Table Of ContentDeep Learning and Its Applications for
Vehicle Networks
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 mechanisms, 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 of world-famous experts on the application of DL to 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 predicted through DL and vehicle speed adjustments based on 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 use natural language processing based on the 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 increasingly popular in society. 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 the understanding and adjustment of intelligent vehicle
behavior. This book will become a valuable reference for your understanding of this critical field.
Deep Learning and Its
Applications for Vehicle Networks
Edited by
Fei Hu and Iftikhar Rasheed
Cover Image: Shutterstock
First edition published 2023
by CRC Press
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and by CRC Press
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© 2023 selection and editorial matter, Fei Hu and Iftikhar Rasheed; individual chapters, the contributors
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Library of Congress Cataloging-in-Publication Data
Names: Hu, Fei, 1972- editor. | Rasheed, Iftikhar, editor.
Title: Deep learning and its applications for vehicle networks / edited by
Fei Hu and Iftikhar Rasheed.
Description: First edition. | Boca Raton : CRC Press, [2023] | Includes
bibliographical references and index.
Identifiers: LCCN 2022054949 (print) | LCCN 2022054950 (ebook) | ISBN
9781032041377 (hbk) | ISBN 9781032041384 (pbk) | ISBN 9781003190691 (ebk)
Subjects: LCSH: Vehicular ad hoc networks (Computer networks) | Deep
learning (Machine learning)
Classification: LCC TE228.37 .D44 2023 (print) | LCC TE228.37 (ebook) |
DDC 006.3/1--dc23/eng/20230105
LC record available at https://lccn.loc.gov/2022054949
LC ebook record available at https://lccn.loc.gov/2022054950
ISBN: 978-1-032-04137-7 (hbk)
ISBN: 978-1-032-04138-4 (pbk)
ISBN: 978-1-003-19069-1 (ebk)
DOI: 10.1201/9781003190691
Typeset in Times
by SPi Technologies India Pvt Ltd (Straive)
Contents
Preface vii
About the Editors ix
List of Contributors xi
PART I Deep learning for vehicle safety and security 1
1 Deep learning for vehicle safety 3
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 17
Sadegh Arefnezhad and Arno Eichberger
3 A deep learning perspective on Connected Automated Vehicle (CAV)
cybersecurity and threat intelligence 39
Manoj Basnet and Mohd Hasan Ali
PART II Deep learning for vehicle communications 57
4 Deep learning for UAV network optimization 59
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 87
Konstantinos Koufos, Karim El Haloui, Cong Zhou, Valerio Frascolla and
Mehrdad Dianati
6 Deep learning-based index modulation systems for vehicle communications 115
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 133
H. M. Abdul Aziz and Sanjoy Das
v
vi Contents
PART III Deep learning for vehicle control 165
8 Vehicle emission control on road with temporal traffic information using
deep reinforcement learning 167
Zhenyi Xu, Yang Cao, Yu Kang and Zhenyi Zhao
9 Load prediction of an electric vehicle charging pile 179
Peng Shurong, Peng Jiayi, Yang Yunhao and Li Bin
10 Deep learning for autonomous vehicles: a vision-based approach to self-
adapted robust control 215
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 235
11 A natural language processing-based approach for automating IoT search 237
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 259
Xinxin Yang and Bo Gu
13 Sub-signal detection from noisy complex signals using deep learning and
mathematical morphology 273
Jie Wei, Hamilton Clouse and Ashley Diehl
PART V Miscellaneous 293
14 The basics of deep learning algorithms and their effect on driving
behavior and vehicle communications 295
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 321
Aldebaro Klautau, Ilan Correa, Felipe Bastos, Ingrid Nascimento, João Borges,
Ailton Oliveira, Pedro Batista and Silvia Lins
Preface
The term Deep Learning (DL) describes the set of machine learning algorithms that applies large neu-
ral networks with recurrent or convolutional features. In other words, these are Deep Neural Networks
(DNNs) for feature generation, learning, classification, and prediction. Currently, DL has been widely
utilized by many online services provided by Amazon, Google, and Facebook. DL has a wide range of
applications in the domain of the automotive industry, from computer vision processing for autonomous
vehicles to intelligent vehicular networks for providing high data rates.
The forthcoming intelligent vehicles will be equipped with a broad range of sensors and actuators,
for example, the engine controller, radar, light detection and ranging (LIDAR), and cameras, will enable
a vehicle to have knowledge about its surroundings. The vehicles will also incorporate stronger comput-
ing capabilities with larger on-board storage devices. This transformation would mean that a traditional
vehicular network requires smart processing abilities.
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-predic-
tion-based scheduling and routing, intelligent network congestion control mechanism, smart load balanc-
ing and vertical handoff control, intelligent network security strategies, virtual smart & efficient resource
allocation and intelligent distributed resource allocation methods.
This book is a valuable reference or textbook for college students and researchers who want to under-
stand the intelligent vehicle communication systems. It can also be used by industry engineers and techni-
cians for hardware/software design purpose.
This book is based on the work from world-famous experts on the application of DL for vehicle net-
works. It consists of the following five parts:
Part I. 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. Safety aims to overcome the natural faults to ensure that
the autonomous vehicle can correctly recognize the images captured by its camera and adopt the proper
actions. For example, a vehicle can realize the road obstacle ahead and apply the brake in a timely man-
ner. Security aims to overcome intentional attacks from adversaries. For example, one may insert falsified
sensing signals to the vehicle’s RF communication channels to mislead the vehicles; or it pollutes the
imaging data to generate wrong DL results. In this part, we will explain how DL can be used to recognize
the driver’s drowsiness situation. This is also important to vehicle safety.
Part II. 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. In this part, we have selected a few chapters on the use of
DL for different network layers in V2X networks. For example, in the physical layer, DL can be used for
an energy-efficient modulation parameter selections.
Part III. DL for vehicle control: For each individual vehicle, many operations require intelligent con-
trol. For example, the brake needs to be applied with different strength levels depending on the distance
to the obstacle and current vehicle speed. In this part, we have collected a few chapters on the use of DL
for different vehicle operation 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. There are hundreds of sensors in an autonomous vehicle. All those sensors’ data needs to
vii
viii Preface
be fused based on efficient data fusion methods. Then DL could be used to further extract interpretable
information from the sensor data.
Part IV. DL for information management: This part covers some intelligent information collection and
understanding. For example, 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. Vehicle crowdsensing is also a chal-
lenging issue since some vehicles do not have incentives to participate in the sensor network. In this part,
we will introduce the use of DL for valuable information extraction to better understand road environment.
Part V. Other applications: This part introduces the use of DL models for other vehicle controls. For
example, we could use DL to recognize the driver’s behavior to determine whether the driver has a clear
mind during driving. In this part, we have also discussed the simulator design issues to study the use of DL
for computer vision understanding based on the video data collected by the vehicle’s cameras.
Autonomous vehicles are becoming more and more popular in easingly society. The DL and its
variants will play increasingly 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.
About the Editors
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 and 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 is currently working as an assistant professor in the Department of Information and
Communication Engineering at The Islamia University of Bahawalpur, Pakistan. He obtained his Ph.D.
degree at the University of Alabama, United States of America in the field of Electrical Engineering (in
2020). His research interests include wireless communications, 5G/6G cellular systems, artificial intel-
ligence, vehicle-to-everything (V2X) communications, and Cyber Security. He has published many book
chapters and high-quality journal papers.
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