مقاله انگلیسی رایگان در مورد هوش مصنوعی و یادگیری ماشینی به عنوان عوامل کلیدی برای ارتباطات V2X – الزویر 2023

Abstract

     The automotive industry is undergoing a profound digital transformation to create autonomous vehicles. Vehicle-to-Everything (V2X) communications enable the provisioning of transportation use cases for road traffic and safety management. At the same time, during the past decade, Artificial Intelligence (AI) and Machine Learning (ML) have been in the spotlight because of their outstanding performance in various domains, including natural language processing, and computer vision. Considering also current standardization efforts, towards incorporating AI and ML as integral sub-systems of beyond 5G and 6G networks, these technologies are considered very promising to optimize user, control, and management network functions, but also to support road safety and even entertainment applications. This survey systematically reviews existing research at the intersection of AI/ML and V2X communications, focusing on handover management, proactive caching, physical and computation resources allocation, beam selection optimization, packet routing, and QoS prediction in vehicular environments. We extract the underlying AI/ML techniques, the training features, their architecture and discuss several aspects regarding the intricacies of vehicular environments and ML. These aspects include time complexity of the algorithms, quality of real-world vehicle traces, suitability of AI/ML techniques in relevance to the designated network operation and the underlying automotive use case, as well as velocity and positioning accuracy requirements towards the creation of more realistic and representative synthetic data.

Introduction

     During the past years, car manufacturers have introduced driver assistance systems to their models, coupled with onboard intelligence, leading to a higher perception of their surroundings. This enables the possibility to achieve different levels of autonomous driving. Autonomous driving is considered critical in improving car safety, eliminating accidents due to human error, reducing traffic congestion, and improving passenger comfort. The Society of Automotive Engineers (SAE) has defined six driving automation levels, ranging from no automation to full automation [1]. Communications among vehicles, infrastructure and road users, collectively defined as Vehicle-to-Everything (V2X), are essential in realizing safety and non-safety-related applications, such as autonomous driving, car platooning, information sharing among vehicles and high data-rate infotainment.

     V2X may refer to Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Network (V2N) or Vehicle-to-Pedestrian (V2P) communication. V2P refers to the communication among vehicles and pedestrians, cyclists or motorized two-wheeler operators, collectively called Vulnerable Road Users (VRUs) [2]. There are two leading technologies in V2X: i) the Cellular-V2X (C-V2X) based on cellular 4G/LTE [3] and 5G networks [4] and ii) the Dedicated Short-Range Communication (DSRC) [5] based on IEEE 802.11p [6]. These solutions will complement the sensor/camera/radar information and intelligently connect the car to its surroundings and the network.

Conclusion

     This survey presented recent advances in AI/ML applications in V2X communications. We classified the related literature in handover management, beam allocation, caching, radio network allocation, computations resources management, routing and QoS prediction considering only vehicular environments. For each category, we surveyed the ML technique, the training features, architecture, and optimization objectives, and extracted results and observations concerning time complexity, performance and suitability of learning techniques according to the designated problem

     g techniques according to the designated problem. Based on the surveyed publications, there is no “one size fits all” solution. Depending on the problem, different tasks require different formulations, including AI/ML algorithm selection, optimization objectives, architecture, and training features. Each family of AI/ML algorithms comes with their own advantages and disadvantages. The problem formulation must also consider the requirements of the underlying use case, which is largely affected by the training and response times of the selected AI/ML model. In addition, it is vital to explore approaches that reduce these times, while preserving the robustness of the AI/ML algorithm. AI/ML in V2X has already shown potential in optimizing network operations, but there are still open issues that need to be addressed due to the intricacies of both AI/ML and the highly dynamic vehicular networks. Based on the surveyed papers, vehicular networks, empowered by AI/ML and V2X communications as cooperative technologies, can be transformed into autonomous networks with self-configuration/optimization/healing capabilities.