A Machine Learning-Assisted Framework for Determination of Performance Degradation Causes and Selection of Channel Switching Strategy in Vehicular Networks [Technology Transfer Activities]

Huang, Chin-tser; Comert, Gurcan; Abuhdima, Esmail; Pisu, Pierluigi; Liu, Jian; Nazeri, Amirhossein; Benedict College; Clemson University; University of South Carolina; Center for Connected Multimodal Mobility, Clemson University

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ROSA P serves as an archival repository of USDOT-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by USDOT or funded partners. As a repository, ROSA P retains documents in their original published format to ensure public access to scientific information.

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A Machine Learning-Assisted Framework for Determination of Performance Degradation Causes and Selection of Channel Switching Strategy in Vehicular Networks [Technology Transfer Activities]

2023-02-01
Datasets Available
By Huang, Chin-tser ; Comert, Gurcan ; Abuhdima, Esmail ; ...

English

Details Datasets Related Documents You May Also Like

Details:

Alternative Title:

Technology Transfer Activities
Creators:

Huang, Chin-tser ; Comert, Gurcan ; Abuhdima, Esmail ; Pisu, Pierluigi ; Liu, Jian ; ... More +

Huang, Chin-tser ; Comert, Gurcan ; Abuhdima, Esmail ; Pisu, Pierluigi ; Liu, Jian ; Nazeri, Amirhossein Less -
Corporate Creators:

Benedict College ; Clemson University ; University of South Carolina ; Center for Connected Multimodal Mobility, Clemson University

Benedict College ; Clemson University ; University of South Carolina ; Center for Connected Multimodal Mobility, Clemson University Less -
Corporate Contributors:

United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program

United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program Less -
Subject/TRT Terms:

[+]

Artificial Intelligence Cellular Networks Communication Connected Vehicles Connectivity Machine Learning Networks
Resource Type:

Brief
Geographical Coverage:

United States
Edition:

Technology Transfer
Corporate Publisher:

Center for Connected Multimodal Mobility, Clemson University
Abstract:

We expect this study to add knowledge to the transportation community and the public. Our research suggests that LSTM has the potential to forecast communication degradation in future autonomous vehicle designs. Additionally, our simulation results indicate that the amplitude of linearly polarized electric fields is less affected by dust and sand compared to circularly polarized fields.
Format:

PDF
Funding:

NSF Grant Nos. 1719501 ; 1954532 ; 2131080

NSF Grant Nos. 1719501 ; 1954532 ; 2131080 Less -
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha-512:9087d329c2e5c7feb22c9715b44a6d9ef8c5068b9a757f7ad148a35f421271f588f37e644a340cb15d419f2fe169677b1ff940ecbd390e952ef9cd995c64fbe4
Download URL:

https://rosap.ntl.bts.gov/view/dot/74013/dot_74013_DS1.pdf
File Type:

[PDF-287.79 KB]