Over-the-Air (OTA) updates constitute a fundamental service for modern vehicles, providing the critical capability of update-ability upon which many other use cases depend. While previous studies have investigated technological advancements to improve their efficiency and adoption, such as ad-hoc communication schemes and edge caching, few have evaluated their actual impact on production communication networks. This study presents the first comprehensive numerical evaluation of how large-scale automotive OTA updates affect network congestion at the radio-access network (RAN) level in urban environments. Leveraging empirical network data and simulated urban vehicle mobility data from the city of Barcelona, Spain, a numerical evaluation is provided to determine the impact of massive OTA updates in physical resource block (PRB) usage and temporal network congestion variations. Specifically, a massive OTA update scenario to thousands of vehicles in an urban environment is considered. Evaluation results demonstrate substantial variations in PRB usage during OTA updates, both across cells and over time. In the worst affected cells, PRB load increases by up to 30%, while other cells with similar vehicle densities remain largely unaffected. Moreover, the temporal distribution of vehicle connectivity shows that peak-hour usage of vehicle and background traffic is highly correlated, further increasing the load on already congested cells. These findings demonstrate the need for adaptive OTA update planning strategies to alleviate network congestion while ensuring timely updates for all vehicles.

Vehicle-to-Everything (V2X) communications enable the exchange of information among vehicles to improve road safety and traffic efficiency. As V2X deployments progress, vehicles are expected to support an increasing number of V2X services, often characterized by different priorities and data transmission requirements. However, existing V2X congestion control mechanisms primarily focus on maintaining channel load stability and fairness at the vehicle level, typically assuming homogeneous traffic demands. This paper proposes a demand- and priority-aware adaptive congestion control technique that explicitly accounts for heterogeneous and time-varying V2X service requirements. The results demonstrate that the proposed technique improves the satisfaction of V2X service demands while maintaining stable channel operation. The proposed technique aligns with current V2X standards, preserving backward compatibility while providing enhancements consistent with ongoing standardization activities.

Scalable Vehicle-to-Everything (V2X) networks are key to support the large-scale deployment of connected and automated mobility. However, the scalability of V2X networks is currently challenged by the limitations of existing V2X communication paradigms, which prioritize the reliable and timely delivery of the transmitted information over a careful message content selection - an approach that can potentially lead to the transmission of unnecessary information and an inefficient usage of communication resources. Semantic and task-oriented V2X communications have recently been proposed to address these scalability challenges by focusing on the content of the transmitted messages, particularly on its relevance to the intended receivers. In this paper, we numerically demonstrate that semantic and task-oriented V2X communications can substantially improve the scalability of V2X networks, increasing by up to a 4.1x factor the number of supported vehicles under high-density conditions. In addition, we show that semantic and task-oriented V2X communications can also decrease the inter-reception time between consecutive messages by up to 67

Maneuver coordination is a key enabler of connected and automated driving, allowing vehicles to negotiate and execute maneuvers that would otherwise be difficult, inefficient or unsafe. Existing approaches and use cases typically assume coordination with a single predefined target vehicle, which limits the number of coordination opportunities. This paper introduces a maneuver coordination approach based on multi-target selection, which allows a vehicle to identify and select among multiple potential coordination vehicles for a given maneuver. Multi-target maneuver coordination does not require modifications to the maneuver execution logic or to the underlying coordination protocol. Instead, it extends the decision-making process preceding coordination, enabling vehicles to exploit a broader set of feasible cooperative interactions. Results show that multi-target maneuver coordination significantly increases triggered and successfully executed coordinations while maintaining a low computational cost, as the proposed approach achieves these gains without requiring the analysis of a large number of potential target vehicles. These improvements preserve coordination success rates while enabling earlier maneuver initiation.

Trajectory prediction allows autonomous vehicles to anticipate the future behavior of surrounding objects (or agents) and, accordingly, maximize the safety and efficiency of their driving. State-of-the-art Transformed-based interaction-aware trajectory prediction models, which rely on attention mechanisms to capture multi-agent interactions and maximize prediction accuracy, are commonly trained and evaluated on long-range high-quality datasets. These datasets are typically obtained by aggregating data from multiple vehicles or drones and removing any object detection or tracking noise offline. Yet, information about a surrounding object's state (its position, speed, heading) is far from being noiseless in real-world deployments. Object state estimation is affected by perception uncertainties and localization errors that can be particularly large for objects received via Vehicle-to-Everything (V2X) communications. In this paper, we analyze the impact of noisy object state information on the trajectory prediction accuracy of a state-of-the-art Transformer-based interaction-aware trajectory prediction model. Our study demonstrates that trajectory prediction accuracy can rapidly deteriorate as the noise intensity increases. Numerical results show that the prediction accuracy can reduce by a 1.3x factor under small noise levels and by as much as a 3.9x factor under the highest (yet realistic) noise conditions. These findings reveal the strong sensitivity of trajectory prediction models to noisy data, underscoring the need for more realistic training and evaluation datasets as well as noise mitigation strategies.

Next generation wireless networks must sustain deterministic service levels for time-sensitive closed-loop applications. Flexible duplexing (FD) is an efficient solution to support these services, as it enables simultaneous uplink (UL) and downlink (DL) transmissions over orthogonal resources within the same band. However, simultaneous UL and DL transmissions can create conflicts that degrade performance due to interference from in-band emissions (IBE) and UL-to-DL cross-link interference (CLI). In this paper, we propose to use traffic forecasting and predictive scheduling to mitigate UL/DL conflicts in FD. Our proposal exploits traffic predictions to increase the likelihood of scheduling CLI-free UL and DL transmissions, and leverages spatial diversity to minimize the impact of unavoidable conflicts. Results show that the proposed scheme reduces UL/DL scheduling conflicts and improves the SINR of conflicted transmissions by more than 5 dB. This leads to gains of over 40

This paper presents FORESEE, a novel cooperative lane change model for connected and automated driving. FORESEE leverages Vehicle-to-Everything (V2X) data to anticipate traffic conditions and effectively organize lane changes. Specifically, it uses V2X data to organize vehicles into lanes based on their desired speeds, which helps to homogenize traffic flow and reduce disturbances caused by speed differences among vehicles within the same lane. The study demonstrates that implementing cooperative lane changes with FORESEE enhances average vehicle speed and energy efficiency compared to non-cooperative lane changes, which typically rely on short term and local information about the ego vehicle and its immediate neighbors. This is achieved through fewer but more effective lane changes. Additionally, vehicles can maintain speeds closer to their desired speeds, resulting in fewer fluctuations in speed and acceleration and enhanced driving comfort. Moreover, cooperative lane changes can better manage road traffic disturbances, such as obstacles, by anticipating traffic conditions and organizing lane changes ahead. FORESEE serves as a valuable framework for the future design and testing of V2X-based maneuver coordinations as their effectiveness depends on how vehicles change lanes and their ability to plan and organize maneuvers in consideration of the upcoming traffic conditions.

Maneuver coordination can improve traffic safety and efficiency by enabling cooperation among vehicles during maneuver execution. Within the context of the Internet of Vehicles (IoV), such coordination is a key enabler for advanced connected and automated mobility. However, realizing this potential in complex traffic environments remains challenging due to unexpected traffic changes and interactions among vehicles. This study presents a complete design and implementation of maneuver coordination. It identifies key challenges for effective maneuver coordination and proposes solutions and design configurations to improve or configure its effectiveness. Our analysis demonstrates that the effectiveness of maneuver coordination is strongly influenced by state synchronization among the vehicles involved in a maneuver, traffic prediction accuracy, negotiation timing, and the ability to cope with unexpected traffic changes. We evaluate the proposed maneuver coordination design for cooperative lane changes and analyze how different configurations affect coordination effectiveness. The results show that more permissive configurations can increase the number of successful coordinations, whereas more conservative configurations tend to increase the percentage of successful coordination attempts. This reveals a clear trade-off between coordination availability and coordination reliability that should be considered when configuring maneuver coordination strategies.

Maneuver coordination is essential for cooperative connected automated driving, enabling vehicles to negotiate maneuvers and interactions through V2X communication. While prior work has largely focused on how to initiate and execute coordinations, considerably less attention has been given to how ongoing coordinations should be terminated when they become unsuitable. This paper introduces the first complete design and implementation of maneuver coordination cancellation, including a state machine, message set, and decision-making logic. Our evaluation shows that cancellation significantly reduces the time vehicles spend in coordinations that cannot succeed, allowing them to become available for new maneuvers sooner. This increases the number of triggered coordinations and improves the number of successful maneuver coordinations. Overall, the study demonstrates that maneuver coordination cancellation improves cooperative driving, and establishes a foundation for further refinements that can enhance the efficiency and robustness of connected automated driving.

The design of communication systems has traditionally focused on the reliable and timely delivery of data. However, the scalability challenges faced by the evolution to a 6G-driven society demand new communication paradigms that carefully curate the content being transmitted. This paper envisions a joint semantic and task-oriented communication paradigm where Connected and Autonomous Vehicles (CAVs) transmit only the information necessary to convey the desired meaning that is relevant to the intended receivers based on the communication context. The V2X domain offers a unique environment for the development of the envisioned semantic and task-oriented communications paradigm, as CAVs are native semantic devices, and the V2X domain is rich in contextual information. This contextual information can be leveraged to estimate the relevance that information may have for the intended receivers. We illustrate and quantitatively evaluate the potential benefits of semantic and task-oriented V2X communications. Numerical results show that by focusing on the transmission of the most relevant information for the intended receivers, semantic and task-oriented V2X communications can achieve a two-fold improvement in communication efficiency, which will significantly benefit the scalability of V2X networks.

This paper examines the critical role of intent-sharing in enabling effective maneuver coordination for connected and automated vehicles (CAVs). Successful maneuver coordinations require vehicles to accurately know other vehicles' driving intentions. Intent-sharing can be achieved by the remote vehicles directly communicating their plans with the ego vehicle, as opposed to the ego vehicle predicting the trajectory on the remote vehicles’ behalf. In this paper, we investigate the potential of intent-sharing on maneuver coordination effectiveness by quantifying the percentage of successful coordinations. We analyze the potential of intent-sharing by comparing its effectiveness for coordinated lane changes in a highway scenario with the effectiveness of a trajectory prediction method based on current kinematic data. Our analysis demonstrates in two scenarios substantial improvements in maneuver coordination when CAVs have direct access to the nearby vehicles’ driving intentions through intent sharing. These findings highlight the importance of including intent-sharing in the maneuver coordination protocol.

Connected and automated driving introduces a myriad of new V2X services, such as cooperative perception and maneuver coordination, that significantly increase the channel load and require multi-channel V2X operation. Policies for Multi-Channel Operation (MCO) and congestion control are therefore essential for simultaneously supporting multiple V2X services across several channels. In this context, this paper presents the design, implementation, and extensive validation of a Facilities layer V2X congestion control solution for multi-channel operation integrated into an ETSI-compliant Cooperative Intelligent Transportation Systems (C-ITS) protocol stack. Our approach dynamically adapts transmission parameters based on real-time channel conditions and the priorities and requirements of the V2X services operating in a C-ITS station. By employing a Traffic-Class based proportional fairness strategy, the solution allocates available communication resources among multiple V2X services, effectively responding to varying channel loads in real time. Scalable experimental results in a virtualized environment demonstrate that our solution meets ETSI Release 2 requirements while bridging the gap between simulation-based evaluations and real-world testing, accounting for hardware limitations and processing delays. This work lays a robust foundation for scalable and congestion-aware C-ITS testing and validation prior to real world deployments. This paper makes our code publicly available so that other researchers can replicate our study and further explore MCO solutions for V2X communications.

Automated Vehicle Marshalling (AVM) is an innovative technology poised to transform the automotive industry by enabling automated vehicles to be wirelessly controlled within geofenced areas while ensuring guaranteed Functional Safety (FuSa). Significant investments from major automakers and suppliers are driving the advancement of this SAE Level 4 driverless technology. Standardization is a crucial prerequisite for the widespread deployment of AVM, requiring collaboration among academia, international standardization bodies (e.g., ISO, ETSI, SAE), and industry consortia such as VDA and 5GAA. This article outlines the current standardization efforts and deployment status of AVM, elaborates on core vehicle motion control mechanisms, FuSa principles, communication interfaces, message formats, and spectrum requirements. Through this comprehensive examination, the article aims to address how AVM can be seamlessly integrated into future Intelligent Transportation System (ITS) ecosystems. As the automotive industry progresses toward greater automation and connectivity, AVM represents a major advancement in automated vehicle maneuvering and control for manufacturing plants, logistics depots, parking facilities, and charging stations.

Connected Automated Vehicles (CAVs) utilize their onboard sensors to perceive the environment. The perception range and accuracy can be affected by adverse weather or non-line-of-sight conditions. Cooperative perception or sensor sharing can overcome these limitations by enabling CAVs to exchange sensor data, thus collectively enhancing their perception capabilities. Previous studies have shown the potential of cooperative perception, but limited attention has been given to the fusion of V2X data received through cooperative perception messages with onboard sensor information. The fusion process can be influenced by the quantity and quality of the V2X data. An increased volume of V2X data can reduce uncertainty in the perceived environment; however, when the data is noisy, it may compromise the accuracy of the fusion results. This study investigates the fusion of onboard sensor and V2X data in cooperative perception, and demonstrates that while perception can significantly improve as the V2X penetration rate increases, it can introduce a significant number of false positives if V2X data is not highly accurate. False positives result in the detection of ghost objects that do not actually exist. These ghost objects can, in turn, compromise safety and driving efficiency. Our analysis found that false positives or ghost objects can appear even with accurate V2X data. These findings highlight the challenges in cooperative perception and the importance of developing robust data fusion methods to enhance the reliability of cooperative perception. This is particularly relevant in light of ongoing standardization efforts, such as ETSI TS 103 324 on collective perception.

Automated vehicles rely on onboard sensors to perceive their surroundings and navigate autonomously. However, sensor performance may degrade under adverse weather conditions or when line-of-sight is obstructed. Cooperative perception (or collective perception) is expected to mitigate these limitations by enabling Connected and Automated Vehicles (CAVs) to share sensor data and collaboratively enhance situational awareness. Several studies have analyzed the potential of cooperative perception, yet the fusion of V2X data with information from onboard sensors has received limited focus. V2X data may contain errors that affect the quality of the fused data, and hence the effectiveness of cooperative perception. This study analyzes the impact of sensing measurement errors, V2X packet losses, and GNSS inaccuracies on the effectiveness of cooperative perception. The results highlight the potential of cooperative perception to enhance perception levels and range compared to using onboard sensors alone. However, they also identify key challenges related to the generation of ghost vehicles during the fusion process, which must be addressed to prevent V2X data from introducing additional errors when fused with onboard sensor data.

Connected and automated vehicles can leverage V2X communications to coordinate their maneuvers. Maneuver coordination is expected to improve traffic efficiency and safety, but the design of maneuver coordination is a challenging task in complex traffic scenarios, as maneuvers affect not only the involved vehicles but also nearby traffic. This study introduces a reference state machine for the design of maneuver coordination. Furthermore, we identify and analyze the challenges that maneuver coordination may encounter. We quantify the relevance of each challenge and propose a set of countermeasures to enhance the robustness and effectiveness of maneuver coordination.

Vehicles and road infrastructure are starting to be equipped with vehicle-to-everything (V2X) communication solutions to increase road safety and provide new services to drivers and passengers. In Europe, the deployment is based on a set of Release 1 standards developed by ETSI to support basic use cases for cooperative intelligent transport systems (C-ITS). For them, the capacity of a single 10 MHz channel in the ITS band at 5.9 GHz is considered sufficient. At the same time, the ITS stakeholders are working toward several advanced use cases, which imply a significant increment of data traffic and the need for multiple channels. To address this issue, ETSI has recently standardized a new multi-channel operation (MCO) concept for flexible, efficient, and future-proof use of multiple channels. This new concept is defined in a set of new specifications that represent the foundation for the future releases of C-ITS standards. The present article provides a comprehensive review of the new set of specifications, describing the main entities that extend the C-ITS architecture at the different layers of the protocol stack. In addition, the article provides representative examples that describe how these MCO standards will be used in the future and discusses some of the main open issues arising. The review and analysis of this article facilitate the understanding and motivation of the new set of Release 2 ETSI specifications for MCO and the identification of new research opportunities.

The support of Cooperative Intelligent Transport Systems (C-ITS) services requires seamless interoperability between involved stakeholders. To this aim, the 5G Automotive Association has recently endorsed a Vehicle-to-Network-to-Everything (V2N2X) architecture trialed at national initiatives to support road traffic management V2X services. The architecture enables interoperability at the application level through a cloud-federated Information Sharing Domain (ISD) that supports data sharing and interoperability among stakeholders. This study analyses the possibility to support critical and latency-sensitive V2X services using 5G-based Vehicle-to-N etwork-to- Vehicle (V2N2V) communications over the federated cloud-based V2N2X architecture. The analysis considers the intersection collision avoidance (ICA) service as a case study and scenarios involving multiple Mobile Network Operators (MNOs) and Original Equipment Manufacturer (OEM) clouds. We show that the ICA requirements can be supported, provided connections with controlled latencies (under Service Level Agreements or SLAs) are established between the OEM clouds and the ISD. However, the small tolerance to latency variations can compromise the support of the critical and latency-sensitive V2X services over the federated cloud-based V2N2X architecture, and solutions are necessary to ensure the scalability of the system.

Teleoperated driving (ToD) enables the remote driving or control of vehicles. For this purpose, vehicles must transmit video feeds to the ToD control center so that the remote operator is fully aware of the driving conditions and can safely control the vehicle. 5G (and beyond) networks are fundamental for the deployment of ToD as they can provide the low latency, reliable and broadband connection necessary to connect the vehicle and ToD control center. However, it is unclear whether common 5G network architectures and configurations are well-suited to support the simultaneous teleoperation of multiple vehicles with demanding uplink bandwidth, as current networks are mainly configured to support mobile broadband services. This paper demonstrates that MEC or edge-based 5G networks are better suited to support and scale the ToD service than centralized networks, and quantifies the bandwidth required to simultaneously teleoperate multiple vehicles under various 5G network architectures and configurations, including different duplexing modes and TDD frame structures. Finally, the study shows that the configuration of the control channels can help mitigate the impact that the processing time of the video feeds has on the capacity to support and scale the ToD service.

Connected Automated Vehicles (CAVs) will use multiple V2X services to support connected and automated driving functions. The bandwidth required to support such services will augment as CAVs are gradually deployed. It is therefore important to accurately estimate the spectrum requirements to anticipate possible scalability challenges ahead. Current estimations consider a simplified modeling of the transmitter as well as context factors such as the number of vehicles in the communication range. Moreover, they do not accurately model if the Quality of Service (QoS) of the considered V2X services is satisfied or not. This study progresses the state of the art with a novel analytical model that quantifies the bandwidth required to support multiple V2X services. The model considers the impact of the vehicular context, the transmission parameters and the communication requirements to take into account the QoS at the receiver. This is important since adapting the transmission parameters can reduce the channel load but also impacts the probability to correctly receive each packet and therefore the bandwidth required to guarantee a target QoS at the receiver. The proposed model can be adapted to different wireless technologies and messages, but is applied in this study to quantify the bandwidth required by LTE-V2X to support the transmission of CAMs, CPMs and MCMs. The study demonstrates the scalability challenges ahead to support multiple V2X services.