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 traject...
Gespeichert in:
| Weitere beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton, FL
CRC Press, 2023.
2023
|
| Ausgabe: | First edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781000877250/?ar |
| Zusammenfassung: | "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"-- |
| Beschreibung: | Includes bibliographical references. - Description based on online resource; title from digital title page (viewed on January 12, 2024) |
| Umfang: | 1 Online-Ressource (xiii, 342 Seiten) illustrations |
| ISBN: | 9781003190691 1003190693 9781000877236 100087723X 9781000877250 1000877256 |
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| 245 | 1 | 0 | |a Deep learning and its applications for vehicle networks |c edited by Fei Hu and Iftikhar Rasheed |
| 250 | |a First edition. | ||
| 264 | 1 | |a Boca Raton, FL |b CRC Press, 2023. |c 2023 | |
| 300 | |a 1 Online-Ressource (xiii, 342 Seiten) |b illustrations | ||
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| 520 | |a "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"-- | ||
| 650 | 0 | |a Vehicular ad hoc networks (Computer networks) | |
| 650 | 0 | |a Deep learning (Machine learning) | |
| 650 | 4 | |a Réseaux ad hoc de véhicules | |
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| 650 | 4 | |a COMPUTERS / Artificial Intelligence | |
| 650 | 4 | |a COMPUTERS / Networking / General | |
| 650 | 4 | |a Deep learning (Machine learning) | |
| 650 | 4 | |a Vehicular ad hoc networks (Computer networks) | |
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| discipline | Informatik |
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| id | ZDB-30-ORH-098490591 |
| illustrated | Illustrated |
| indexdate | 2025-01-17T11:22:18Z |
| institution | BVB |
| isbn | 9781003190691 1003190693 9781000877236 100087723X 9781000877250 1000877256 |
| language | English |
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| physical | 1 Online-Ressource (xiii, 342 Seiten) illustrations |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2023 |
| publishDateSearch | 2023 |
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| publisher | CRC Press, 2023. |
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| spelling | Deep learning and its applications for vehicle networks edited by Fei Hu and Iftikhar Rasheed First edition. Boca Raton, FL CRC Press, 2023. 2023 1 Online-Ressource (xiii, 342 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references. - Description based on online resource; title from digital title page (viewed on January 12, 2024) "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"-- Vehicular ad hoc networks (Computer networks) Deep learning (Machine learning) Réseaux ad hoc de véhicules Apprentissage profond COMPUTERS / Artificial Intelligence COMPUTERS / Networking / General Hu, Fei 1972- HerausgeberIn edt Rasheed, Iftikhar HerausgeberIn edt 9781032041377 Erscheint auch als Druck-Ausgabe 9781032041377 |
| spellingShingle | Deep learning and its applications for vehicle networks Vehicular ad hoc networks (Computer networks) Deep learning (Machine learning) Réseaux ad hoc de véhicules Apprentissage profond COMPUTERS / Artificial Intelligence COMPUTERS / Networking / General |
| title | Deep learning and its applications for vehicle networks |
| title_auth | Deep learning and its applications for vehicle networks |
| title_exact_search | Deep learning and its applications for vehicle networks |
| title_full | Deep learning and its applications for vehicle networks edited by Fei Hu and Iftikhar Rasheed |
| title_fullStr | Deep learning and its applications for vehicle networks edited by Fei Hu and Iftikhar Rasheed |
| title_full_unstemmed | Deep learning and its applications for vehicle networks edited by Fei Hu and Iftikhar Rasheed |
| title_short | Deep learning and its applications for vehicle networks |
| title_sort | deep learning and its applications for vehicle networks |
| topic | Vehicular ad hoc networks (Computer networks) Deep learning (Machine learning) Réseaux ad hoc de véhicules Apprentissage profond COMPUTERS / Artificial Intelligence COMPUTERS / Networking / General |
| topic_facet | Vehicular ad hoc networks (Computer networks) Deep learning (Machine learning) Réseaux ad hoc de véhicules Apprentissage profond COMPUTERS / Artificial Intelligence COMPUTERS / Networking / General |
| work_keys_str_mv | AT hufei deeplearninganditsapplicationsforvehiclenetworks AT rasheediftikhar deeplearninganditsapplicationsforvehiclenetworks |