دانلود مقاله هوش مصنوعی و یادگیری ماشین به عنوان توانمندسازهای کلیدی برای ارتباطات V2X
چکیده
1. مقدمه
2. مروری بر V2X
3. مروری بر یادگیری ماشین
4. راه حل های هوش مصنوعی و ML در ارتباطات V2X
5. بحث و گفتگو و مسائل باز
6. جهت تحقیق
7. نتیجه گیری
اعلامیه منافع رقابتی
در دسترس بودن داده ها
منابع
Abstract
1. Introduction
2. V2X overview
3. Machine Learning overview
4. AI and ML solutions in V2X communications
5. Discussion and open issues
6. Research directions
7. Conclusion
Declaration of Competing Interest
Data availability
References
چکیده
صنعت خودرو در حال دستخوش یک تحول دیجیتالی عمیق برای ایجاد وسایل نقلیه خودران است. ارتباطات وسیله نقلیه به همه چیز (V2X) امکان ارائه موارد استفاده از حمل و نقل را برای مدیریت ترافیک جاده ای و ایمنی فراهم می کند. در عین حال، در طول دهه گذشته، هوش مصنوعی (AI) و یادگیری ماشین (ML) به دلیل عملکرد فوق‌العاده خود در حوزه‌های مختلف، از جمله پردازش زبان طبیعی و بینایی رایانه، در کانون توجه قرار گرفته‌اند. با در نظر گرفتن تلاش‌های استانداردسازی فعلی، به منظور ترکیب AI و ML به عنوان زیرسیستم‌های یکپارچه فراتر از شبکه‌های 5G و 6G، این فناوری‌ها برای بهینه‌سازی عملکردهای شبکه کاربر، کنترل و مدیریت و همچنین برای پشتیبانی از ایمنی جاده و حتی برنامه‌های سرگرمی بسیار امیدوارکننده در نظر گرفته می‌شوند. . این نظرسنجی به طور سیستماتیک تحقیقات موجود در تقاطع ارتباطات AI/ML و V2X را با تمرکز بر مدیریت تحویل، ذخیره‌سازی فعال، تخصیص منابع فیزیکی و محاسباتی، بهینه‌سازی انتخاب پرتو، مسیریابی بسته‌ها و پیش‌بینی QoS در محیط‌های خودرو بررسی می‌کند. ما تکنیک‌های زیربنایی AI/ML، ویژگی‌های آموزشی، معماری آن‌ها را استخراج می‌کنیم و چندین جنبه را در مورد پیچیدگی‌های محیط‌های خودرو و ML مورد بحث قرار می‌دهیم. این جنبه‌ها شامل پیچیدگی زمانی الگوریتم‌ها، کیفیت ردیابی وسایل نقلیه در دنیای واقعی، مناسب بودن تکنیک‌های AI/ML در ارتباط با عملیات شبکه تعیین‌شده و مورد استفاده زیربنایی خودرو، و همچنین سرعت و دقت موقعیت‌یابی الزامات برای ایجاد موارد بیشتر است. داده های مصنوعی واقعی و معرف
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
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.