The book combines theoretical foundations with practical methodologies and experimental validation. It introduces advanced techniques including frequency-aware temporal modeling, hypergraph convolutional networks, manifold learning, multimodal large language models, and latent-flow diversification methods for safety-critical scenario generation. Designed for researchers, graduate students, and industry practitioners, the book serves as a valuable reference for intelligent driving, artificial intelligence, biomedical engineering, and cognitive computing applications in future transportation systems.