This book presents a practical roadmap for developing dependable, scalable, and evolvable software-defined vehicles by combining AI-driven engineering, data-centric architectures, and continuous verification. It enables faster deployment of reliable Level 4 autonomous functions while reducing development and validation effort through compliance-by-design methodologies.Its novel contribution is the integration of Generative AI, digital twins, heterogeneous redundancy, and uncertainty-aware inference to bridge probabilistic AI with functional safety requirements. The book covers the entire Software-Defined Vehicle lifecycle, including hardware-software co-design, perception, V2X localization, adaptive vehicle systems, and verification. Intended for automotive engineers, researchers, and graduate students, it serves as both a reference and a practical guide for automating development workflows and implementing scalable assurance for next-generation intelligent vehicles.

This book presents a practical roadmap for developing dependable, scalable, and evolvable software-defined vehicles by combining AI-driven engineering, data-centric architectures, and continuous verification. It enables faster deployment of reliable Level 4 autonomous functions while reducing development and validation effort through compliance-by-design methodologies.Its novel contribution is the integration of Generative AI, digital twins, heterogeneous redundancy, and uncertainty-aware inference to bridge probabilistic AI with functional safety requirements. The book covers the entire Software-Defined Vehicle lifecycle, including hardware-software co-design, perception, V2X localization, adaptive vehicle systems, and verification. Intended for automotive engineers, researchers, and graduate students, it serves as both a reference and a practical guide for automating development workflows and implementing scalable assurance for next-generation intelligent vehicles.