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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]

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]

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.

This paper studies the application of physical-layer network coding (PNC) in vehicle-to-everything communications to accommodate the time-critical nature of vehicular ad-hoc networks (VANETs). The idea can theoretically reduce the transmission latency by 50%, thus alleviating the short contact time issues caused by high-speed vehicle motion. Conventional studies of PNC primarily considered static networks. In highly mobile networks like VANETs, the carrier frequency offsets (CFOs) due to high-speed motion will lead to inter-carrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) systems. Moreover, the vehicular environment with time–frequency-selective channels further undermines accurate channel estimation for multiple users. It is also worth noting that the CFO that exists in OFDM modulated PNC cannot be completely eliminated through CFO tracking and equalization as in conventional point-to-point transmissions. These critical issues can significantly increase the bit error rate at the receiver. To address these challenges, this paper proposes an ICI-aware approach that jointly achieves accurate channel estimation, signal detection, and channel decoding. We express the channel estimation and detection and decoding as two optimization problems and resolve them with the expectation–maximization algorithm and the belief propagation algorithm, respectively. The proposed approach can efficiently mitigate the negative effect of ICI by exploiting both pilot and data tones in channel estimation, detection, and decoding. Both simulation and experiment are conducted to evaluate the proposed approach, and the results reveal that the proposed algorithm outperforms the benchmark that simply treats ICI as Gaussian noise.

In vehicle-to-everything (V2X) communication network, cellular device-to-device (D2D) communication can not only improve data rate and spectral utilization but also reduce the traffic load and power consumption. However, cellular D2D-based V2X technology has also a potential deficiency to meet various requirements of V2X communication, particularly in energy efficiency (EE). Power allocation provides an important approach to optimize the EE. In this paper, a new approach for power allocation with EE optimization (EEO) is proposed in cellular D2D-based V2X communication network. The mathematical framework of the new approach is formulated and proved and an algorithm is also proposed. Numerical simulation results are provided to demonstrate the feasibility of the proposed algorithm and the superiority over existing well-known algorithms. [ABSTRACT FROM AUTHOR]

This paper studies a receive directional beam control method in vehicle-to-everything (V2X) wireless communication system. First, we reveal that for a given receive beam, the channel coherence time of fast varying vehicle channels decreases as time goes by. Then, we propose a novel adaptive receive beam control method termed dynamic beam management (DBM), tailored to this channel characteristic, which adaptively selects one of two predefined actions, i.e., channel estimation and beam alignment. The channel estimation is a process of reacquiring channel state information using known pilot signals for coherent detection toward a given receive beam direction. The beam alignment is a kind of analog beam selection method, which steers the direction of the receive beam to attain higher communication performance. Since the beam alignment must search through large dimension, it costs more time compared to the channel estimation. Therefore, given different time overheads of channel estimation and beam alignment, new design criteria are required to decide when each action should be conducted in V2X communications. The proposed DBM figures out the time point when to re-estimate the channel and to realign the receive beam, taking into account their gains and overheads. Furthermore, we show that although a narrow beam has a merit of high power gain, too narrow beam is vulnerable to the pointing error, and reduces channel coherence time, and thus we numerically find the optimal receive beamwidth. The effectiveness and feasibility of the proposed DBM are underpinned by both analytic and simulation results. [ABSTRACT FROM AUTHOR]

Dedicated short-range communication (DSRC), which is an essential part of vehicle-to-vehicle/infrastructure communication to enhance the road safety, is often characterized by the IEEE 802.11p standard. Numerous works have been done on the DSRC safety message broadcasting performance for highway scenario. However, up to date, no work has been done on the performance of the IEEE 802.11p safety message broadcasting for the road-intersection scenario in urban environment. An intersection scenario is different from a highway scenario. In a highway scenario, it is often been considered that all the vehicles have the same communication range as well as the same carrier sensing range. However, this is not the case for the intersection scenario, where there exist a lot of obstructions, such as buildings and urban canyons. In an intersection, the communication and carrier sensing ranges of a vehicle heavily depend on the location of that vehicle. This paper first analyzes and then provides solution on improving the broadcasting performance of the DSRC safety message at an intersection while considering the IEEE 802.11p enhanced distributed channel access mechanism. To facilitate different communication and carrier sensing ranges of different vehicles, we divide the intersection region into few parts/areas based on an empirically validated path loss model. We present an analytical study on the packet reception rates and channel access delay which is applicable for the different positions of transmitters and receivers in the intersection areas. The analytical results are verified by the NS-3 simulation. From the results, we find that the overall delivery ratio is very poor when the broadcasting vehicle is not close enough to the intersection-center. To improve the overall broadcast performance of such scenarios, we employ a road side unit (RSU) at the intersection-center to relay the safety messages once. We show the performance improvement via relaying while first using omni-directional and then using special sector antennas, the so-called bidirectional antenna, at the RSU. From the results, it is shown that relaying with the omni-directional antenna gives moderate improvement on the overall delivery ratio, while a significant improvement can be achieved by relaying with sector antenna.

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.

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.

The technology of static user-centric virtual cell (VC) has been designed in the fifth-generation (5G) ultra-dense networks (UDNs) for alleviating both frequent handover and inter-cell interference. However, to provide the user-centric services, the fairness of resource management must be involved when the common vehicular-to-X (V2X) messages are multicast to the vehicle groups. In this paper, a dynamic user-centric virtual cell (DUVC) scheme is proposed for updating adaptively the VC through the mobile tracking of the vehicles. Furthermore, an approximation algorithm is proposed for solving the max-min-fair problem of resource management in V2X communication in order to better support V2X services throughout the service VC. Numerical results are provided for demonstrating that the proposed DUVC scheme is suitable for implementing in the V2X communication. Finally, the proposed algorithm is shown to outperform three other existing algorithms in terms of fairness of resource management. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything (V2X) communication has been widely studied in recent years for the rapid development of the vehicular traffic. In this paper, a V2X communication system integrating the probabilistic shaping (PS) bitloading multi-input-multi-output (MIMO) visible light communication (VLC) and the millimeter wave radar is proposed. The PS bitloading algorithm utilizes the full spectrum resource of the LED headlights, and the MIMO configuration enhances the performance of the VLC system. Furthermore, the linear and nonlinear interpolation method makes it possible to calculate the SNR at every point from the SNR measured at several discrete calibration points. The SNR table enables the radar to guide the VLC according to the distance, achieving the highest achievable information rate (AIR) at every point. The system supports the V2X communication beyond 1.1 Gb/s up to 5 m. This is the first VLC based V2X sensing and communication system, and the AIR in the system is 1.8484 Gb/s. As far as we know, it is the highest data rate reported in the LED headlight based VLC system. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Network (VANET) simulators have been gaining widespread attention in recent years and are crucial for conducting vehicular research. The fact that VANET simulators can simulate near real life scenarios to evaluate driver-safety applications without endangering human lives vastly contributes to their popularity. In this paper, we present the VEhicular NeTwork Open Simulator (VENTOS), a closed-loop VANET simulator that combines the capabilities of both communication network and vehicular traffic simulators. More specifically, VENTOS comprises of Simulation of Urban Mobility (SUMO) and Objective Modular Network Testbed in C++ (OMNET++). As an alternative to current proprietary Vehicular Ad-hoc Network (VANET) simulators, VENTOS is a free, open-source simulator that is designed for vehicular traffic flow analysis and can incorporate new control logic, such as intelligent traffic controller, collaborative driving, dynamic routing, and self-driving capability. VENTOS also allows for V2X (Vehicle-to-Vehicle or Vehicle-to-Infrastructure) communication through dedicated short-range communication (DSRC) integration. We discuss about the VENTOS architecture as well as various example applications that use VENTOS. [ABSTRACT FROM AUTHOR]

To overcome the limitations of Dedicated Short Range Communications (DSRC) with short range, non-supportability of high density networks, unreliable broadcast services, signal congestion and connectivity disruptions, cellular vehicle-to-everything (C-V2X) communication networks, standardized in 3rd Generation Partnership Project (3GPP) Release 14, have been recently introduced to cover broader vehicular communication scenarios including vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P) and vehicle-to-infrastructure/network (V2I/N). In C-V2X, vehicles can directly communicate over PC5 based dedicated sidelinks called direct mode or V2V communication. However, high vehicle densities may require reuse of cellular spectrum for V2V. Moreover, infrastructure mode communication through V2I/N links can augment V2V communication by enhancing communication range and reliability for enhanced safety along with consistent performance under traffic congestions. Motivated by the stringent connection reliability, spectral efficiency, and coverage requirements in C-V2X, this paper presents the first comprehensive and tractable analytical framework for performance of C-V2X networks over shared V2V and cellular uplink channels, where the transmitting vehicles can deliver their information via infrastructure or direct mode, based on their distances, propagation environments and the bias factor. By practically modeling the vehicles on the roads using the doubly stochastic Cox process and the base-stations, we derive new association probabilities, new success probabilities of infrastructure and direct mode, and overall success probability of the C-V2X communication over shared channels, which are validated by the simulations results. Our results reveal the benefits of our proposed model (possibility of selecting both direct and infrastructure modes over shared channels) compared to V2V network in terms of success probability. [ABSTRACT FROM AUTHOR]

Recently, new research activities have emerged for controlling traffic. Since future vehicles will travel autonomously and communicate with their surrounding environment, they will then be able to negotiate the right-of-way at intersections [cooperative intersection management (CIM)] as well as reserve their itinerary (road reservation). Both concepts, i.e., CIM and road reservation, are very promising for relieving traffic congestion. The scope of this paper is to prevent deadlock under real-time conditions, by taking advantage of these two concepts. This paper presents an appropriate graph to model the network of intersections and a sufficient condition for obtaining deadlock-free traffic with a specified route from their origin to their destination. In addition, it proposes a hierarchical approach in which a network server, intersection servers, and vehicles contribute to improving the traffic condition. Simulations were performed on a network of 25 interconnected intersections, as well as, on a real urban network to prove the effectiveness of the model. The results are presented and discussed. [ABSTRACT FROM AUTHOR]

Managing resources in dynamic vehicular environments is a tough task, which is becoming more challenging with the increased number of access technologies today available in connected cars (e.g., IEEE 802.11, LTE), in the variety of applications provided on the road (e.g., safety, traffic efficiency, and infotainment), in the amount of driving awareness/coordination required (e.g., local, context, and cooperative awareness), and in the level of automation toward zero-accident driving (e.g., platooning and autonomous driving). The open programmability and logically centralized control features of the software-defined networking (SDN) paradigm offer an attractive means to manage communication and networking resources in the vehicular environment and promise improved performance. In this paper, we enumerate the potentials of software-defined vehicular networks, analyze the need to rethink the traditional SDN approach from theoretical and practical standpoints when applied in this application context, and present an emulation approach based on the proposed node car architecture in Mininet-WiFi to showcase the applicability and some expected benefits of SDN in a selected use case scenario.