Universal Learning Approach of an Intelligent Algorithm for Non-GNSS Assisted Beamsteering in V2I Systems
In intelligent transportation systems, an important task is to provide a highly efficient communication channel between vehicles and other infrastructure objects that meets energy efficiency requirements and involves low time delays. The paper presents a method for generating synthetic data of the “...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/14/2/86 |
| _version_ | 1797620365673889792 |
|---|---|
| author | Ekaterina Lopukhova Ansaf Abdulnagimov Grigory Voronkov Ruslan Kutluyarov Elizaveta Grakhova |
| author_facet | Ekaterina Lopukhova Ansaf Abdulnagimov Grigory Voronkov Ruslan Kutluyarov Elizaveta Grakhova |
| author_sort | Ekaterina Lopukhova |
| collection | DOAJ |
| description | In intelligent transportation systems, an important task is to provide a highly efficient communication channel between vehicles and other infrastructure objects that meets energy efficiency requirements and involves low time delays. The paper presents a method for generating synthetic data of the “vehicle-to-infrastructure” system, capable of simulating many scenarios of traffic situations to increase the generalizing ability of an intelligent beamsteering algorithm. The beamsteering algorithm is based on gradient boosting and is designed to connect and track vehicles with minimal delays without relying on GNSS coordinates. The predictors for the applied machine learning algorithm were: the relief, vehicle type, direction of movement and speed, timestamps, and the received signal power level. The generated dataset included the traffic model based on the Lighthill–Whitham–Richards macroscopic model and SUMO software package simulations. Simulation results showed 94% accuracy in correctly identified positions for the main lobe according to vehicle behavior. |
| first_indexed | 2024-03-11T08:40:15Z |
| format | Article |
| id | doaj.art-e842a5137da0425c87d13b0cd08b29fb |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T08:40:15Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-e842a5137da0425c87d13b0cd08b29fb2023-11-16T21:12:08ZengMDPI AGInformation2078-24892023-02-011428610.3390/info14020086Universal Learning Approach of an Intelligent Algorithm for Non-GNSS Assisted Beamsteering in V2I SystemsEkaterina Lopukhova0Ansaf Abdulnagimov1Grigory Voronkov2Ruslan Kutluyarov3Elizaveta Grakhova4School of Photonics Engineering and Research Advances (SPhERA), Ufa University of Science and Technology, 32 Z. Validi Street, Ufa 450076, RussiaDepartment of Automated Control Systems, Ufa University of Science and Technology, Z. Validi Street, Ufa 450076, RussiaSchool of Photonics Engineering and Research Advances (SPhERA), Ufa University of Science and Technology, 32 Z. Validi Street, Ufa 450076, RussiaSchool of Photonics Engineering and Research Advances (SPhERA), Ufa University of Science and Technology, 32 Z. Validi Street, Ufa 450076, RussiaSchool of Photonics Engineering and Research Advances (SPhERA), Ufa University of Science and Technology, 32 Z. Validi Street, Ufa 450076, RussiaIn intelligent transportation systems, an important task is to provide a highly efficient communication channel between vehicles and other infrastructure objects that meets energy efficiency requirements and involves low time delays. The paper presents a method for generating synthetic data of the “vehicle-to-infrastructure” system, capable of simulating many scenarios of traffic situations to increase the generalizing ability of an intelligent beamsteering algorithm. The beamsteering algorithm is based on gradient boosting and is designed to connect and track vehicles with minimal delays without relying on GNSS coordinates. The predictors for the applied machine learning algorithm were: the relief, vehicle type, direction of movement and speed, timestamps, and the received signal power level. The generated dataset included the traffic model based on the Lighthill–Whitham–Richards macroscopic model and SUMO software package simulations. Simulation results showed 94% accuracy in correctly identified positions for the main lobe according to vehicle behavior.https://www.mdpi.com/2078-2489/14/2/86V2Ibeamsteeringphased antenna arraydigital elevation modelmachine learninggradient boosting |
| spellingShingle | Ekaterina Lopukhova Ansaf Abdulnagimov Grigory Voronkov Ruslan Kutluyarov Elizaveta Grakhova Universal Learning Approach of an Intelligent Algorithm for Non-GNSS Assisted Beamsteering in V2I Systems Information V2I beamsteering phased antenna array digital elevation model machine learning gradient boosting |
| title | Universal Learning Approach of an Intelligent Algorithm for Non-GNSS Assisted Beamsteering in V2I Systems |
| title_full | Universal Learning Approach of an Intelligent Algorithm for Non-GNSS Assisted Beamsteering in V2I Systems |
| title_fullStr | Universal Learning Approach of an Intelligent Algorithm for Non-GNSS Assisted Beamsteering in V2I Systems |
| title_full_unstemmed | Universal Learning Approach of an Intelligent Algorithm for Non-GNSS Assisted Beamsteering in V2I Systems |
| title_short | Universal Learning Approach of an Intelligent Algorithm for Non-GNSS Assisted Beamsteering in V2I Systems |
| title_sort | universal learning approach of an intelligent algorithm for non gnss assisted beamsteering in v2i systems |
| topic | V2I beamsteering phased antenna array digital elevation model machine learning gradient boosting |
| url | https://www.mdpi.com/2078-2489/14/2/86 |
| work_keys_str_mv | AT ekaterinalopukhova universallearningapproachofanintelligentalgorithmfornongnssassistedbeamsteeringinv2isystems AT ansafabdulnagimov universallearningapproachofanintelligentalgorithmfornongnssassistedbeamsteeringinv2isystems AT grigoryvoronkov universallearningapproachofanintelligentalgorithmfornongnssassistedbeamsteeringinv2isystems AT ruslankutluyarov universallearningapproachofanintelligentalgorithmfornongnssassistedbeamsteeringinv2isystems AT elizavetagrakhova universallearningapproachofanintelligentalgorithmfornongnssassistedbeamsteeringinv2isystems |