Deep Learning and Its Applications for Vehicle Networks
- Available for pre-order on April 18, 2023. Item will ship after May 9, 2023
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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.
Table of Contents
Part I. Deep Learning for Vehicle Safety and Security
1. Deep Learning for Vehicle Safety by Raiyan Talkhani, Tao Huang, Shushi Gu, Zhaoxia Guo, Guanglin Zhang, Wei Xiang. 2. Deep Learning for Driver Drowsiness Classification for a Safe Vehicle Application by Sadegh Arefnezhad, Arno Eichberger. 3. A Deep Learning Perspective on Connected Automated Vehicle (CAV) Cybersecurity and Threat Intelligence by Manoj Basnet, Mohd. Hasan Ali.
Part II. Deep Learning for Vehicle Communications
4. Deep Learning for UAV Network Optimization by Jian Wang, Yongxin Liu, Shuteng Niu, Houbing Song. 5. State-of-the-Art in PHY Layer Deep Learning for Future Wireless Communication Systems and Networks by Konstantinos Koufos, Karim El Haloui, Cong Zhou, Valerio Frascolla, and Mehrdad Dianati. 6. Deep Learning-based Index Modulation Systems for Vehicle Communications by Junfeng Wang, Yue Cui, Zeyad A. H. Qasem, Haixin, Sun, Guangjie Han, Mohsen Guizani. 7. Deep Reinforcement Learning Applications in Connected-Automated Transportation Systems by HM Abdul Aziz, Sanjoy Das.
Part III. Deep Learning for Vehicle Control
8. Vehicle emission control on road with temporal traﬃc information using deep reinforcement learning by Zhenyi Xu, Yang Cao, Yu Kang, Zhenyi Zhao. 9. Load Prediction of Electric Vehicle Charging Pile by PENG Shurong, PENG Jiayi, YANG Yunhao, LI Bin. 10. Deep learning for autonomous vehicles: a vision-based approach to self-adapted robust control by Gustavo A. Prudencio de Morais, Lucas Barbosa Marcos, José Nuno A. D. Bueno, Marco Henrique Terra, Valdir Grassi Junior.
Part IV. DL for Information Management
11. A Natural Language Processing Based Approach for Automating IoT Search by Cheng Qian, William Grant Hatcher, Weichao Gao, Erik Balsch, Chao Lu, Wei Yu. 12. Towards Incentive-Compatible Vehicular Crowdsensing: A Reinforcement Learning-Based Approach by Xinxin Yang, Bo Gu. 13. Sub-Signal Detection from Noisy Complex Signals Using Deep Learning and Mathematical Morphology by Jie Wei, Hamilton Clouse, Ashley Diehl.
Part V. Miscellaneous
14. The basics of Deep learning algorithms and their effect on driving behavior and vehicle communications by Abdennour Najmeddine, Ouni Tarek, Ben Amor Nader. 15. Integrated Simulation of Deep Learning, Computer Vision and Physical Layer of UAV and Ground Vehicle Networks by Aldebaro Klautau, Ilan Correa, Felipe Bastos, Ingrid Nascimento, João Borges, Ailton Oliveira, Pedro Batista and Silvia Lins.
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 (<1% faculty were awarded each year) in 2020.
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.