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Cellular vehicle-to-everything (C-V2X) is one of the enabling vehicular communication technologies gaining momentum from the standardization bodies, industry, and researchers aiming to realize fully autonomous driving and intelligent transportation systems. The 3rd Generation Partnership Project (3GPP) standardization body has actively been develop...
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... In [9], it is shown that there are efforts by 3GPP to standardize the relation between the C-V2X communica-tions and the autonomous driving application. The study aims to simplify the 3GPP documentation and its role in achieving Level-5 autonomous driving. ...
Vehicle-to-Everything (V2X) communications are constrained by both 3GPP technical specifications, as well as
by country-specific spectrum regulations. The world’s largest economies, such as the USA, EU and China have
self-imposed regulations regarding the specific bandwidths and central spectrum frequencies where both safety
and non-safety related V2X communication services are allowed to occur (always aligned with the aforemen
tioned 3GPP technical specifications). Although the channels used for safety, non-safety, and control packets
differ, what all of these countries have in common is that V2X shall occur mostly on New Radio Unlicensed (NRU) spectrum, i.e., by means of private networks. A specific bandwidth in the public spectrum is also available, but
since public spectrum is purchased through auctions, it is quite common the case that one particular operator will
own the entirety of this spectrum, leading to a monopoly in V2X operations. Besides, this public spectrum is quite
limited in bandwidth. This of course includes all of the Intelligent Transportation Systems (ITS) services, even
location-based services, such as the ones that require the usage of positioning technologies, like autonomous
vehicles, that require said services in order to support complex maneuvers and cooperative driving. Global
Navigation Satellite Systems (GNSS) such as GPS or Galileo, currently already offer high-accuracy location to
vehicles. However, this form of stand-alone position estimation of the vehicle has several drawbacks, as the
information is constrained to the individual vehicle and not shared with others in a secure manner. This ex
change of position information between other entities (not only vehicles, but also other infrastructure nodes) is
vital for actions such as cooperative maneuvers and to counter loss of satellite sight (e.g., when entering a
tunnel). Taking these facts into consideration, it is therefore expected that in the mid to long-term, municipalities
and highways will possess dedicated private 5G networks for V2X operations with the aim of offering a plethora
of vehicular services, including positioning ones. Since the existent scientific literature lacks an integrated
analysis of precise positioning services for ITS in 5G private networks, we propose in this paper, to provide a
comprehensive review connecting these diverse elements, examining the role of 5G private networks in trans
mitting positioning messages in V2X scenarios. Additionally, the paper shall explore hybrid positioning systems
that combine 5G and GNSS technologies, illustrating their potential to enhance V2X communications. This study
offers a roadmap for the evolution of ITS and V2X communications by showcasing current trends and identifying
areas for further research.
... Current communication protocols, such as Dedicated Short-Range Communication (DSRC), 4G LTE, and the more recent 5G technology, are commonly used in connected vehicle (CV) applications. These protocols enable real-time data sharing between vehicles and other transportation agents, allowing for safer and more efficient traffic management [13][14][15][16][17][18]. ...
Every time the proper functioning of the vehicles must be guaranteed, as well as safety and efficiency. To achieve this, some expensive solutions are used, with few connectivity options and that fail to meet consumer demand. This paper presents a low-cost hardware system for the design of a real-time communication protocol between the electronic control unit (ECU) of a vehicle and a remote server based in a embedded system. A dual tone multi-frequency (DTMF) approach is implemented, so error codes (DTCs) are always available on a unit equipped with this system. The vehicle-to-infrastructure (V2I) communication protocol through voice channels is provided by cellular technology infrastructure, in which primary information is shared to monitor vehicles. With real-time data transmission, communication is established through a voice phone call between the vehicle’s ECU and the destination server, communicating the DTC codes. The system shows that the communication protocol has an effectiveness of 78.23%, which means that with the use of 2G technology, which is active and operating in many regions, it allows the information with the data to be received by the receiving user. Through this implemented system, it is ensured that if a vehicle suffers an accident or stops due to a mechanical failure in a region where there is no cellular technology coverage, information or a message can be sent so that through communication the rescue can be carried out using an cellular technology coverage.
... Cellular networks are increasingly being used to link V2X devices because of their high data rates, low latency, regulated quality of service (QoS) [55], and dependability, which allow for greater coverage capacity and worldwide deployment [56]. To be more precise, the 3GPP standards organization specifies V2X services in the LTE network (releases 14) and improved V2X for 5G new radio in future version 15 and 16 [57]. The following is a description of critical communication standards for vehicle networks. ...
Vehicle-to-everything (V2X) communication is an emerging technology that facilitates communication among vehicles and numerous environmental entities. However, it encounters certain challenges throughout the handover process. This study analyses the challenges and complexity of managing handover, specifically in maintaining uninterrupted connection and meeting the service criteria outlined in the 3GPP 5G new radio (NR) standard. Various applications of V2X technology that require handover management are explored, such as vehicle safety and traffic management, enhanced driver assistance, and autonomous driving. Furthermore, this paper illuminates the most recent developments in V2X communication, highlighting the significance of efficient handover management, resolving technical issues, based on the full potential of the use of V2X apps that contribute to the establishment of a transportation ecosystem that is characterized by enhanced safety, increased intelligence, and improved connectivity. This paper can be used as a starting point for thinking about how to improve C-V2X communication.
... Local data are used to train local models and, in turn, local models are used to update the global model. This aggregated global model is returned to the local models for further training and this procedure is repeated until the global model converges and works to further maintain this convergence, i.e., a common operational picture at all levels [30][31][32][33][34][35][36][37][38]. Large volumes of IoMT data can be harnessed without sharing, complex dy- Local data are used to train local models and, in turn, local models are used to update the global model. ...
... Large volumes of IoMT data can be harnessed without sharing, complex dy- Local data are used to train local models and, in turn, local models are used to update the global model. This aggregated global model is returned to the local models for further training and this procedure is repeated until the global model converges and works to further maintain this convergence, i.e., a common operational picture at all levels [30][31][32][33][34][35][36][37][38]. Large volumes of IoMT data can be harnessed without sharing, complex dynamics and complex data-sharing agreements. ...
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Potential applications of the work include novel ML-based systems for sustainable smart cities and smart territory control.
Abstract
The rapid development of modern information technology (IT), power supply, communication and traffic information systems and so on is resulting in progress in the area of distributed and energy-efficient (if possible, powered by renewable energy sources) smart grid components securely connected to entire smart city management systems. This enables a wide range of applications such as distributed energy management, system health forecasting and cybersecurity based on huge volumes of data that automate and improve the performance of the smart grid, but also require analysis, inference and prediction using artificial intelligence. Data management strategies, but also the sharing of data by consumers, institutions, organisations and industries, can be supported by edge clouds, thus protecting privacy and improving performance. This article presents and develops the authors’ own concept in this area, which is planned for research in the coming years. The paper aims to develop and initially test a conceptual framework that takes into account the aspects discussed above, emphasising the practical aspects and use cases of the Social Internet of Things (SIoT) and artificial intelligence (AI) in the everyday lives of smart sustainable city (SSC) residents. We present an approach consisting of seven algorithms for the integration of large data sets for machine learning processing to be applied in optimisation in the context of smart cities.
... Existing autonomous driving technologies primarily focus on enhancing the intelligence of individual vehicles [6]. While deep learning technologies have contributed to the designing of more efficient resource allocation algorithms for autonomous driving systems with multiple vehicles and roadside base stations, the advent of wireless communication technologies such as 5G and Cellular Vehicle-to-Everything (C-V2X) has ushered in novel prospects for autonomous driving satisfying the need for reliable and fast down-link sensory data transmission from roadside base stations with wide-range coverage [7]. These technologies facilitate the reliable real-time exchange of perception information among the mix of vehicles and roadside base stations, thereby augmenting the distant perceptual capabilities of each vehicle and enhancing the safety of autonomous-driving vehicles [6,8]. ...
The proliferation of wireless technologies, particularly the advent of 5G networks, has ushered in transformative possibilities for enhancing vehicular communication systems, particularly in the context of autonomous driving. Leveraging sensory data and mapping information downloaded from base stations using I2V links, autonomous vehicles in these networks present the promise of enabling distant perceptual abilities essential to completing various tasks in a dynamic environment. However, the efficient down-link transmission of vehicular network data via base stations, often relying on spectrum sharing, presents a multifaceted challenge. This paper addresses the intricacies of spectrum allocation in vehicular networks, aiming to resolve the thorny issues of cross-station interference and coupling while adapting to the dynamic and evolving characteristics of the vehicular environment. A novel approach is suggested involving the utilization of a multi-agent option-critic reinforcement learning algorithm. This algorithm serves a dual purpose: firstly, it learns the most efficient way to allocate spectrum resources optimally. Secondly, it adapts to the ever-changing dynamics of the environment by learning various policy options tailored to different situations. Moreover, it identifies the conditions under which a switch between these policy options is warranted as the situation evolves. The proposed algorithm is structured in two layers, with the upper layer consisting of policy options that are shared across all agents, and the lower layer comprising intra-option policies executed in a distributed manner. Through experimentation, we showcase the superior spectrum efficiency and communication quality achieved by our approach. Specifically, our approach outperforms the baseline methods in terms of training average reward convergence stability and the transmission success rate. Control-variable experiments also reflect the better adaptability of the proposed method as the environmental conditions change, underscoring the significant potential of the proposed method in aiding successful down-link transmissions in vehicular networks.
... These advanced scheduling techniques are crucial to effectively manage the limited frequency spectrum while meeting the unique requirements of V2X communications. An overview of V2X communications and the 3rd Generation Partnership Project (3GPP) was presented in [4]. This study analyzed 3GPP's contributions to support advanced C-V2X applications, focusing on their evolution of standards and underlying concepts. ...
The enormous increase in mobile data traffic and the heterogeneity and stringent Quality of Service (QoS) requirements of different applications have placed a substantial strain on the underlying network infrastructure and represent a challenge for Cellular Vehicle-to-Everything (Cellular V2X). V2X communication is a key enabler for the realization of smart and connected transportation systems, offering a wide range of applications, such as enhanced road safety, traffic management, and autonomous driving. In this context, the best way to provide great flexibility and address both the present and future QoS concerns is to use intelligent Radio Resource Management (RRM) in general and creative packet scheduling in particular. The diverse QoS requirements of multiple application classes under dynamic network conditions present substantial challenges for conventional scheduling algorithms given the increasing demand for bandwidth-hungry applications. This study proposes a scheduling system for V2X communications based on traffic prioritization that manages QoS provisioning for different types of traffic considering channel quality, remaining payload, and delay. Simulation results demonstrate the highly promising performance of the proposed New Scheduling V2X Communications (NSVC) algorithm that leads to significantly lower latencies, as the average delay scheme did not exceed 0.001 ms for 100 users.
... The 3rd Generation Partnership Project (3GPP) [41] standardized C-V2X, encompassing LTE-V2X and 5G-V2X (NR) technologies. This standardization process involves progressive enhancements as can be seen in Figure 4. C-V2X also defines a PC5 interface facilitating direct V2X communication via the sidelink, as well as a Uu interface enabling communication between the terminal and base station through the uplink/downlink [42]. With its extensive network capacity and wide coverage, C-V2X is well-suited for supporting collaborative perception in the internet of vehicles (IoV) environment, enhancing data transmission reliability, reducing transmission delays, and minimizing frequent horizontal switching within the network. ...
... Standardization and evolution of C-V2X[42]. ...
Environment perception plays a crucial role in enabling collaborative driving automation, which is considered to be the ground-breaking solution to tackling the safety, mobility, and sustainability challenges of contemporary transportation systems. Despite the fact that computer vision for object perception is undergoing an extraordinary evolution, single-vehicle systems’ constrained receptive fields and inherent physical occlusion make it difficult for state-of-the-art perception techniques to cope with complex real-world traffic settings. Collaborative perception (CP) based on various geographically separated perception nodes was developed to break the perception bottleneck for driving automation. CP leverages vehicle-to-vehicle and vehicle-to-infrastructure communication to enable vehicles and infrastructure to combine and share information to comprehend the surrounding environment beyond the line of sight and field of view to enhance perception accuracy, lower latency, and remove perception blind spots. In this article, we highlight the need for an evolved version of the collaborative perception that should address the challenges hindering the realization of level 5 AD use cases by comprehensively studying the transition from classical perception to collaborative perception. In particular, we discuss and review perception creation at two different levels: vehicle and infrastructure. Furthermore, we also study the communication technologies and three different collaborative perception message-sharing models, their comparison analyzing the trade-off between the accuracy of the transmitted data and the communication bandwidth used for data transmission, and the challenges therein. Finally, we discuss a range of crucial challenges and future directions of collaborative perception that need to be addressed before a higher level of autonomy hits the roads.
... It is used for V2V, V2I, and V2P communications, the exchange of information being carried out with the help of sidelink (SL) transmission. Within PC5, C-V2X applications operate in the 5.9 GHz ITS band for short-range applications, on a distance of less than 1 km [115]. ...
A suitable control architecture for connected vehicle platoons may be seen as a promising solution for today’s traffic problems, by improving road safety and traffic flow, reducing emissions and fuel consumption, and increasing driver comfort. This paper provides a comprehensive overview concerning the defining levels of a general control architecture for connected vehicle platoons, intending to illustrate the options available in terms of sensor technologies, in-vehicle networks, vehicular communication, and control solutions. Moreover, starting from the proposed control architecture, a solution that implements a Cooperative Adaptive Cruise Control (CACC) functionality for a vehicle platoon is designed. Also, two control algorithms based on the distributed model-based predictive control (DMPC) strategy and the feedback gain matrix method for the control level of the CACC functionality are proposed. The designed architecture was tested in a simulation scenario, and the obtained results show the control performances achieved using the proposed solutions suitable for the longitudinal dynamics of vehicle platoons.
... C-V2X encapsulates all Third Generation Partnership Project (3GPP) V2X technologies, and it has been introduced in various standards [3]. The 3GPP Technical Report [4] solutions do not consider the diversity of autonomous driving services with different data traffic characteristics and requirements. ...
Automated driving requires the support of critical communication services with strict performance requirements. Existing fifth-generation (5G) schedulers residing at the base stations are not optimized to differentiate between critical and non-critical automated driving applications. Thus, when the traffic load increases, there is a significant decrease in their performance. Our paper introduces SOVANET, a beyond 5G scheduler that considers the Radio Access Network (RAN) load, as well as the requirements of critical, automated driving applications and optimizes the allocation of resources to them compared to non-critical services. The proposed scheduler is evaluated through extensive simulations and compared to the typical Proportional Fair scheduler. Results show that SOVANET’s performance for critical services presents clear benefits.
... With the help of V2N, it is now possible for a human operator to drive a car remotely with the evolution of cloud-based applications. Remote driving may be leveraged in a variety of situations, including 1. Backup solution for the Autonomous vehicle car 2. Enable the fleet owners to remotely control the vehicle 3. Enable cloud driven public transportation and private shuttles [75,76]. The potential network requirements to support this use case would be; ...
... In addition to these features, the 5G system architecture for V2X communications also includes the support for network slicing, which allows network operators to create customized network slices for specific V2X use cases [115,116]. This enables operators to optimize network resources and provide the required level of performance and reliability for each use case [117,76]. Other technical details in 3GPP/Release 16 related to V2X communications include the support for edge computing, which enables low-latency processing and analysis of V2X data at the network edge, and the use of security mechanisms such as certificate-based authentication and end-to-end encryption to ensure the confidentiality and integrity of V2X messages. ...
... If it does, the SMF sends the AQPs to the NG-RAN. This feature helps the NG-RAN to be more adaptable to the V2X service and provides more QoS options for delivering the service [61,133,134,76,135]. ...
