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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. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy (BLE) is a prominent short-range wireless communication protocol widely extended for communications and sensor systems in consumer electronics and industrial applications, ranging from manufacturing to retail and healthcare. The BLE protocol provides four generic access profile (GAP) roles when it is used in its low-energy version, i.e., ver. 4 and beyond. GAP roles control connections and allow BLE devices to interoperate each other. They are defined by the Bluetooth special interest group (SIG) and are primarily oriented to connect peripherals wirelessly to smartphones, laptops, and desktops. Consequently, the existing GAP roles have characteristics that do not fit well with vehicular communications in cooperative intelligent transport systems (C-ITS), where low-latency communications in high-density environments with stringent security demands are required. This work addresses this gap by developing two new GAP roles, defined at the application layer to meet the specific requirements of vehicular communications, and by providing a service application programming interface (API) for developers of vehicle-to-everything (V2X) applications. We have named this new approach ITS-BLE. These GAP roles are intended to facilitate BLE-based solutions for real-world scenarios on roads, such as detecting road traffic signs or exchanging information at toll booths. We have developed a prototype able to work indistinctly as a unidirectional or bidirectional communication device, depending on the use case. To solve security risks in the exchange of personal data, BLE data packets, here called packet data units (PDU), are encrypted or signed to guarantee either privacy when sharing sensitive data or authenticity when avoiding spoofing, respectively. Measurements taken and their later evaluation demonstrated the feasibility of a V2X BLE network consisting of picocells with a radius of about 200 m. [ABSTRACT FROM AUTHOR]

In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration of these communication protocols within smart intersections, emphasizing their capabilities and synergies. DSRC, based on IEEE 802.11p, provides reliable short-range communication with data rates up to 27 Mbps and latencies below 50 ms, ideal for real-time safety applications. C-V2X leverages LTE and 5G networks, offering broader coverage up to 10 km and supporting data rates up to 100 Mbps, with latencies as low as 20 ms in direct communication mode (PC5). LoRa 2.4 GHz, known for its long-range (up to 15 km in rural areas, 1–2 km in urban settings) and low-power characteristics, offers data rates between 0.3 and 37.5 kbps, suitable for non-critical data exchange and infrastructure monitoring. The study evaluates the performance and interoperability of these technologies in urban environments, focusing on data latency, transmission reliability, and scalability. Experimental results from simulated and real-world scenarios show that DSRC maintains reliable communication within 1 km with minimal interference. C-V2X demonstrates superior scalability and coverage, maintaining robust communication over several kilometers in high-density urban settings. LoRa 2.4 GHz exhibits excellent penetration through urban obstacles, maintaining connectivity and efficient data transmission with packet error rates below 10%. [ABSTRACT FROM AUTHOR]

Summary: Vehicular connectivity is a significant key contribution in vehicular ad‐hoc networks (VANETs). The inherent high dynamicity of vehicles is a serious challenge that affects automotive connectivity and disrupts inter‐vehicle communications stability. Vehicle‐to‐everything (V2X) communication enables vehicles to connect to any other networks and facilitates the evolutionary trend of automotive innovations toward autonomous internet‐enabled intelligent vehicles. In this article, we provide a systematic literature review (SLR) for vehicular connectivity and V2X communications by investigating 58 research articles from 2018 to 2023 in VANETs. We propose six fundamental research questions (RQs) and a taxonomy to systematize the study of the baseline articles and answer the crucial RQs. This SLR can be helpful for researchers as a complete study focusing on V2X analysis, making it possible to achieve Technical Aspects and New challenges. [ABSTRACT FROM AUTHOR]

The rapid advancements in automotive technology and the emergence of next-generation networks such as 5G and 6G are laying the foundation for the internet of vehicles (IoV), a revolutionary concept to transform transportation systems. The convergence of artificial intelligence (AI) and connected vehicles IoV is driving a paradigm shift in the transportation sector, especially in the dynamic framework of 5G and future 6G networks. This survey paper provides a thorough survey of the evolving AI-based IoV security landscape. We explore key areas of 5G/6G networks, focusing on the complex interplay of machine learning (ML) and deep learning (DL) in enhancing vehicle-to-everything (V2X) security and connected vehicles. Addressing the unique challenges of 6G, this paper outlines future directions for improving security and highlights open research issues. This comprehensive survey, which aims to provide information and guidance to both researchers and practitioners, contributes to a detailed understanding of the security issues associated with connected vehicles in the emerging 6G era. [ABSTRACT FROM AUTHOR]

The idea of cooperative perception for navigation assistance was introduced about a decade ago with the aim to increase safety on dangerous areas like intersections. In this context, roadside infrastructure appeared very recently to provide a new point of view of the scene. In this paper, we propose to combine the Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) approaches in order to take advantage of the elevated points of view offered by the infrastructure and the in-scene points of view offered by the vehicles to build a semantic grid map of the moving elements in the scene. To create this map, we chose to use camera information and 2-Dimentional (2D) bounding boxes in order to minimize the impact on the network and ignored possible depth information as opposed to all state-of-the art methods. We propose a framework based on two fusion methods: one based on the Bayesian theory and the other on the Dempster-Shafer Theory (DST) to merge the information and chose a label for each cell of the semantic grid in order to assess the best fusion method. Finally, we evaluate our approach on a set of datasets that we generated from the CARLA simulator varying the proportion of Connected Vehicle (CV) and the traffic density. We also show the superiority of the method based on the DST with a gain on the mean intersection over union between the two methods of up to 23.35%. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic conditions and historical vehicle trajectory information, the source vehicle may not transfer its packet to the correct relay vehicles and, finally, to the destination. Thus, this kind of routing protocol fails to guarantee successful packet delivery. Using the greater network flexibility and scalability of the software-defined network (SDN) architecture, this study designs a two-phase integrated DQN and RF Packet Routing Framework (IDRF) that combines the deep Q-learning network (DQN) and random forest (RF) approaches. First, the IDRF offline phase corrects the vehicle's historical trajectory information using the vehicle trajectory continuity algorithm and trains the DQN model. Then, the IDRF real-time phase judges whether vehicles can meet each other and makes a real-time routing decision to select the most appropriate relay vehicle after adding real-time vehicles to the VANET. In this way, the IDRF can obtain the packet transfer path with the shortest end-to-end delay. Compared to two DQN-based approaches, i.e., TDRL-RP and VRDRT, and traditional VANET routing algorithms, the IDRF exhibits significant performance improvements for both sparse and congested periods during intensive simulations of the historical GPS trajectories of 10,357 taxis within Beijing city. Performance improvements in the average packet delivery ratio, end-to-end delay, and overhead ratio of the IDRF over TDRL-RP and VRDRT under different numbers of pairs and transmission ranges are at least 3.56%, 12.73%, and 5.14% and 6.06%, 11.84%, and 7.08%, respectively. [ABSTRACT FROM AUTHOR]

Cellular Vehicle-to-Everything (C-V2X) communications is a technology that enables intelligent vehicles to exchange information and thus coordinate with other vehicles, road users, and infrastructure. However, despite advancements in cellular technology for V2X applications, significant challenges remain regarding the ability of the system to meet stringent Quality-of-Service (QoS) requirements when deployed at scale. Thus, smaller-scale V2X use case deployments may embody a necessary stepping stone to address these challenges. This work assesses network architectures for an Intelligent Perception System (IPS) blind road junction or blind corner scenarios. Measurements were collected using a private 5G NR network with Sub-6GHz and mmWave connectivity, evaluating the feasibility and trade-offs of IPS network configurations. The results demonstrate the feasibility of the IPS as a V2X application, with implementation considerations based on deployment and maintenance costs. If computation resources are co-located with the sensors, sufficient performance is achieved. However, if the computational burden is instead placed upon the intelligent vehicle, it is questionable as to whether an IPS is achievable or not. Much depends on image quality, latency, and system performance requirements.

With the increasing demand for road traffic safety assessment, global concerns about road safety have been rising. This is particularly evident with the widespread adoption of V2X (Vehicle-to-Everything) technology, where people are more intensively focused on how to leverage advanced technological means to effectively address challenges in traffic safety. Through the research of driving style recognition technology, accurate assessment of driving behavior and the provision of personalized safety prompts and warnings have become crucial for preventing traffic accidents. This paper proposes a risk field construction technique based on environmental data collected by in-vehicle sensors. This paper introduces a driving style recognition algorithm utilizing risk field visualization and mask learning technologies. The research results indicate that, compared to traditional classical models, the improved algorithm performs excellently in terms of accuracy, stability, and robustness, enhancing the accuracy of driving style recognition and enabling a more effective evaluation of road safety. [ABSTRACT FROM AUTHOR]

The notion of vehicle-to-everything (V2X) has been adopted and developed in a host of application scenarios due to its wide compatibility with intelligent transport, smart city or other relevant concepts since it was set forth. Narrative reviews for specific V2X, such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-grid (V2G), rather than a comprehensive review of V2X, cannot provide a reference for a broader and comprehensive application of V2X. Therefore, this study first investigates the knowledge landscape of V2X publications by bibliometric analysis to portray the theme evolution and outline of V2X research. Then, the applications of V2X are then summarized into communication, computation, and service scenarios, and their forms, supporting technologies, and challenges are reviewed. The relationships and synergistic development in various V2X are also discussed. This holistic insight is necessary to help researchers connect the sub-concepts of V2X for the synergistic development of various V2X. [ABSTRACT FROM AUTHOR]

Global warming, pollution, and increasing energy demand have compelled electrification of the transport sector. Electric vehicles are not only an attractive and cleaner mode of transport, but they also possess the capacity to offer flexible storage alternative based on bidirectional vehicle-to-grid schemes. Vehicle-to-grid or V2G technology permits electric vehicles' batteries to store energy and discharge it back to the power grid during peak-load periods. However, the feasibility and economic viability of V2G is still a matter of concern and needs investigation. In this paper, the authors delved into the feasibility of V2G technology by analysing the real time-charging data of a V2G demonstration project named EV-elocity, located at the University of Nottingham campus in the UK. The authors analysed the charging data and trip-status data of two charging sites and put forward some insights regarding the feasibility of V2G and the behavioural traits of the vehicles. This paper will enlighten the research community regarding the feasibility and benefits of V2G in a real-world environment by analysing the charging/discharging and vehicle behaviour and reporting the opportunities and benefits of vehicle-to-grid technology. [ABSTRACT FROM AUTHOR]

Collective perception enables connected vehicles to share detailed environmental data, significantly enhancing situational awareness and safety. This data sharing is crucial for the functioning of modern vehicular networks, but it introduces the challenge of managing redundant information, which can congest communication channels and degrade network performance. To address this challenge, several redundancy mitigation rules have been proposed and extensively evaluated to filter out unnecessary data. This work investigates the impact of different redundancy mitigation rules on the performance of connected vehicular networks with collective perception under different market penetration rates. Additionally, the study introduces a set of hybrid rules designed to optimize this balance for collective perception services in vehicular networks. These hybrid rules are compared to scenarios without object filtering and other existing redundancy mitigation rules. Key performance metrics include channel busy ratio, environment awareness ratio, redundancy level, and the age of information. By analyzing the metrics as a function of the distance between the reported object and the receiving connected vehicle, the study identifies key trends in balancing redundancy reduction with information freshness under diverse network conditions. The results demonstrate that hybrid redundancy mitigation rules outperform existing approaches by effectively balancing channel load, redundancy level, and environment awareness, while maintaining lower age of information values. This balance is particularly crucial for safety-critical objects in close proximity to the connected vehicle. The findings highlight the importance of intelligent redundancy mitigation strategies in enhancing the timeliness and reliability of information in densely populated vehicular networks, ensuring the efficient and safe operation of connected vehicles.

Smart cities today can utilize vehicular delay tolerant networks (VDTN) to collect data from connected-objects in the environment for various delay-tolerant applications. They can take advantage of the available intelligent transportation systems (ITS) infrastructures to deliver data to the central server. The system can also exploit multiple and diverse mobility patterns found in cities, such as privately owned cars, taxis, public buses, and trams, along with their vehicle-toeverything (V2X) communications capabilities. In the envisioned convergence between the ITS and V2X, we believe that a simple and efficient routing protocol can be deployed for the delay-tolerant data delivery, contrary to the implementation of optimized solutions that might be resource-demanding and difficult to standardize. In this paper, we analyzed the performances of four baseline VDTN routing protocols, namely: direct delivery, first contact, epidemic, and spray and wait, to understand their strengths and weaknesses. Our simulation results highlighted the trade-off between distinct approaches used by those protocols and pointed out some gaps that can be refined. This study provides new interesting ideas and arguments towards developing a simple, efficient, and high-performing routing protocol for data collection in smart cities. [ABSTRACT FROM AUTHOR]

Offloading computation-intensive tasks to mobile edge computing (MEC) servers, such as road-side units (RSUs) and a base station (BS), can enhance the computation capacities of the vehicle-to-everything (V2X) communication system. In this work, we study an MEC-assisted multi-vehicle V2X communication system in which multi-antenna RSUs with liner receivers and a multi-antenna BS with a zero-forcing (ZF) receiver work as MEC servers jointly to offload the tasks of the vehicles. To control the energy consumption and ensure the delay requirement of the V2X communication system, an energy consumption minimization problem under a delay constraint is formulated. The multi-agent deep reinforcement learning (MADRL) algorithm is proposed to solve the non-convex energy optimization problem, which can train vehicles to select the beneficial server association, transmit power and offloading ratio intelligently according to the reward function related to the delay and energy consumption. The improved K-nearest neighbors (KNN) algorithm is proposed to assign vehicles to the specific RSU, which can reduce the action space dimensions and the complexity of the MADRL algorithm. Numerical simulation results show that the proposed scheme can decrease energy consumption while satisfying the delay constraint. When the RSUs adopt the indirect transmission mode and are equipped with matched-filter (MF) receivers, the proposed joint optimization scheme can decrease the energy consumption by 56.90% and 65.52% compared to the maximum transmit power and full offloading schemes, respectively. When the RSUs are equipped with ZF receivers, the proposed scheme can decrease the energy consumption by 36.8% compared to the MF receivers. [ABSTRACT FROM AUTHOR]

Automated Driving (AD) has been receiving considerable attention from industry, the public, and researchers for its ability to reduce accidents, emissions, and congestion. The purpose of this study is to extend the standardized Local Dynamic Map (LDM) by adding two new layers, and develop efficient and accurate algorithms designed to enhance AD by exploiting the LDM coupled with Cooperative Perception (CP). The LDM is implemented as a Neo4j graph database and extends the standard four-layer structure by adding a detection layer and a prediction layer. A custom Application Programming Interface (API) manages all incoming data, generates the LDM, and runs the algorithms. Currently, the API can match detected entities coming from different sources, correctly position them on the map even in the presence of high uncertainties in the data, and predict their future actions. We tested the developed LDM with real-world data, which we collected using a prototype vehicle from Centro Ricerche FIAT (CRF) Trento Branch—the supporting research center for this work—in urban, suburban, and highway areas of Trento, Italy. The results show that the developed solution is capable of accurately matching and predicting detected entities, is robust to high uncertainties in the data, and is efficient, achieving real-time performance in all scenarios. From these results we can conclude that the LDM and CP have the potential to be core parts of AD, bringing improvements to the development process. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RIS) are expected to bring about a revolutionary transformation in vehicular networks, thus paving the way for a future characterized by connected and automated vehicles (CAV). An RIS is a planar structure comprising many passive elements that can dynamically manipulate electromagnetic waves to enhance wireless communication by reflecting, refracting, and focusing signals in a programmable manner. RIS exhibits substantial potential for improving vehicle-to-everything (V2X) communication through various means, including coverage enhancement, interference mitigation, improving signal strength, and providing additional layers of privacy and security. This article presents a comprehensive survey that explores the emerging opportunities arising from the integration of RIS into vehicular networks. To examine the convergence of RIS and V2X communications, the survey adopted a holistic approach, thus highlighting the potential benefits and challenges of this combination. In this study, we examined several applications of RIS-aided V2X communication. Subsequently, we delve into the fundamental emerging technologies that are expected to empower vehicular networks, encompassing mobile edge computing (MEC), non-orthogonal multiple access (NOMA), millimeter-wave communication (mmWave), Artificial Intelligence (AI), and visible light communication (VLC). Finally, to stimulate further research in this domain, we emphasize noteworthy research challenges and potential avenues for future exploration. [ABSTRACT FROM AUTHOR]

Network slicing is introduced for elastically instantiating logical network infrastructure isolation to support different application types with diversified quality of service (QoS) class indicators. In particular, vehicular communications are a trending area that consists of massive mission-critical applications in the range of safety-critical, intelligent transport systems, and on-board infotainment. Slicing management can be achieved if the network infrastructure has computing sufficiency, a dynamic control policy, elastic resource virtualization, and cross-tier orchestration. To support the functionality of slicing management, incorporating core network infrastructure with deep learning and reinforcement learning has become a hot topic for researchers and practitioners in analyzing vehicular traffic/resource patterns before orchestrating the steering policies. In this paper, we propose QoS-driven management by considering (edge) resource block utilization, scheduling, and slice instantiation in a three-tier resource placement, namely, small base stations/access points, macro base stations, and core networks. The proposed scheme integrates recurrent neural networks to trigger hidden states of resource availability and predict the output of QoS. The intelligent agent and slice controller, namely, RDQ3N, gathers the resource states from three-tier observations and optimizes the action on allocation and scheduling algorithms. Experiments are conducted on both physical and virtual representational vehicle-to-everything (V2X) environments; furthermore, service requests are set to massive thresholds for rendering V2X congestion flow entries. [ABSTRACT FROM AUTHOR]

This paper is focused to the emerging technological development in the area of the advanced vehicular communication systems. It considers the most up to date Vehicular to Everything technologies and discusses the typical scenarios related to the communication between vehicles, between vehicles and infrastructure, vehicles and pedestrians and communication between vehicles and network environment. Paper also considers the arising issues and countermeasures related to the security, privacy and quality of service (QoS) as well as predictive QoS aspects. [ABSTRACT FROM AUTHOR]

In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing of data involving different aspects of the vehicular traffic. Moreover, new resource-hungry applications have been envisaged, such as autonomous driving or in-cruise entertainment, hence making the demand for computation and storage resources one of the most important challenges in vehicular networks. In this context, Mobile Edge Computing (MEC) has become the key technology to handle these problems by providing cloud-like capabilities at the edge of mobile networks to support delay-sensitive and computation-intensive tasks. In the meantime, researchers have envisaged use of onboard vehicle resources to extend the computing capabilities of MEC systems. This paper presents a comprehensive review of the most recent works related to MEC-assisted vehicular networks, as well as vehicle-assisted MEC systems. We illustrate the MEC system architecture and discuss its deployment in vehicular environments, as well as the key technologies to realize this integration. After that, we review the recent literature by identifying three different areas, i.e.: (i) MEC providing additional resources to vehicles (e.g., for task offloading); (ii) MEC enabling innovative vehicular applications (e.g., platooning), and (iii) vehicular networks providing additional resources to MEC systems. Finally, we discuss open challenges and future research directions, addressing the possible interplays between MEC systems and vehicular networks. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy (BLE) is a prominent short-range wireless communication protocol widely extended for communications and sensor systems in consumer electronics and industrial applications, ranging from manufacturing to retail and healthcare. The BLE protocol provides four generic access profile (GAP) roles when it is used in its low-energy version, i.e., ver. 4 and beyond. GAP roles control connections and allow BLE devices to interoperate each other. They are defined by the Bluetooth special interest group (SIG) and are primarily oriented to connect peripherals wirelessly to smartphones, laptops, and desktops. Consequently, the existing GAP roles have characteristics that do not fit well with vehicular communications in cooperative intelligent transport systems (C-ITS), where low-latency communications in high-density environments with stringent security demands are required. This work addresses this gap by developing two new GAP roles, defined at the application layer to meet the specific requirements of vehicular communications, and by providing a service application programming interface (API) for developers of vehicle-to-everything (V2X) applications. We have named this new approach ITS-BLE. These GAP roles are intended to facilitate BLE-based solutions for real-world scenarios on roads, such as detecting road traffic signs or exchanging information at toll booths. We have developed a prototype able to work indistinctly as a unidirectional or bidirectional communication device, depending on the use case. To solve security risks in the exchange of personal data, BLE data packets, here called packet data units (PDU), are encrypted or signed to guarantee either privacy when sharing sensitive data or authenticity when avoiding spoofing, respectively. Measurements taken and their later evaluation demonstrated the feasibility of a V2X BLE network consisting of picocells with a radius of about 200 m.

The driving experience has improved significantly due to the advancement of automotive information and electronic technologies. Vehicles are becoming intelligent and connected, which are named Intelligent Connected Vehicles (ICVs). However, the security problems of ICVs have emerged, and the cyber security of ICVs has become one of the most popular research fields to avoid ICVs being invaded. To mitigate the security threats of ICVs and enhance their defense ability, we investigate ICV security from the aspects of vehicle attacks and defenses. We classify the attack and defense technologies in terms of the functions of various information subsystems in the ICV, i.e., the ICV information perception system, in-vehicle communication system, control system, and V2X communication systems. We further highlight the security challenges and potential research directions to facilitate the development of better security mechanisms for ICV security. [ABSTRACT FROM AUTHOR]

V2X communication is susceptible to attenuation and fading caused by external interference. This interference often leads to bit error and poor quality and stability of the wireless link, and it can easily disrupt packet transmission. In order to enhance communication reliability, the 3rd Generation Partnership Project (3GPP) introduced the Hybrid Automatic Repeat Request (HARQ) technology for both 4G and 5G systems. Nevertheless, it can be improved for poor communication conditions (e.g., heavy traffic flow, long-distance transmission), especially in advanced or cooperative driving scenarios. In this paper, we propose an Adaptive Hybrid Automatic Repeat Request (A-HARQ) scheme that can reduce the average block error rate, the average number of retransmissions, and the round-trip time (RTT). It adapts the Q-learning model to select the timing and frequency of retransmission to enhance the transmission reliability. We also design some transmission schemes—K-repetition, T-delay and [T, K]-overlap—which are used to shorten latency and avoid packet collision. Compared with the conventional 5G HARQ, our simulation results show that the proposed A-HARQ scheme decreases the system's average BLER, the number of retransmissions, and the RTT to 5.55%, 1.55 ms, and 0.97 ms, respectively. [ABSTRACT FROM AUTHOR]

Connected automated vehicles (CAVs) can use vehicle-to-everything (V2X) communications to exchange their driving intentions and coordinate their maneuvers. Message generation rules are necessary to decide when and how maneuver coordination messages (MCMs) should be generated. The design of these generation rules must consider the critical nature of maneuver coordination and the limited bandwidth available for V2X communications. This study proposes the first two sets of V2X message generation rules for maneuver coordination between CAVs. The Risk proposal increases the rate at which vehicles generate MCMs when vehicles detect a potential safety risk. With the Tracking Trajectories proposal, vehicles generate a new maneuver coordination message when they significantly modify their planned trajectory. For both proposals, the messages include the planned and possible desired trajectories of the ego vehicle. The evaluation shows that the proposed generation rules efficiently support maneuver coordination and offer a balance between more frequent updates of the driving intentions of CAVs and lower coordination time and better control of the V2X communications channel load. This study also reveals that congestion control protocols can significantly impact maneuver coordination. [ABSTRACT FROM AUTHOR]

As one of the key technologies of the intelligent transportation system (ITS), vehicle-to-everything (V2X) communications have received increasing attention. Because of the broad and unlicensed spectrum, free space optical (FSO) communications have become a potential technology for V2X communications. In this article, the implementation of FSO communications is introduced first. Afterward, for the three advantages of FSO communications in ITS, the concrete principle and implementation approaches are illustrated in detail. First, based on the adaptive illumination schemes, the communication range is expanded without visual interference caused by illumination. Second, with the help of mode division multiplexing (MDM) and wavelength division multiplexing (WDM), the communication rate is further increased. Third, integrating sensing and communication enables vehicles to effectively obtain information and improve traffic efficiency and safety. In addition, applications of FSO communications in various communication scenarios in ITS are described. To satisfy the complex requirements of communication, illumination, and sensing in traffic scenarios, a deep learning-enabled heterogeneous system of FSO and radio frequency is proposed. Furthermore, future applications and open issues are provided for further investigation. [ABSTRACT FROM AUTHOR]

Collective perception is one of the key ideas of vehicular networking and allows the exchange of data about perceived objects. However, unlike autonomous driving systems, human drivers cannot screen large numbers of objects to judge their dangerousness. An assistance system in the vehicle, therefore, must do this job. This article shows a concept for a human–machine interface that could be used to warn the driver in case such a system detects an actually dangerous object. A user study in a driving simulator was performed to evaluate its potential to prevent accidents. Eye-tracking glasses were used to analyze the driver’s gaze during different types of situations. Furthermore, the participants’ subjective experience was evaluated with a questionnaire. Results show that drivers trust the system and brake earlier and with more control due to the warnings, and ultimately, the majority of accidents could be avoided thanks to the warnings. [ABSTRACT FROM AUTHOR]

With the increasing demand for road traffic safety assessment, global concerns about road safety have been rising. This is particularly evident with the widespread adoption of V2X (Vehicle-to-Everything) technology, where people are more intensively focused on how to leverage advanced technological means to effectively address challenges in traffic safety. Through the research of driving style recognition technology, accurate assessment of driving behavior and the provision of personalized safety prompts and warnings have become crucial for preventing traffic accidents. This paper proposes a risk field construction technique based on environmental data collected by in-vehicle sensors. This paper introduces a driving style recognition algorithm utilizing risk field visualization and mask learning technologies. The research results indicate that, compared to traditional classical models, the improved algorithm performs excellently in terms of accuracy, stability, and robustness, enhancing the accuracy of driving style recognition and enabling a more effective evaluation of road safety.

Global warming, pollution, and increasing energy demand have compelled electrification of the transport sector. Electric vehicles are not only an attractive and cleaner mode of transport, but they also possess the capacity to offer flexible storage alternative based on bidirectional vehicle-to-grid schemes. Vehicle-to-grid or V2G technology permits electric vehicles’ batteries to store energy and discharge it back to the power grid during peak-load periods. However, the feasibility and economic viability of V2G is still a matter of concern and needs investigation. In this paper, the authors delved into the feasibility of V2G technology by analysing the real time-charging data of a V2G demonstration project named EV-elocity, located at the University of Nottingham campus in the UK. The authors analysed the charging data and trip-status data of two charging sites and put forward some insights regarding the feasibility of V2G and the behavioural traits of the vehicles. This paper will enlighten the research community regarding the feasibility and benefits of V2G in a real-world environment by analysing the charging/discharging and vehicle behaviour and reporting the opportunities and benefits of vehicle-to-grid technology.

This paper presents an analysis of the effectiveness of the element interaction model in a vehicular ad hoc network (VANET). An analysis of the mathematical model and its numerical solution for the system of boundary device interactions in the traditional configuration of roadside unit (RSU) placement using single- and dual-channel connection between on-board units (OBU) and RSU is given. In addition, the model efficiency is improved using a clustering approach. The efficiency evaluation is based on calculating the percentage of unprocessed requests generated by OBUs during their mobility, the average power consumption and the magnitude of the delay in transmitting and processing the generated requests in the OBU–RSU system. The traditional and cluster models are compared. The results obtained in this paper show that each of the proposed models can be effectively implemented in mobile nodes and will significantly reduce the overall expected query processing time to improve the organization and algorithmic support of VANET. Along with this, it is shown that the developed approach allows for efficient power consumption when combining RSUs into clusters with a shared cache. The novelty of solving the problems is due to the lack of a comprehensive model that allows the distribution and prediction of the parameters and resources of the system for different computational tasks, which is essential when implementing and using V2X technology to solve the problems of complex management of VANET elements. [ABSTRACT FROM AUTHOR]

To counter increasing threats of cyberattacks on automobiles, it is important to practice “secure by design.” For this reason, the regulations and standards that require a risk-based security design are being formulated in the automotive industry. In the concept phase described in ISO/SAE21434:2021, it is necessary to determine cybersecurity controls based on results of the threat analysis (TA) and the risk assessment (RA). On the other hand, the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) defines four categories of cybersecurity controls: protection, detection, response, and recovery demand to select cybersecurity controls from these categories. To be able to formulate appropriate cybersecurity control for detection, this article proposes an extension of the threat analysis and risk assessment (TARA) based on Japanese Automotive Standards Organization (JASO) TP15002:2016 to derive candidate monitoring points (MPs) to implement detection functions. We also present evaluation metrics for the MPs derived by our proposed method and discuss future research directions. [ABSTRACT FROM AUTHOR]

The cellular vehicle-to-everything (C-V2X) sidelink technology, specified in the long-term evolution (LTE) and further improved in the 5G new radio (NR) standards to facilitate direct data exchange between vehicles, will play a crucial role in revolutionizing transportation systems. However, the demand for very high reliability and ultralow latency services especially challenges the sidelink resource allocation mechanism when performed by distributed vehicles, in the so- called autonomous mode. One of the major causes of performance degradation is the resource allocation mechanism, which was designed for orthogonal multiple access (OMA) and can generate interference and collisions under high load conditions. In this context, here we argue in favor of the use of non-OMA (NOMA) as a game changer for the sidelink in the upcoming 6G V2X, and the purpose of this article is to provide a reference for further intriguing studies in the field. Additionally, the gain achievable over conventional allocation schemes by enabling NOMA through the use of successive interference cancelation (SIC) at the receiver is measured through realistic simulations conducted when considering the latest C-V2X specifications. [ABSTRACT FROM AUTHOR]

Vehicle-to-Everything (V2X) communication technology aims to enhance intelligent vehicles’ coordination and perception by enabling interactions with other vehicles, road users, and infrastructure. Although 4th and 5th generation cellular technologies (4G LTE and 5G NR) can support non-safety V2X communication, their design, rooted in multimedia and telephony, doesn’t align well with safety-critical applications. Despite the advancements of 5G NR over 4G LTE, ensuring reliable Quality-of-Service (QoS) metrics remains a notable concern. The process of handover, a core cellular network function, significantly contributes to the challenges in cellular communication reliability. To assess handover’s impact on latency in V2X communication scenarios, a driving-focused measurement campaign was executed on an early 5G NR-enabled commercial cellular network. Findings reveal that, in terms of its effect on end-user latency, the more mature 4G LTE network displays on average 63% (intra-freq.) and 36% (inter-freq.) lower latency costs when performing handovers compared to early 5G NR deployments. Notably, inter-generation handovers (4G to 5G and vice versa) exhibit 24% (intra-freq.) and 10% (inter-freq.) higher average latency costs with respect to other handover types. Furthermore, discernible variations in network reliability emerge between distinct V2X scenarios such as urban and rural environments. The prevailing reliability concerns tied to present handover algorithms hold substantial weight, as they undermine intelligent vehicles’ trust in network-received information. Consequently, these outcomes underscore the urgency for designing and implementing handover strategies better suited to the dynamic mobility characteristics of intelligent vehicles across diverse V2X scenarios.

Pedestrian-to-vehicle communication helps to prevent pedestrian accidents by disseminating position information of a pedestrian to nearby vehicles. This is especially useful when a pedestrian is in the blind spot of vehicles. In urban canyons, roadside buildings obstruct satellite signals, which may cause an outage in pedestrian positioning. Using vehicles and roadside units as positioning anchors helps to solve this problem. But the performance of distance estimation, based on the attenuation property of wireless signals, is degraded by multipath propagation. To address this issue, this paper exploits phase information of V2X signals, instead of signal strength, for distance estimation. The signal transmitted by a pedestrian device is simultaneously received by several anchors, and the phase difference of arrival (PDoA) is used to compute the distance difference. Using the OFDM structure, phase information of multiple subcarriers can be computed efficiently. Potential problems like inter-symbol interference, synchronization error and phase ambiguity are addressed and the accuracy of distance estimation is further improved by combining phase information of multiple subcarriers before fixing the phase ambiguity. The effectiveness of the proposed method is verified by simulation evaluations, using 3D map and ray-tracing.

Advancements in intelligent vehicles and Intelligent Transport Systems (ITS) have shown that they are now feasible in both technology and commerce. However, there are still significant challenges to overcome, particularly regarding the perception and coordination of intelligent vehicles in unfavourable conditions. Vehicle-to-Everything (V2X) communications is a technology that aims to enable intelligent vehicles to communicate with other road users and infrastructure to increase their range of perception and coordination capabilities. While the 4th generation of cellular technology (4G LTE) is capable of supporting V2X communications to some extent, its multimedia and telephony-centric design does not translate well to safety-critical applications. As a result, the 5th generation of cellular technology (5G NR) is being developed to improve V2X communications. To investigate the effectiveness of 5G NR in V2X communications, a driving-based measurement campaign of a commercial cellular network with early 5G NR deployments was conducted. Results showed that the existing 4G LTE network is limited in its capability, and early 5G NR deployments can in fact outperform it. However, neither 4G LTE nor 5G NR can reliably support advanced V2X applications. Early 5G NR deployments suffer from significant reliability issues compared to existing 4G LTE deployments. These reliability issues are of particular concern, as they impact the vehicle’s ability to trust the information it receives. These findings highlight the need for further design and implementation of intelligent vehicles and future 5G NR networks to address these reliability concerns and ensure the safe and efficient operation of intelligent vehicles in all conditions.

With the development of communications, various applications of communication technologies, such as remote driving, delivery drones, and telesurgery, are emerging. In particular, in many cases, these applications need real-time video transmission services, and they should support low latency for operation reliability and quick response in emergencies. Sensor sharing is required to support advanced communication services, but the latency analysis of device-to-remote users or remote servers with high data traffic is insufficient. Most related works have device-to-device communication or low data traffic messages for basic device status sharing. However, the latency analysis of sensor sharing between a device and a remote server or remote user is essential to support advanced communication services such as autonomous driving utilizing data offloading and device operation by remote users via the base station and server. Therefore, in this paper, we analyze the end-to-end latency and latency elements for video sharing, which is the most representative sensor in 4G long-term evolution (LTE) and 5G new radio (NR) Uu interfaces. In addition, we derive the supportable video resolution according to the raw video and encoded video transmission in each communication system. For each video resolution level, we analyze which latency elements have a significant effect on the end-to-end latency. Depending on each communication system, we investigate the number of users for the real-time sensor-sharing system that can be supported at the same time. Simulation results show that the LTE Uu interface supports up to full high definition (FHD) video resolution, and the 5G Uu interface supports up to ultrahigh definition (UHD). Additionally, the results show that only a single user can be supported with the FHD resolution level in the LTE Uu interface, whereas up to 19 users can be supported in the 5G Uu interface.

By sharing resources for radar sensing and communications, integrated sensing and communication (ISAC) has promising applications in diverse scenarios, such as human-machine interface, vehicle-to-everything (V2X), remote sensing, and environment surveillance.In order to resolve the frequency resource conflict in high-speed V2X networks, based on the modulation orthogonal time frequency (OTFS), the OTFS-ISAC system was proposed, where the base station receives reflected echoes to obtain sensing parameters of vehicle motion and then establish the vehicle’s mobile topology.Additionally, in terms of the multi-vehicle network, the design of OTFS-ISAC with NOMAsatisfying the fairness and the algorithm of power allocation were proposed.Simulations show that compared with the NOMA without radar sensing system, the proposed design improves the system data rate by 25%.

In a context in which the connectivity level of last-generation vehicles is constantly on the rise, the combined use of Vehicle-To-Everything (V2X) connectivity and autonomous driving can provide remarkable benefits through the synergistic optimization of the route and the speed trajectory. In this framework, this paper focuses on vehicle ecodriving optimization in a connected environment: the virtual test rig of a premium segment passenger car was used for generating the simulation scenarios and to assess the benefits, in terms of energy and time savings, that the introduction of V2X communication, integrated with cloud computing, can have in a real-world scenario. The Reference Scenario is a predefined Real Driving Emissions (RDE) compliant route, while the simulation scenarios were generated by assuming two different penetration levels of V2X technologies. The associated energy minimization problem was formulated and solved by means of a Variable Grid Dynamic Programming (VGDP), that modifying the variable state search grid on the basis of the V2X information allows to drastically reduce the DP computation burden by more than 95%. The simulations show that introducing a smart infrastructure along with optimizing the vehicle speed in a real-world urban route can potentially reduce the required energy by 54% while shortening the travel time by 38%. Finally, a sensitivity analysis was performed on the biobjective optimization cost function to find a set of Pareto optimal solutions, between energy and travel time minimization. [ABSTRACT FROM AUTHOR]

Roadside millimeter-wave radar is one of the most important sensors in roadside perception systems, which have been widely used in intelligent traffic systems. It is important to find an effective calibration procedure for roadside radar to obtain the World Geodetic System-1984 (WGS-84) coordinates of detected targets. However, it is difficult to calibrate roadside millimeter-wave radar safely on public roads without road closures. To solve this problem, this article proposes a roadside millimeter-wave radar calibration method based on connected vehicle technology. First, all the trajectory points of vehicles detected by radar, including the connected vehicle, are collected, and the WGS-84 coordinates of the connected vehicle are obtained by vehicle-to-everything (V2X) communication with the roadside unit. Then, the trajectory points of vehicles are clustered with the density-based spatial clustering of applications with noise (DBSCAN) method, and the trajectory of the connected vehicle in the radar coordinate system is identified with the velocity-matching method automatically. The data pairs of the connected vehicle in the radar coordinate system and WGS-84 coordinate system are obtained with time synchronization. Finally, the calibration parameters are solved with five different types of optimization methods. We verify these methods on the beltway in Changsha. The results show that the improved pseudo inverse method (IPIM) and PIM achieve better performance than the extrinsic calibration method (ECM), rotate coordinate method (RCM), and improved RCM, and they can meet lane-level positioning requirements for V2X applications. This study provides an economical and effective way to solve the calibration problem for roadside millimeter-wave radar in an engineering project. [ABSTRACT FROM AUTHOR]

Recent advances in wireless communication technology allow for the cooperative coordination of vehicles and infrastructures under vehicle-to-everything (V2X) communication protocols. V2X communication protocols are shaping a new reality for intersection control, allowing for individual driver preferences to be taken into account, such as heterogeneity in terms of value of time and willingness to pay to reduce individual delay. In this context, different economic instruments have been proposed for intersection control under connected vehicles, including various types of auctions, direct payments, and credit schemes. This study offers a comprehensive review on market-inspired control approaches under connected vehicles for both signalized and unsignalized intersections, focusing on auction-based and direct transaction schemes among drivers as incentive mechanisms for aligning user and system objectives. A structured way of analyzing the existing literature is introduced, underlining relevant methodological and practical issues. The key aspects of market-based control schemes are outlined, including priority allocation, types of agents employed, incentive compatibility, solution approaches, and validation. Emerging challenges for the implementation of such approaches and future research directions are then identified and discussed. [ABSTRACT FROM AUTHOR]

As there are many factors affecting vehicle lane changes in a tunnel, which leads to the unstable state of vehicles during lane changes and an increase of collision events, a new vehicle lane-changing model is proposed by considering the influence of typical factors such as noise and brightness in a tunnel under a vehicle-to-everything (V2X) environment. First, V2X-based technology enables real-time access to a target vehicle surrounding information characteristics in a tunnel, establishing a lane-changing decision model to quantify the willingness of vehicles to change lanes. Second, considering safety as a prerequisite for a lane change, establishing a vehicle safety lane-change-distance model and a minimum safe-distance model was introduced for comparison to evaluate the safety of lane changing. On this basis, considering the noise and brightness effects of a tunnel, the relationship between brightness, noise, and response time of human-driven vehicles, hybrid driving vehicles, and autonomous vehicles is quantitatively analyzed, and then a new vehicle lane-changing model in a tunnel is established. The results of the research show that brightness in a tunnel has a more significant effect on the driver than noise. At the same time, autonomous driving, as well as hybrid driving, has better stability and comfort with less change in velocity, acceleration, and other states during lane changing in a tunnel compared to manned driving, which proves the reasonableness of the model and helps to provide a model basis for research of real vehicle lane changing under a V2X environment. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RIS) are expected to bring about a revolutionary transformation in vehicular networks, thus paving the way for a future characterized by connected and automated vehicles (CAV). An RIS is a planar structure comprising many passive elements that can dynamically manipulate electromagnetic waves to enhance wireless communication by reflecting, refracting, and focusing signals in a programmable manner. RIS exhibits substantial potential for improving vehicle-to-everything (V2X) communication through various means, including coverage enhancement, interference mitigation, improving signal strength, and providing additional layers of privacy and security. This article presents a comprehensive survey that explores the emerging opportunities arising from the integration of RIS into vehicular networks. To examine the convergence of RIS and V2X communications, the survey adopted a holistic approach, thus highlighting the potential benefits and challenges of this combination. In this study, we examined several applications of RIS-aided V2X communication. Subsequently, we delve into the fundamental emerging technologies that are expected to empower vehicular networks, encompassing mobile edge computing (MEC), non-orthogonal multiple access (NOMA), millimeter-wave communication (mmWave), Artificial Intelligence (AI), and visible light communication (VLC). Finally, to stimulate further research in this domain, we emphasize noteworthy research challenges and potential avenues for future exploration.

The proper power allocation between multiple energy sources is crucial for hybrid electric vehicles to guarantee energy economy. As a data-driven technique, offline deep reinforcement learning (DRL) solely exploits existing data to train energy management strategy (EMS), which becomes a promising solution for intelligent power allocation. However, current offline DRL-based strategies put high demands on the quality of datasets, and it is difficult to obtain numerous high-quality samples in practice. Thus, a bootstrapping error accumulation reduction (BEAR)-based strategy is proposed to enhance the energy-saving performance with different kinds of datasets. After that, based on the advanced V2X technology, a data-driven energy management updating framework is proposed to improve both fuel economy and adaptability of EMS via multi-updating. Specifically, the framework deploys multiple V2X-based buses to collect real-time information, and updates the strategy periodically making full use of offline data. The results show that the proposed BEAR-based EMS performs better than state-of-the-art offline EMSs in terms of fuel economy, especially realizing an improvement of 2.25% when training with mixed datasets. It is also validated that the offline EMS with the updating mechanism can reduce energy costs step by step under two different kinds of initial datasets. • An offline DRL-based EMS is proposed to enhance energy-saving performance. • Based on V2X technology, an updating framework is designed to extract EMS. • A comparative experiment is carried out on proposed strategy and advanced offline EMSs. • Overall performance with limited computational resources is discussed. • The effectiveness of framework is verified under different initial datasets. [ABSTRACT FROM AUTHOR]

The article presents a 2-port multiple-input multiple-output (MIMO) antenna array designed for millimeter-wave band operation, specifically optimized for Vehicle-to-Everything (V2X) applications. To enhance performance, a novel approach employing two types of metasurfaces on a single plate is utilized. The first metasurface, based on a loop structure, aims to improve the frequency bandwidth, while the second metasurface, utilizing a composite right/left-handed (CRLH) design, focuses on reducing mutual coupling. These two steps result in an improved frequency bandwidth exceeding 45% and a decreased structure width of about 50% compared to other designs. Notably, the compact nature of these metasurfaces, measuring approximately 32 × 3 mm 2 , is a significant feature that reduces manufacturing costs. The arrayed antenna, with 20 series elements, is suitable for medium-range radar in V2X applications, although our method is generally applicable to other ranges. Additionally, the radiation pattern exhibits a 40-degree beam rotation within the frequency bandwidth, making it well-suited for V2X applications. The results demonstrate impressive diversity performance, with a consistent maximum gain of 16 dB across the entire frequency bandwidth of approximately 68 GHz to 82 GHz, representing an 18.2% coverage. [ABSTRACT FROM AUTHOR]