Deep learning and its applications for vehicle networks

• Titel: Deep learning and its applications for vehicle networks
• Mitwirkende: Hu, Fei [HerausgeberIn]
• Mitwirkende: Rasheed, Iftikhar [HerausgeberIn]
• Verf.angabe: edited by Fei Hu and Iftikhar Rasheed
• Ausgabe: First edition.
• Verlagsort: Boca Raton
• Verlag: CRC Press
• E-Jahr: 2023
• Jahr: [2023]
• Umfang: 1 online resource
• Fussnoten: Includes bibliographical references and index. - Description based on print version record and CIP data provided by publisher; resource not viewed
• ISBN: 978-1-003-19069-1
• ISBN: 1-003-19069-3
• ISBN: 978-1-000-87723-6
• ISBN: 1-000-87723-X
• ISBN: 978-1-000-87725-0
• ISBN: 1-000-87725-6
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Sach-SW: COMPUTERS / Artificial Intelligence
• Sach-SW: COMPUTERS / Networking / General
• Sach-SW: Deep learning (Machine learning)
• Sach-SW: Vehicular ad hoc networks (Computer networks)
• K10plus-PPN: 1871288509
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4418448985
• Lokale URL Inst.: Zum Volltext

Abstract

"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"--