Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach
Autonomous intelligent transportation systems consistently require effective and secure communication through vehicular networks, enabling autonomous vehicle communication. The reduction of traffic congestion, the alerting of approaching emergency vehicles, and assistance in low visibility traffic a...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/19/4023 |
| _version_ | 1797576015187279872 |
|---|---|
| author | Radwa Ahmed Osman |
| author_facet | Radwa Ahmed Osman |
| author_sort | Radwa Ahmed Osman |
| collection | DOAJ |
| description | Autonomous intelligent transportation systems consistently require effective and secure communication through vehicular networks, enabling autonomous vehicle communication. The reduction of traffic congestion, the alerting of approaching emergency vehicles, and assistance in low visibility traffic are all made possible by effective communication between autonomous vehicles and everything (AV2X). Therefore, a new adaptive AV2X model is proposed in this paper to improve the connectivity of vehicular networks. This proposed model is based on the optimization method and a distributed deep learning model. The presented approach optimizes the inter-vehicle location if required for ensuring effective communication between the autonomous vehicle (AV) and everything (X) using the Lagrange optimization algorithm. Furthermore, the system is evaluated in terms of energy efficiency and achievable data rate based on the optimal inter-vehicle position to show the significance of the proposed approach. To meet the stated goals, the ideal inter-vehicle position is predicted using a distributed deep learning model by learning from mathematically generated data and defined as a restricted optimization problem using the Lagrange optimization technique to improve communication between AV2X under various environmental conditions. To demonstrate the efficiency of the suggested model, the following characteristics are considered: vehicle dispersion, vehicle density, vehicle mobility, and speed. The simulation results show the significance of the proposed model in terms of energy efficiency and achievable data rate compared with other proposed models. |
| first_indexed | 2024-03-10T21:46:31Z |
| format | Article |
| id | doaj.art-4da81db612c34ae3afe0bafadd6ffae7 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:46:31Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-4da81db612c34ae3afe0bafadd6ffae72023-11-19T14:16:08ZengMDPI AGElectronics2079-92922023-09-011219402310.3390/electronics12194023Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning ApproachRadwa Ahmed Osman0Basic and Applied Science Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, EgyptAutonomous intelligent transportation systems consistently require effective and secure communication through vehicular networks, enabling autonomous vehicle communication. The reduction of traffic congestion, the alerting of approaching emergency vehicles, and assistance in low visibility traffic are all made possible by effective communication between autonomous vehicles and everything (AV2X). Therefore, a new adaptive AV2X model is proposed in this paper to improve the connectivity of vehicular networks. This proposed model is based on the optimization method and a distributed deep learning model. The presented approach optimizes the inter-vehicle location if required for ensuring effective communication between the autonomous vehicle (AV) and everything (X) using the Lagrange optimization algorithm. Furthermore, the system is evaluated in terms of energy efficiency and achievable data rate based on the optimal inter-vehicle position to show the significance of the proposed approach. To meet the stated goals, the ideal inter-vehicle position is predicted using a distributed deep learning model by learning from mathematically generated data and defined as a restricted optimization problem using the Lagrange optimization technique to improve communication between AV2X under various environmental conditions. To demonstrate the efficiency of the suggested model, the following characteristics are considered: vehicle dispersion, vehicle density, vehicle mobility, and speed. The simulation results show the significance of the proposed model in terms of energy efficiency and achievable data rate compared with other proposed models.https://www.mdpi.com/2079-9292/12/19/4023autonomous vehiclesvehicular communicationvehicle-to-everything (V2X)cooperative communicationenergy efficiencyachievable data rate |
| spellingShingle | Radwa Ahmed Osman Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach Electronics autonomous vehicles vehicular communication vehicle-to-everything (V2X) cooperative communication energy efficiency achievable data rate |
| title | Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach |
| title_full | Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach |
| title_fullStr | Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach |
| title_full_unstemmed | Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach |
| title_short | Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach |
| title_sort | optimizing autonomous vehicle communication through an adaptive vehicle to everything av2x model a distributed deep learning approach |
| topic | autonomous vehicles vehicular communication vehicle-to-everything (V2X) cooperative communication energy efficiency achievable data rate |
| url | https://www.mdpi.com/2079-9292/12/19/4023 |
| work_keys_str_mv | AT radwaahmedosman optimizingautonomousvehiclecommunicationthroughanadaptivevehicletoeverythingav2xmodeladistributeddeeplearningapproach |