Artificial Intelligence and Machine Learning as key enablers for V2X communications: A comprehensive survey

Christopoulou, Maria; Barmpounakis, Sokratis; Koumaras, Harilaos; Kaloxylos, Alexandros

doi:10.1016/j.vehcom.2022.100569

Cited by 8 publications

(5 citation statements)

References 101 publications

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“…В контексті розвитку автономного транспорту використання Vehicle-to-Everything (V2X) комунікації відіграє важливе значення для підтримки безпеки, ефективності та надійності дорожнього руху. Проте існує проблема неефективності, що пояснюється недостатньою оптимізацією V2X комунікації, що в свою чергу може призвести до затримок та непередбачуваних ситуацій на автодорожньому шляху й може становити загрозу безпеці для всіх можливих учасників руху [2].…”

Section: використання машинного навчання для оптимізації V2x комуніка...unclassified

“…Рис. 1 Приклади застосування V2X комунікації Джерело: сформовано автором на основі [1][2][3] Автомобіль-автомобіль комунікація (V2V), метою якої є запобігання аваріям та покращення транспортного потоку. V2V відноситься до прямого зв'язку між транспортними засобами, що дозволяє їм обмінюватися відповідною інформацією, наприклад, місцезнаходження та швидкість, а також можливі небезпечні ситуації, які можуть бути виявлені та ідентифіковані за допомогою датчиків транспортного засобу.…”

Section: використання машинного навчання для оптимізації V2x комуніка...unclassified

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Використання Машинного Навчання Для Оптимізації V2x Комунікації В Контексті Розвитку Автономного Транспорту

Лещишин,

Шпак

2024

НТС

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Leshchyshyn M.M, Shpak O.I. Using machine learning to optimize V2X communication in the context of the development of autonomous transport.This article aims to consider the use of machine learning to optimize V2X communication in order to promote the development of autonomous transport. Describing the current challenges and limitations in using V2X communication, the article describes how machine learning can solve these problems and improve the efficiency, safety and reliability of autonomous transport systems.The article, based on the latest research, describes the current state of V2X communication technologies and the challenges they face. Based on this, the possibilities of using machine learning to optimize data transmission, traffic management and forecasting will be considered, which can improve the functionality of autonomous transport systems.The aim of this study is to explore questions on the use of V2X communication for the development of autonomous vehicles in combination with deep learning neural networks based on a combined ATZ model. To achieve this, the application of V2X communication and basic communication technologies such

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Section: використання машинного навчання для оптимізації V2x комуніка...unclassified

Використання Машинного Навчання Для Оптимізації V2x Комунікації В Контексті Розвитку Автономного Транспорту

Лещишин,

Шпак

2024

НТС

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“…Certain network objective factors influence network latency and teleoperation [ 18 , 22 , 29 , 31 , 32 , 33 , 34 , 41 , 60 , 65 , 89 , 90 , 91 , 92 ]. Latency is directly impacted by distance, vehicle density, handover between cells, speed, mobility, and packet loss.…”

Section: Impact Of Network Latency On Teleoperationmentioning

confidence: 99%

Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies

Kamtam,

Lu,

Bouali

et al. 2024

Sensors

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With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and operational efficiency. However, teleoperation faces substantial challenges, with network latency being a critical factor influencing its performance. This survey paper explores the impact of network latency along with state-of-the-art mitigation/compensation approaches. It examines cascading effects on teleoperation communication links (i.e., uplink and downlink) and how delays in data transmission affect the real-time perception and decision-making of operators. By elucidating the challenges and available mitigation strategies, the paper offers valuable insights for researchers, engineers, and practitioners working towards the seamless integration of teleoperation in the evolving landscape of CAVs.

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“…The challenges arise from integration of FL and blockchain are pointed out in the survey with indication of future research direction. In [26], the survey presents a compilation of network-controlled functions that have been optimized through data-driven approaches in vehicular environments. The research related to integration of AI/ML and V2X communications in the areas such as handover and resource management or user association, caching, routing, beam-forming optimization and QoS prediction are extensively reviewed.…”

Section: Of 29mentioning

confidence: 99%

A Survey on Artificial Intelligence based Internet of Vehicles utilizing Unmanned Aerial Vehicles

Ali Shah,

Fernando,

Kashef

2023

Preprint

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The vehicular ad hoc network (VANET) when augmented with internet is called internet of vehicles (IoV). In the IoV, vehicles have on board units (OBUs), use communication technologies and customized software to communicate with infrastructure around them. The vehicles can have permanent connectivity and global network awareness based on cloud computing. IoV technologies are still being assessed to ensure that the network is protected from attacks, supports a dynamic vehicular mobility scenarios and meets the low latency communication requirements between the vehicles and the surrounding infrastructure. To meet these strict Quality of Service (QoS) demands, Unmanned Aerial Vehicles (UAVs) play a vital role in IoV ecosystems. An UAV can serve as a mobile roadside unit (mRSU) to transmit data between vehicles, stationery RSUs and other UAVs. The UAVs have better line of sight (LOS) connections which result in better channel states and lower path loss than fixed RSUs. Although the inclusion of UAVs in vehicular networks has improved the system performance by providing better resource management and routing solutions, the traditional optimization techniques with their limitations are often not applicable. Therefore, in recent years, research community is integrating Artificial Intelligence (AI) and Machine Learning (ML) into UAV based IoV ecosystem to better manage resource allocation, routing and mobility management issues to optimize the overall network performance. In this survey paper we review the existing research done in AI/ML based UAV-IoV networks and communications with focus on resource management and routing from year 2019 to 2023. We have studied different AI techniques, their training attributes and architectures in various research works. The limitations of AI-based approaches in terms of required computational resources, availability of real world data and AI models’ complexity for the UAV-IoV environment are also discussed. Finally, the future directions for UAV-IoV research opportunities that can leverage the full potential of AI are presented.

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Artificial Intelligence and Machine Learning as key enablers for V2X communications: A comprehensive survey

Cited by 8 publications

References 101 publications

Використання Машинного Навчання Для Оптимізації V2x Комунікації В Контексті Розвитку Автономного Транспорту

Використання Машинного Навчання Для Оптимізації V2x Комунікації В Контексті Розвитку Автономного Транспорту

Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies

A Survey on Artificial Intelligence based Internet of Vehicles utilizing Unmanned Aerial Vehicles

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