Deep Learning and Its Applications for Vehicle Networks 1st Edition

Capture the future of intelligent transportation with Deep Learning and Its Applications for Vehicle Networks, 1st Edition by Fei Hu. This definitive guide connects cutting-edge deep learning methods with the real-world demands of vehicle networks—V2X, VANETs, and autonomous systems—making it essential for researchers, engineers, and city planners.

From the first chapter, you’ll be drawn into clear explanations of neural-network architectures—CNNs, RNNs, GNNs, and reinforcement learning—and how they solve latency, reliability, and safety challenges in connected vehicles. The book balances rigorous theory with practical application: model design, data-driven optimization, edge-cloud collaboration, and performance evaluation techniques tailored for urban mobility, highway systems, and smart-city deployments across North America, Europe, and Asia.

You’ll appreciate case studies and empirical results that demonstrate scalable solutions for traffic prediction, cooperative perception, resource allocation, and intrusion detection in vehicular environments. The author’s accessible style translates complex algorithms into actionable strategies, making the content valuable for graduate students, industry practitioners, and policymakers who need to implement efficient, secure, and robust vehicle-network systems.

Whether you’re developing autonomous driving stacks, optimizing V2X communication, or planning intelligent-transportation infrastructure, this volume equips you with the knowledge to accelerate innovation and improve safety on roads worldwide. Practical, forward-looking, and technically rich, Deep Learning and Its Applications for Vehicle Networks by Fei Hu is a must-have reference for anyone shaping the next generation of connected mobility. Order your copy to stay at the forefront of intelligent transportation research and deployment.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.