Deep Learning and Its Applications for Vehicle Networks

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.

深度學習（DL）將成為基於人工智慧的車輛網路的有效方法，並能提供一套強大的工具來處理這種車輛網路動態。在車輛網路的各個領域中，DL可以用於基於學習的通道估計、交通流預測、車輛軌跡預測、基於位置預測的排程和路由、智能網路擁塞控制機制、智能負載平衡和垂直切換控制、智能網路安全策略、虛擬智能和高效資源分配以及智能分散式資源分配方法。

本書基於世界知名專家在車輛網路中應用DL的研究工作。它由以下五個部分組成：(1)「DL用於車輛安全和安全性」：本部分涵蓋了使用DL算法進行車輛安全或安全性的幾個章節。(2)「DL用於有效的車輛通信」：車輛網路包括車輛對車輛和車輛對路邊的通信。智能車輛網路需要在所有車輛中靈活選擇最佳路徑，根據頻寬可用性進行自適應發送速率控制，及時從路邊基站下載數據等。(3)「DL用於車輛控制」：對於每輛車輛，許多操作都需要智能控制：根據道路交通情況控制排放；通過DL預測充電樁負載；根據攝像頭捕捉的圖像分析調整車速。(4)「DL用於信息管理」：本部分涵蓋了一些智能信息收集和理解。我們可以根據道路交通情況和給定的目的地信息使用DL進行節能車輛軌跡控制；我們還可以使用基於DL算法的自然語言處理在駕駛過程中進行自動物聯網（IoT）搜索。(5)「其他應用」。本部分介紹了DL模型在其他車輛控制方面的應用。

自動駕駛車輛在社會中越來越受歡迎。DL及其變體將在認知車輛通信和控制中扮演越來越重要的角色。其他機器學習模型，如深度強化學習，也將促進對智能車輛行為的理解和調整。我們期望本書將成為您理解這一關鍵領域的寶貴參考資料。

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。