Deep Learning and Its Applications for Vehicle Networks

Description

Deep Learning (DL) is 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... Read more

Deep Learning (DL) is 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 and 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:

(I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security.

(II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station.

(III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis.

(IV) 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.

(V) Other applications. This part introduces the use of DL models for other vehicle controls.

Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.

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PART I Deep learning for vehicle safety and security

1 Deep learning for vehicle safety

Raiyan Talkhani, Tao Huang, Shushi Gu, Zhaoxia Guo, Guanglin Zhang

and Wei Xiang

2 Deep learning for driver drowsiness classification for a safe vehicle application

Sadegh Arefnezhad and Arno Eichberger

3 A deep learning perspective on Connected Automated Vehicle (CAV)

cybersecurity and threat intelligence

Manoj Basnet and Mohd Hasan Ali

PART II Deep learning for vehicle communications

4 Deep learning for UAV network optimization

Jian Wang, Yongxin Liu, Shuteng Niu and Houbing Song

5 State-of-the-art in PHY layer deep learning for future wireless

communication systems and networks

Konstantinos Koufos, Karim El Haloui, Cong Zhou, Valerio Frascolla and

Mehrdad Dianati

6 Deep learning-based index modulation systems for vehicle communications

Junfeng Wang, Yue Cui, Zeyad A. H. Qasem, Haixin Sun, Guangjie Han and

Mohsen Guizani

7 Deep reinforcement learning applications in connected-automated

transportation systems

H. M. Abdul Aziz and Sanjoy Das

PART III Deep learning for vehicle control

8 Vehicle emission control on road with temporal traffic information using

deep reinforcement learning

Zhenyi Xu, Yang Cao, Yu Kang and Zhenyi Zhao

9 Load prediction of an electric vehicle charging pile

Peng Shurong, Peng Jiayi, Yang Yunhao and Li Bin

10 Deep learning for autonomous vehicles: a vision-based approach to selfadapted

robust control

Gustavo A. Prudencio de Morais, Lucas Barbosa Marcos, José Nuno A. D. Bueno,

Marco Henrique Terra and Valdir Grassi Junior

PART IV DL for information management

11 A natural language processing-based approach for automating IoT search

Cheng Qian, William Grant Hatcher, Weichao Gao, Erik Balsch, Chao Lu and

Wei Yu

12 Towards incentive-compatible vehicular crowdsensing: a reinforcement

learning-based approach

Xinxin Yang and Bo Gu

13 Sub-signal detection from noisy complex signals using deep learning and

mathematical morphology

Jie Wei, Hamilton Clouse and Ashley Diehl

PART V Miscellaneous

14 The basics of deep learning algorithms and their effect on driving

behavior and vehicle communications

Abdennour Najmeddine, Ouni Tarek and Ben Amor Nader

15 Integrated simulation of deep learning, computer vision and physical layer

of UAV and ground vehicle networks

Aldebaro Klautau, Ilan Correa, Felipe Bastos, Ingrid Nascimento, João Borges,

Ailton Oliveira, Pedro Batista and Silvia Lins

Editor(s)

Biography

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.

Deep Learning and Its Applications for Vehicle Networks

Description

Table of Contents

Editor(s)

Biography

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