platform, edge AI, Vehicle-to-Everything (V2X) communication, and cloud-connected mobility are enabling safer, smarter, and more sustainable transportation ecosystems. These advancements are fostering intelligent vehicles capable of autonomous decision-making, seamless connectivity, enhanced user experiences, and reduced environmental impact.Against this backdrop, the research topic "AI-Driven Automotive Software Systems: Model-Based Design, Functional Safety, and Intelligent Mobility" aims to bring together researchers, engineers, and industry practitioners to present cutting-edge advances, innovative methodologies, and practical solutions for the design, verification, validation, and deployment of next-generation automotive software systems. The focus is on bridging the gap between AI innovations and dependable automotive software engineering to accelerate the realization of safe, intelligent, and software-defined mobility.The rapid evolution of Software-Defined Vehicles (SDVs), autonomous driving, and connected mobility has significantly increased the complexity of automotive software systems. Integrating Artificial Intelligence (AI) into safety-critical applications such as Advanced Driver Assistance Systems (ADAS), autonomous perception, battery management, and intelligent vehicle control presents major challenges in ensuring functional safety, cybersecurity, real-time performance, explainability, and regulatory compliance. Traditional development approaches are often inadequate for managing the growing complexity of AI-enabled automotive systems. Recent advances in Model-Based Design (MBD), digital twins, edge AI, high-performance vehicle computing, AUTOSAR Adaptive Platform, Automotive Ethernet, and AI-assisted verification provide new opportunities to accelerate development while improving software quality and reliability. This Research Topic aims to bring together researchers and practitioners to present innovative methodologies, tools, architectures, and case studies that integrate AI, model-based engineering, functional safety, and intelligent mobility. The goal is to promote scalable, trustworthy, and standards-compliant automotive software solutions that support the next generation of safe, connected, autonomous, and sustainable transportation systems.This Research Topic welcomes original research articles, review papers, short communications, and perspective articles addressing recent advances in AI-driven automotive software systems. Contributions should focus on the integration of Artificial Intelligence, Model-Based Design (MBD), Functional Safety, and Intelligent Mobility to develop reliable, secure, and intelligent automotive systems. Topics of interest include, but are not limited to:AI and machine learning for automotive software engineeringModel-Based Design, Model-Based Systems Engineering (MBSE), and digital twinsFunctional safety (ISO 26262), SOTIF, and AI assuranceAUTOSAR Classic and Adaptive PlatformSoftware-Defined Vehicles (SDVs) and zonal architecturesAutomotive Ethernet, in-vehicle networking, and V2X communicationADAS, autonomous driving, perception, and sensor fusionEdge AI, embedded intelligence, and real-time automotive computingVerification, validation, testing, and Hardware-in-the-Loop (HiL) methodologiesCybersecurity (ISO/SAE 21434), over-the-air (OTA) updates, and secure software architecturesIntelligent electric and connected vehicle technologiesExplainable AI, trustworthy AI, and AI-enabled predictive maintenanceSubmissions presenting novel algorithms, architectures, development frameworks, industrial case studies, benchmark datasets, experimental validations, and practical implementations that advance the state of the art in intelligent automotive software engineering are particularly encouraged. Topic editors: Sivakumar P PSG CT, Manimuthu Arunmozhi, Sivakumar P Source: https://www.frontiersin.org/research-topics/85237/ai-driven-automotive-software-systems-model-based-design-functional-safety-and-intelligent-mobility