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Free space optical (FSO) communication has shown promising advantages among other wireless schemes. It provides a very high data rate, freedom from licensing, low cost of deployment and requires low power. FSO is a technology that has undergone rapid development over the last several years. These communication systems are a line-of-sight technology in which information is transmitted through the atmosphere on modulated laser beams or light-emitting diode (LED) beams. When FSO technology was first introduced, it was seen as an attractive option to bridge the "last mile bottleneck" that is present in many of today's optical fiber-based networks. As compared to existing radio frequency (RF) based wireless systems, this technology possesses multiple advantages, such as high bandwidth, license-free band use, long operational range, spatial reusability, security and immunity to electromagnetic interference. The narrow and directional characteristics of a laser beam employed in FSO enable spatial reuse and make it hard to eavesdrop, thus raising the level of security. The use of light as carrier in this technology provides immunity to electromagnetic interference. Despite its many advantages, this technology is susceptible to some weather conditions, such as fog, rain, sleet and snow, and to misalignment of transmitter–receiver terminals. Atmospheric conditions will impair the propagation of an optical signal because the propagation of light may undergo absorption and scattering. Pointing error caused by misalignment of the transceivers is another major challenge in FSO communication system. The pointing error may result in degradation or even total loss of the received signal. This error may arise because of transceiver sway, platform vibration, the motion of mobile stations, errors or uncertainties in the tracking system. Another type of pointing error is beam wandering caused by the in-homogeneity of large-scale eddies in the atmosphere (i.e., atmospheric turbulence), where the transmitted beam may deviate from its intended path. This survey paper focuses on the systems involved in the alignment of the transmitter and the receiver so that the maximum amount of power is collected by the receiver and the analysis of beam profile in various atmospheric conditions and their mitigation methods. [ABSTRACT FROM AUTHOR]

In this article, a new compact planar phased array antenna is proposed for blind spot reduction in V2X communications. The proposed antenna can be easily integrated on the front-panel or rear-view side by providing 2-dimensional wide communication coverage while overcoming physical integration space limitations. Moreover, because the proposed antenna is based on a planar structure, it can be integrated effectively with beamforming circuits. To verify the proposed antenna, both single and array configurations were fabricated and compared with conventional patch antennas at 5.9 GHz. The proposed single antenna with a size of 1λ0 × 1.1λ0 × 0.02λ0 exhibited a measured bandwidth of 4.3% and a peak gain of 6.1 dBi. The measured HPBWs in the E- and H- planes were approximately 126° and 136°, respectively, compared to 75.5° and 66.9° for the conventional patch antenna. Further, the proposed antenna and reference patch antenna were extended to 1 × 8 array configurations with 8-ch beamforming circuits. The measured scan angle of the proposed antenna was 160°, compared to only 98° for the patch antenna. Finally, a vehicular communication test was conducted with 256-QAM signals, where the proposed 1 × 8 antenna array exhibited a stable error vector magnitude (EVM) over a wide coverage angle of 150°.

Optimizing downlink coordinated multipoint (CoMP) performance through advanced scheduling algorithms enhances V2X communication technology, enabling efficient resource allocation, minimizing interference, and maximizing data rates for reliable and synchronized communication between vehicles and infrastructure. In this paper, several scheduling algorithms were compared, including support vector machine (SVM) linear, SVM Radial Basis Function (RBF), SVM Sigmoid, Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Graph Convolutional Networks (GCN). The performance metrics used in this comparison included CoMP decision, throughput, and cell edge throughput. The results showed that the out-rated GCN algorithm had the best-triggering composition for 5G radio networks, outperforming the other algorithms in terms of CoMP decision accuracy and overall throughput. In particular, the GCN algorithm demonstrated significant improvements in cell edge throughput, which is critical for ensuring reliable communication in areas with weaker signal strength. The reported results proves that the integration of advanced scheduling algorithms in the downlink CoMP framework enhances the efficiency of V2X communication, enabling optimized resource allocation, interference mitigation, and maximized throughput, thereby improving system efficiency, reducing latency, and ensuring reliable and seamless information exchange for connected vehicles, smart cities, and industrial automation. [ABSTRACT FROM AUTHOR]

In this article, a new compact planar phased array antenna is proposed for blind spot reduction in V2X communications. The proposed antenna can be easily integrated on the front-panel or rear-view side by providing 2-dimensional wide communication coverage while overcoming physical integration space limitations. Moreover, because the proposed antenna is based on a planar structure, it can be integrated effectively with beamforming circuits. To verify the proposed antenna, both single and array configurations were fabricated and compared with conventional patch antennas at 5.9 GHz. The proposed single antenna with a size of 1λ 0 × 1.1λ 0 × 0.02λ 0 exhibited a measured bandwidth of 4.3% and a peak gain of 6.1 dBi. The measured HPBWs in the E- and H- planes were approximately 126° and 136°, respectively, compared to 75.5° and 66.9° for the conventional patch antenna. Further, the proposed antenna and reference patch antenna were extended to 1 × 8 array configurations with 8-ch beamforming circuits. The measured scan angle of the proposed antenna was 160°, compared to only 98° for the patch antenna. Finally, a vehicular communication test was conducted with 256-QAM signals, where the proposed 1 × 8 antenna array exhibited a stable error vector magnitude (EVM) over a wide coverage angle of 150°. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and advanced human-centred technologies have seen rapid expansion in smart city applications, including smart enterprises, homes, and vehicular networks. The IoT architecture underpins technological support for both human- and device-centric secure solutions, which are essential to the functioning of smart cities. Within this evolving landscape, Vehicle-to-Everything (V2X) technology has emerged as a key innovation, integrating vehicular communication with internet connectivity. This integration facilitates seamless data exchange between vehicles, infrastructure, and other entities, contributing to enhanced road safety, optimized traffic flow, reduced emissions, and the development of more intelligent transportation systems. As V2X technology becomes increasingly widespread, ensuring the security and privacy of the information exchanged within these networks is critical. While much of the existing research has concentrated on general IoT security frameworks and standalone vehicular communication protocols, our study addresses the unique security challenges inherent in V2X environments. This paper identifies and examines various security threats, including data privacy breaches, malicious attacks, and traffic manipulation, and explores strategies to mitigate these risks within V2X communications. In addition, this paper investigates the potential of blockchain technology as an innovative solution for enhancing the security and trustworthiness of information exchanges in V2X networks. The paper also includes a case study that presents a proposed solution demonstrating how blockchain technology could strengthen the security and integrity of data transfers within V2X systems. Through comprehensive analysis, evaluation, and recommendations for future research, this work contributes to the current understanding of secure data exchange in V2X networks.

This paper presents a novel method for using full duplex (FD) communication across shared channels in cellular vehicle-to-everything (C-V2X) networks. Three modes A, B and C have been defined for communication in V2X networks where Mode C for FD communication has been introduced for the first time. We have derived mathematical model for success probability of FD for C-V2X network and formulated expressions for area spectral efficiency (ASE). Dual connectivity (DC) for simultaneous link of receiving vehicle with nearest transmitting vehicle and base station (BS) has also been analyzed for the first time for HD and FD in C-V2X network. Analytical and Monte Carlo simulations results have shown that utilization of FD in C-V2X network provides comparable success probability as compared to HD with improvement in ASE. Success probability of FD remains close to HD in terms of signal to interference noise ratio (SINR) in the range from -40 dBW to 60 dBW. Importance of achieving perfect self-interference cancellation (SIC) for different values of self-interference (SI) in FD network has also been evaluated. FD in C-V2X network has shown to significantly improve ASE with gain of 2.55 dB over Direct Short Range Communication (DSRC) and 2 dB over HD in C-V2X network under specific conditions. No degradation in ASE was observed in case of DC for HD and FD. ASE for FD has shown improvement as compared to HD for DSRC and C-V2X networks when evaluated against density of vehicles, BSs and roads.

Recently, the Third Generation Partnership Project (3GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios. [ABSTRACT FROM AUTHOR]

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Aiming at the problem of spectrum scarcity faced by Vehicle to Everything (V2X) communication, a deep reinforcement learning method is proposed to manage V2X spectrum resources. Firstly, the V2X communication model of a single vehicle to infrastructure link is established. Combined with the constraints such as frequency spectrum subband and transmission power, the optimization problem is constructed to maximize the comprehensive efficiency of V2X communication network. Secondly, considering the non-convexity of the optimization problem, the communication model can be regarded as a Markov decision process. Then, the Dueling-Deep Q Network (Dueling-DQN) algorithm is introduced to obtain the optimal spectrum subband selection and transmission power allocation strategy to maximize the comprehensive efficiency of V2X communication network. Finally, the simulation is carried out on tensorflow software platform to verify the performance of the proposed method. The simulation results show that Dueling-DQN algorithm can obtain higher link performance and V2X communication network efficiency compared with other algorithm.

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. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of C-ITS service on driving behavior, focusing on a pilot project in Daejeon–Sejong, South Korea. C-ITS, an advanced technology, enables bidirectional wireless communication between vehicles or infrastructure, allowing for real-time traffic data collection and dissemination. The study uses a unique analytical method, employing parallel processing techniques for variable extraction and a paired t-test to examine the short-term effects of C-ITS on driving behavior. Findings indicate a significant change in drivers' behavior, particularly in average speed, hard braking rate, severe deceleration rate, speeding rate, and excessive speeding rate, towards safer trends after receiving C-ITS warning services. Reductions in hard braking and severe deceleration were immediate after C-ITS service initiation, while a decrease in excessive speeding was observed after four months. Further research is needed to identify other potential influencing variables and provide an unbiased evaluation of C-ITS effectiveness. The study's implications highlight its role in promoting public acceptance of C-ITS-service-based cooperative autonomous driving strategies. [ABSTRACT FROM AUTHOR]

Summary: Internet of Vehicles (IoVs), the emerging trend of Internet of Things (IoTs), has undoubtedly become a promising trend to improve communication among vehicles on the roads. Vehicle‐to‐everything (V2X) communication that is based on 5G technology enables vehicle users to communicate and collaborate with each other to enhance road traffic efficiency and safety. Owing to the increased traffic load and restricted resources of existing network substructure, a channel that responds to the latency and reliability needs of V2X communication must be designed. Thereby, several intelligent spectrum allocation techniques have been proposed to improve the system's overall effectiveness. In this paper, we discuss the spectrum sharing issue of V2X communication in Device‐to‐Device (D2D)‐based cellular networks. We propose a new multi‐layer neural network (MLNN)‐based Resource Allocation and sharing approach (MNNRA) for D2D‐based V2X communications. According to the main advantages of MLNN, the proposed algorithm takes several profits by improving system performance while reducing computational complexity. Numerical analysis is presented to approve the effectiveness of our proposed solution in terms of network sum rate, packet reception ratio, resource utilization ratio, and time complexity. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) mobile networks fulfill the demands of critical applications, such as Ultra-Reliable Low-Latency Communication (URLLC), particularly in the automotive industry. Vehicular communication requires low latency and high computational capabilities at the network's edge. To meet these requirements, ETSI standardized Multi-access Edge Computing (MEC), which provides cloud computing capabilities and addresses the need for low latency. This paper presents a generalized overview for implementing a 5G-MEC testbed for Vehicle-to-Everything (V2X) applications, as well as the analysis of some important testbeds and state-of-the-art implementations based on their deployment scenario, 5G use cases, and open source accessibility. The complexity of using the testbeds is also discussed, and the challenges researchers may face while replicating and deploying them are highlighted. Finally, the paper summarizes the tools used to build the testbeds and addresses open issues related to implementing the testbeds. [ABSTRACT FROM AUTHOR]

Copyright of Study on Optical Communications / Guangtongxin Yanjiu is the property of Study on Optical Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the design an study of a DSRC/sub-6 GHz Koch fractal antenna single, two and four elements integrated with metamaterial structures, are proposed for the C-V2X and IEEE 802.11p/V2X. The design of automotive antenna is a challenging task due to the degradation of the MIMO antenna performance because of mutual coupling and cause spectral regrowth. Initially, a single antenna exhibits very good performance at 5.9-GHz. Subsequently, two elements and four MIMO elements were designed and analyzed which are placed orthogonally at the corner of the substrate introducing a diversity polarization. Meanwhile, to overcome the mutual coupling effects and improve the isolation between the radiating elements, two new different shaped left-handed metamaterials based on broadside and electrically coupled square split-ring resonator (SRR) with negative permeability and negative permittivity are suggested and their reflection properties are analyzed. However, the performances of various MIMO antenna configurations are investigated before and after inserting the metamaterial structures to assess the potential enhancements achieved. Finally, a further investigation concentrates on the effects of these metamaterial structures on the MIMO antenna radiation pattern and surface current density. In summary, the study demonstrated through simulations and measurements that the suggested MIMO antenna configurations, with the integration of metamaterial structures, exhibit improved performance, achieving a mutual coupling of -45 dB and demonstrating good MIMO diversity attributes

An increasing number of connected vehicles (CVs) driving together with regular vehicles (RVs) on the road is an inevitable stage of future traffic development. As accurate traffic flow state detection is essential for ensuring safe and efficient traffic, the level of road intelligence is being enhanced by the mass deployment of roadside perception devices, which is capable of sensing the mixed traffic flow consisting of RVs and CVs. In this background, we propose a roadside radar and camera data fusion framework to improve the accuracy of traffic flow state detection, which utilizes relatively more accurate traffic parameters obtained from real-time communication between CVs and roadside unit (RSU) as calibration values for training the back propagation (BP) neural network. Then, with the perception data collected by roadside sensors, the BP neural network-based data fusion model is applied to all vehicles including RVs. Furthermore, considering the changes of road environments, a dynamic BP fusion method is proposed, which adopts dynamic training by updating samples conditionally, and are applied to fuse traffic flow, occupancy and RVs speed data. Simulation results demonstrate that for CVs data and all vehicles (including RVs) data, the proposed dynamic BP fusion method is more accurate than single sensor detection, entropy based Bayesian fusion method and traditional BP fusion without training by CVs. It can achieve smaller error, and the accuracies of vehicle speed, traffic flow, and occupancy are all above 95%.

Radar detection and vehicle-to-everything (V2X) communication are two advanced driver-assistance system (ADAS) functions used to improve the safety of the road users and their driving experience. However, the proliferation of radars on vehicles and the functionalities expected for V2X cause several issues like interference and insufficient communication flow. To address these challenges and optimize the use of the electromagnetic spectrum, the mutualization of communication and radar detection functions in the same component using millimeter-wave (mmWave) is proposed. As orthogonal frequency-division multiplexing (OFDM) waveform seems to be the most suitable waveform to enable this cooperation, we propose to investigate the performance of OFDM radar compared to that of current frequency-modulated continuous wave (FMCW) radar. We simulate and analyse the signal-to-noise ratio (SNR) and the probability of detection (PD) of OFDM radar based on the parameters of a current mid-range automotive FMCW corner radar. We observe that FMCW radar has better SNR and PD than OFDM radar under these conditions. However, by applying a coherent integration scheme on receive, the results obtained show that good performance of OFDM radar can be achieved. To check the effect of the parameters used on the communication performance, we provide graphs of the Bit Error Rate (BER). These graphs show that the BER do not suffer from these parameters but rather that they can be beneficial to the communication.

Greenhouse gas (GHG) emissions on roads accounts for about three-quarters of the total emissions of the transport sector, and therefore it is vital to reduce them. South Korea plans to expand 30% of all roads to the cooperative intelligent transport system (C-ITS). This transport system uses vehicle-to-everything communication technologies to reduce GHG emissions by 2030. This study analysed the external benefits of expanding C-ITS using the preference data from a choice experiment. A mixed logit model is used to analyse the heterogeneity of the respondents' preferences. We find that the marginal willingness to pay for reducing traffic congestion, reducing traffic accidents, improving fuel economy, reducing particulate matter emissions and reducing GHG emissions are KRW 137.63/%, KRW 189.92/%, KRW 82.71/%, KRW 447.47/%, and KRW 81.03/%, respectively. It is expected that this result will provide basic data and implications for related transportation policies. • Analysing external benefit of expanding an advanced road transportation system. • The analysis applied a discrete choice model using data collected through surveys. • Marginal willingness to pay is assessed for external benefits in South Korea. • Analysis of public acceptance through scenarios based on national plans. • To secure the majority of public acceptance, less than KRW 6.5 billion is invested. [ABSTRACT FROM AUTHOR]

Emergency message (EM) dissemination is a significant process in vehicular ad hoc networks (VANETs) and plays a vital role in road safety. Nonetheless, EMs' dissemination while avoiding broadcast storms poses a considerable challenge. Moreover, selecting optimal cooperative forwarders is also challenging because of unreliability, high end-to-end delay, and packet loss caused by localization errors. Besides, the conventional broadcast-based schemes usually choose forwarder vehicles based on distance, neglecting the influence of links' qualities. To that end, we propose multi-hop EM dissemination (MEMD) through optimal cooperative forwarder in grid-based fifth-generation (5G) VANET environment. To ensure reliable EMs' dissemination, we incorporate vehicle-to-everything communication instead of the conventional short-range vehicle-to-vehicle communication. The proposed scheme obtains vehicle positions by integrating GPS measurements with a map-matching technique to avoid vehicle localization errors. Instead of a single parameter, the proposed scheme selects an optimal cooperative forwarder by using our proposed fitness function, which considers different mobility and social parameters. The proposed scheme categorizes EMs' dissemination into single-hop and multi-hop broadcasting to avoid unnecessary rebroadcasting and communication congestion in the network. Simulation results demonstrate that for varying density, the proposed scheme achieves an average increase of 12%, 9%, and 8.9% in coverage, and 7.95%, 4%, and 3.4% in PDR, and an average reduction of 13.5%, 7.2%, and 12.7% in E2E delay, and 20.9%, 11.3%, and 4% in rebroadcast ratio, as compared to LCL, REMD, and EMDZoI, respectively. [ABSTRACT FROM AUTHOR]

Recently, the Third Generation Partnership Project (3GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.

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.

In this article, a 4-port circularly polarized (CP) multiple-input multiple-output (MIMO) antenna at 5.9 GHz is presented for the vehicle to everything (V2X) communications. A novel hybrid decoupling structure consisting of circular ring parasites and defected ground structure (DGS) is used to reduce the mutual coupling between the MIMO elements. The circular ring parasites are incorporated between the antennas to reduce the mutual coupling between perpendicular antennas. Whereas, the DGS significantly reduces the mutual coupling between adjacent antennas. These circular ring parasites and DGS affect the electromagnetic field distribution and consequently reduce the mutual coupling. The effectiveness of the decoupling structure is explained through transmission coefficient and surface current distribution. The simulated and measured results show that the proposed MIMO antenna provides overlapping $\text{S}_{\mathrm {11 {}}}$ (−10 dB) and axial ratio (3-dB) bandwidth of 2.04% (5.82 – 5.94 GHz) with a peak gain of 7.68 dBic. Moreover, the fabricated MIMO antenna offers excellent diversity performance, isolation between antenna elements is very high (>34dB), envelope correlation coefficient (ECC) is lower than 0.001, and diversity gain is 9.99 dB, very close to the ideal value of 10 dB. Furthermore, the link budget for the proposed MIMO system is calculated to check the suitability for V2X communications which shows that the antenna can communicate between them over a distance of 1.5 kilometers with a 21 dBm of transmit power. Owing to these qualities, the proposed MIMO antenna can be an excellent choice for V2X MIMO communications.

Connected vehicles can benefit from sharing and merging their observations to develop a more complete understanding of the traffic scene and track traffic participants behind obstructions. Although vehicle-to-vehicle(V2V) communications provide a channel for point cloud data sharing, it is challenging to align point clouds from two vehicles with state-of-the-art techniques due to localization errors, visual obstructions, and differences in perspective. Therefore, we propose a two-phase point cloud registration mechanism to fuse point clouds which focuses on key objects in the scene where the point clouds are most similar and infer the transformation from those. Our system first identifies co-visible objects between vehicle views based on hyper-graph matching using multiple similarity metrics, and then refines the overlap region between co-visible objects across the views for point cloud registration. The system is evaluated based on both experimental and simulation data, which shows tremendous performance improvement when combing with state-of-art baselines.

Cooperative Adaptive Cruise Control (CACC) is a fundamental connected vehicle application that extends Adaptive Cruise Control by exploiting vehicle-to-vehicle (V2V) communication. CACC is a crucial ingredient for numerous autonomous vehicle functionalities including platooning, distributed route management, etc. Unfortunately, malicious V2V communications can subvert CACC, leading to string instability and road accidents. In this paper, we develop a novel resiliency infrastructure, RACCON, for detecting and mitigating V2V attacks on CACC. RACCON uses machine learning to develop an on-board prediction model that captures anomalous vehicular responses and performs mitigation in real time. RACCON-enabled vehicles can exploit the high efficiency of CACC without compromising safety, even under potentially adversarial scenarios. We present extensive experimental evaluation to demonstrate the efficacy of RACCON. [ABSTRACT FROM AUTHOR]

Cooperative-Intelligent Transportation System (C-ITS) safety applications depend on reliable location information timely exchanged by road users. Due to inter-vehicle communication delays and sampling frequency, there always exists a time gap between the state observation update time and safety decision time. Predicting the vehicle’s locations into a future time epoch common to both host and subject vehicles enables real-time collision detection. Current studies of vehicle positioning performance mostly focus on the accuracy and availability of vehicle navigation solutions at equal observation intervals. Location error propagation over the prediction time intervals and dependence on various factors is not much understood. In this paper, we analyzed how the accuracy of the location prediction degrades depending on prediction intervals and state estimate errors from the measurement updates. We adopted the Kalman Filter method to predict locations with two representative location data sets collected in real road environments. Results from a dual-frequency Global Navigation Satellite System (GNSS)/Real-time Kinematic (RTK) receiver show that the Root Mean Square Error (RMSE) of prediction locations grow from a few centimeters at the state updates to about 50 and 100 cm within the prediction intervals of 1 and 2 seconds, respectively. This implies that GNSS/RTK positioning capability is a prerequisite for C-ITS safety applications. The experimental results from a surveying-grade GNSS/Inertial Navigation System (INS) receiver show that the RMSE can remain within 10 cm for the prediction interval of 2 s. High-rate INS velocity measurements provide significant advantages in efficient control of the error growth of the predicted vehicle locations. [ABSTRACT FROM AUTHOR]

The autonomous vehicle (AVs) market is expanding at a rapid pace due to the advancement of information, communication, and sensor technology applications, offering a broad range of opportunities in terms of energy efficiency and addressing climate change concerns and safety. With regard to this last point, the rate of reduction in accidents is considerable when switching safety control tasks to machines from humans, which can be noted as having significantly slower response rates. This paper explores this thematic by focusing on the safety of AVs by thorough analysis of previously collected AV crash statistics and further discusses possible solutions for achieving increased autonomous vehicle safety. To achieve this, this technical paper develops a dynamic run-time safe assessment system, using the standard autonomous drive system (ADS), which is developed and simulated in case studies further in the paper. OpenCV methods for lane detection are developed and applied as robust control frameworks, which introduces the factor of vehicle crash predictability for the ego vehicle. The developed system is made to predict possible crashes by using a combination of machine learning and neural network methods, providing useful information for response mechanisms in risk scenarios. In addition, this paper explores the operational design domain (ODD) of the AV’s system and provides possible solutions to extend the domain in order to render vehicle operationality, even in safe mode. Additionally, three case studies are explored to supplement a discussion on the implementation of algorithms aimed at increasing curved lane detection ability and introducing trajectory predictability of neighbouring vehicles for an ego vehicle, resulting in lower collisions and increasing the safety of the AV overall. This paper thus explores the technical development of autonomous vehicles and is aimed at researchers and practitioners engaging in the conceptualisation, design, and implementation of safer AV systems focusing on lane detection and expanding AV safe state domains and vehicle trajectory predictability.

Fifth-generation (5G) mobile networks fulfill the demands of critical applications, such as Ultra-Reliable Low-Latency Communication (URLLC), particularly in the automotive industry. Vehicular communication requires low latency and high computational capabilities at the network’s edge. To meet these requirements, ETSI standardized Multi-access Edge Computing (MEC), which provides cloud computing capabilities and addresses the need for low latency. This paper presents a generalized overview for implementing a 5G-MEC testbed for Vehicle-to-Everything (V2X) applications, as well as the analysis of some important testbeds and state-of-the-art implementations based on their deployment scenario, 5G use cases, and open source accessibility. The complexity of using the testbeds is also discussed, and the challenges researchers may face while replicating and deploying them are highlighted. Finally, the paper summarizes the tools used to build the testbeds and addresses open issues related to implementing the testbeds.

In this paper we investigate the effects of connected automated vehicles on traffic patterns. We first experimentally study traffic patterns using two connected human-driven vehicles, which are equipped with vehicle-to-vehicle (V2V) communication, and a connected automated vehicle, which is able to respond to V2V information and control its longitudinal motion. Our experimental results indicate the long-range feedback may benefit traffic flow and that car-following models with delay are able to replicate the experimental results. The data fitted models are used in simulations for a 100-car network to study traffic dynamics with partial penetration of connected automated vehicles. [ABSTRACT FROM AUTHOR]

This paper explores the multifaceted domain of resource allocation (RA) in vehicle-to-everything (V2X) communication, emphasizing its pivotal role in advancing intelligent transportation systems (ITS) and beyond. While V2X communication stands as a cornerstone for ITS, enabling enhanced safety, efficiency, and automation in vehicular environments, it necessitates sophisticated RA strategies to address the dynamic and diverse demands of modern networks. As vehicle numbers grow, there is a rising need for more spectral resources to ensure efficient and dependable services. In areas with high vehicle concentrations, the demand for these resources intensifies to maintain swift, stable connectivity. Due to limited power, channel, and spectrum resources, their allocation is a critical challenge in V2X network. Efficient allocation of these resources is vital to prevent interference and ensure smooth communication. This study categorizes RA methods into graph-based, game theory-based, genetic algorithm-based, heuristic-based, optimization technique-based, machine learning-based, deep learning-based, and reinforcement learning-based approaches, each with its unique advantages and applicability in V2X contexts. We further delineate the evaluation metrics critical for assessing these methods, including throughput, latency, reliability, and energy efficiency, to provide a comprehensive context for comparison. Beyond ITS, the paper explores the broader implications of efficient RA in facilitating emergent V2X applications, such as emergency service provisioning, vehicle platooning, and speed harmonization. However, achieving optimal RA in V2X networks is fraught with challenges, including mobility and interference management, scalability, dynamic network topology, and the integration of heterogeneous technologies. The discussion extends to potential future directions, highlighting the importance of cross-layer design, integration of next-generation wireless technologies, and the adoption of artificial intelligence for adaptive and anticipatory resource management. By expanding the discourse beyond ITS, this paper aims to offer readers a holistic understanding of the current state, challenges, and forward-looking insights into RA for V2X communication. [ABSTRACT FROM AUTHOR]

The main goal behind radio resource management (RRM), in any conventional wireless networks, is to efficiently utilize the available resources. Critical and safety communications have very stringent quality of service requirements on reliability and availability. Consequently, the efficient use of resources becomes more compelling when it deals with this type of application. In this context, cellular vehicle‐to‐everything (C‐V2X) communications, enabled by cellular device‐to‐device (D2D), have recently drawn much attention. This is due to their applicability to road safety and traffic efficiency applications, which require information exchange among road users and road‐side entities. This emerging technology, referred hereinafter as LTE‐V or C‐V2X, supports two different communication modes: 3 and 4. Both of the two modes enable direct vehicle‐to‐vehicle (V2V) communications via PC5 interface. However, they differ on how the radio resources are allocated. Several approaches and algorithms are proposed in the literature to address the resource allocation issue. In this paper, a state of the art of the radio resource allocation strategies was made for LTE‐V technology and a qualitative description was carried out of these different schemes. We proposed a classification of these strategies into different categories. This classification identified the different strengths and weaknesses of each proposal. Up to our knowledge, several surveys concerning resource allocation are published in the context of LTE but very few of them focuses on both RRM and vehicular networks. [ABSTRACT FROM AUTHOR]

Cooperative driving is a promising approach to increase traffic efficiency and safety but requires high Vehicle-to-Everything (V2X) communication performance for coordinating and executing a cooperative maneuvers, especially in scenarios with high vehicle densities under congested channel conditions. Recent studies identified that content-agnostic congestion control mechanisms deployed in decentralized V2X networks maintain the radio communication performance under congested channel conditions but severely degrade the communication performance from the application perspective, such as for cooperative driving, as focused in this paper. Thus, we propose a relevance-aware resource allocation mechanism for decentralized V2X networks that prioritizes vehicles with relevant information and counteracts channel conditions. Our evaluation results show that our proposed approach maintains the radio communication performance and significantly increases the communication performance from the application perspective compared to content-aware resource allocation and content-agnostic congestion control mechanisms.

The technical exploration of safe automated driving has evolved from single vehicle intelligence to vehicular networking. Therefore, to a certain extent, the safety requirements have been translated into the requirements for network performance. Although Vehicle-to-Everything (V2X) communication technologies have developed decades of years, from the Dedicated Short-Range Communication (DSRC) to the Long Term Evolution V2X (LTE-V2X), the introduction of millimeter wave (mmWave) in the 5G era will achieve an unprecedented leap in V2X performance by empowering multi-gigabit data transmission with ultra-low latency. Meanwhile, the emergence of Software Defined Networking (SDN) enables centralized network management and allows flexible scheduling of communication resources to match V2X capacities. Their benefits for vehicular networks have been revealed by plenty of studies respectively. Nevertheless, no researchers investigate their combination to improve the safety of automated driving, especially lack of scalable prototype systems for proof-of-concepts (PoCs), not to mention the field test with real traffic. In this paper, we firstly propose a novel architecture, i.e. software-defined dynamic mmWave V2X network, to support cooperative perception. And then, necessary hardware and software platforms are introduced to compose prototype systems. Step by step, we conduct PoCs from the indoors to the outdoors, which demonstrate the network functions, the prototype scalability and most importantly, the significant enhancement to the vehicle perception for safe automated driving.

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 min respectively. [ABSTRACT FROM AUTHOR]

Cooperation between road users based on V2X communication has the potential to make road traffic safer and more efficient. The exchange of information enables the cooperative orchestration of critical traffic situations, such as truck overtaking maneuvers on freeways. With the benefit of such a system, questions arise concerning system failure or the abrupt and unexpected behavior of road users. A human-machine interface (HMI) organizes and negotiates the cooperation between drivers and maintains smooth interaction, trust, and system acceptance, even in the case of a possible system failure. A study was conducted with 30 truck drivers on a dynamic truck driving simulator to analyze the negotiation of cooperation requests and the reaction of truck drivers to potential system failures. The results show that an automated cooperation request does not translate into a significantly higher cooperation success rate. System failures in cooperative truck passing maneuvers are not considered critical by truck drivers in this simulated environment. The next step in the development process is to investigate how the success rate of truck overtaking maneuvers on freeways can be further increased as well as the implementation of the system in a real vehicle to investigate the reaction behavior of truck drivers in case of system failures in a real environment. [ABSTRACT FROM AUTHOR]

At present, most unmanned mining vehicles (UMVs) adopt batteries to meet the requirements of low power consumption in driving control systems, and saving energy is the key to increase the working time and production efficiency. Mobile edge computing (MEC) is an effective technology that can improve the driving performance, whereas reduces the power consumption caused by the UMV's CPU. However, sending more offloading tasks to MEC servers means higher wireless channel transmission power, and especially in mining areas, where the communication quality of wireless channels are easily deteriorated by dust, rocks and ravines. To solve this contradiction, this article firstly analyzes the UMVs' consumption of computational power and communicational power based on the proposed MEC architecture. Then, considering that flexible connection methods can reduce the end-to-end delay of offloading tasks and improve the use efficiency of link resources, a joint connection modes, uplink paths and computational tasks assignment method is proposed to reduce the power consumption under a strict delay constraint. Furthermore, a novel algorithm is presented to obtain the optimal parameters. Finally, through a simulation experiment, the effectiveness of this method in reducing the power consumption compared with the shortest path method is proved.

In vehicular ad hoc networks (VANETs), vehicles can communicate with other vehicles or devices through vehicle-to-X communication. However, with the rise of the Internet of Things, it is a real challenge to make the limited spectrum resource meet the increasing communication demands, i.e., the channel contention problem. To solve this problem, this paper proposes a strategy combining multi-hop forwarding through vehicles and dynamic spectrum access. Firstly, a group-based multi-hop broadcast protocol, G-hop, is proposed. G-hop classifies vehicles with similar characteristics such as moving speed and communication distance into same groups using the depth-first-search algorithm. Messages are forwarded within a group in priority and then across groups, which limits both the range and the number of relay vehicles, i.e., channel contenders. Further, we adopt deep reinforcement learning techniques to achieve dynamic spectrum access. We design a Global Optimization algorithm based on Experience Accumulation (GOEA) using deep reinforcement learning. In GOEA, a network structure combined with recurrent neural network and deep Q-network is proposed for learning the time-varying process, and then a reward method is applied to optimize the global utility. Vehicles that need to transmit messages select channels dynamically following the guidance of GOEA. The experimental results demonstrate that the G-hop protocol reduces the packet loss rate from 0.8 to about 0.1. Meanwhile, compared with Slotted Aloha and DQN, our GOEA algorithm reduces the collision probability and channel idle probability by 60%. Moreover, as the number of vehicles increases, the transmission success rate can be improved by 20%. [ABSTRACT FROM AUTHOR]

With the development of modern Intelligent Transportation System (ITS), reliable and efficient transportation information sharing becomes more and more important. Although there are promising wireless communication schemes such as Vehicle-to-Everything (V2X) communication standards, information sharing in ITS still faces challenges such as the V2X communication overload when a large number of vehicles suddenly appeared in one area. This flash crowd situation is mainly due to the uncertainty of traffic especially in the urban areas during traffic rush hours and will significantly increase the V2X communication latency. In order to solve such flash crowd issues, we propose a novel system that can accurately predict the traffic flow and density in the urban area that can be used to avoid the V2X communication flash crowd situation. By combining the existing grid-based and graph-based traffic flow prediction methods, we use a Topological Graph Convolutional Network (ToGCN) followed with a Sequence-to-sequence (Seq2Seq) framework to predict future traffic flow and density with temporal correlations. The experimentation on a real-world taxi trajectory traffic data set is performed and the evaluation results prove the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This work introduces a Multiple-Input Multiple-Output (MIMO) patch antenna operating from 5.84 to 6.01 GHz for Vehicle-to-everything (V2X) communication. The proposed MIMO antenna is fabricated on low cost FR4 substrate with compact dimensions of 1. 17 λ o × 1. 17 λ o × 0. 03 λ o (λ o is the wavelength at the smallest operating frequency). The study highlights the importance of MIMO technology in V2X communication. The antenna elements are placed orthogonally to improve the isolation. The mutual coupling is further reduced by employing the Defected Ground Structure (DGS) consisting of circular rings and swastika-shaped rectangular slits. The presented MIMO antenna has diverse radiation pattern characteristics ensuring the maximum coverage area with high radiation intensity. The maximum Envelope Correlation Coefficient (ECC) of 0.02 is obtained in the operating frequency range. Other MIMO parameters are also calculated and verified. The suggested antenna exhibits a peak realized gain of 6.3 dBi. These results strongly imply that the proposed antenna is suitable for the V2X communications. [ABSTRACT FROM AUTHOR]

Advances in technology are not only changing the world around us but also driving the wireless industry to develop the next generation of network technology. There is a lot of buzz building over the advent of 5G that will facilitate the entire planet through continuous and ubiquitous communication connecting anybody to anything, anywhere, anytime, and anyhow regardless of the device, service, network, or geographical existence. 5G will also prove to be a paradigm shift including high carrier frequencies with massive bandwidths, having a large number of antennas, and with an extreme base station and device densities. In this paper, we investigate the potential beneficiaries of 5G and identify the use-cases, where 5G can make an impact. In particular, we consider three main use-cases: vehicle-to-everything (V2X) communication, drones, and healthcare. We explore and highlight the problems and deficiencies of current cellular technologies with respect to these use-cases and identify how 5G will overcome those deficiencies. We also identified the open research problems and provide possible future directions to cope with those issues.