Deep Learning and Its Applications for Vehicle Networks
Hu, Fei, Rasheed, Iftikhar
Deep Learning (DL) will be an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart & efficient resource allocation and intelligent distributed resource allocation methods.
This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (1) DL for vehicle safety and security: In this part, we have a few chapters to cover the use of DL algorithms for vehicle safety or security. (2) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. Intelligent vehicle networks require the flexible selection of the best path across all vehicles, the adaptive sending rate control based on bandwidth availability, timely data downloading from roadside base-station, etc. (3) DL for vehicle control: For each individual vehicle, many operations require intelligent control: the emission is controlled based on the road traffic situation; the charging pile load is predicted through DL; the vehicle speed is adjusted based on the camera-captured image analysis. (4) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (5) Other applications. This part introduces the use of DL models for other vehicle controls.
Autonomous vehicles are becoming more and more popular in the society. The DL and its variants will play more and more important roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate the intelligent vehicle behavior understanding and adjustment. We expect that this book will become a valuable reference to your understanding of this critical field.
Dr. Fei Hu is a professor in the department of Electrical and Computer Engineering at the University of Alabama. He has published over 10 technical books with CRC press. His research focus includes cyber security and networking. He obtained his Ph.D. degrees at Tongji University (Shanghai, China) in the field of Signal Processing (in 1999), and at Clarkson University (New York, USA) in Electrical and Computer Engineering (in 2002). He has published over 200 journal/conference papers and books. Dr. Hu's research has been supported by U.S. National Science Foundation, Cisco, Sprint, and other sources. He won the school's President's Faculty Research Award (
Dr. Iftikhar Rasheed has already published many book chapters and journal papers. He is currently an Assistant Professor in the Department of Telecommunication Engineering at The Islamia University Bahawalpur, Pakistan. He obtained his Ph.D. degrees at the University of Alabama, Tuscaloosa, Alabama, USA in the field of Electrical Engineering (in 2020). His research interests include wireless communications, 5G cellular systems, and artificial intelligence, vehicle to everything (V2X) communications, and cybersecurity.
Dr. Fei Hu是阿拉巴馬大學電機與電腦工程系的教授。他與CRC出版社合作出版了超過10本技術書籍。他的研究專注於網絡安全和網絡技術。他在1999年獲得同濟大學（中國上海）的信號處理博士學位，並在2002年獲得克拉克森大學（美國紐約）的電機與電腦工程博士學位。他已發表了200多篇期刊/會議論文和書籍。Hu博士的研究得到了美國國家科學基金會、思科、Sprint和其他資助機構的支持。他曾獲得該校校長教師研究獎。
Dr. Iftikhar Rasheed已經發表了許多專書章節和期刊論文。他目前是巴哈瓦爾布爾伊斯蘭大學電信工程系的助理教授。他在2020年在阿拉巴馬大學獲得了電機工程博士學位。他的研究興趣包括無線通信、5G蜂窩系統、人工智能、車載通信和網絡安全。