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 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.
Table of Contents
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
