arXiv subjects

Miguel Sepulcre

Publications and source records attributed to Miguel Sepulcre.

At least 19 recordsLinked to original sources

arXiv2026

How the Fusion of Onboard Sensors and V2X Data can Improve (or not) the Cooperative Perception of Connected Automated Vehicles

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.

cs.NI↗

arXiv2026

Fusion or Confusion? Potential and Challenges in Fusion of Onboard Sensors and V2X Data in Cooperative Perception

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.

cs.NI↗

arXiv2026

Support of Teleoperated Driving with 5G Networks

Teleoperated driving (ToD) can support autonomous driving under complex or unexpected traffic scenarios that an autonomous vehicle may not understand or be able to handle. In ToD, autonomous vehicles transmit video feeds and perception data to the remote control center. The operator uses this data to understand the driving environment and remotely control the vehicle that can take over the control once the scenario is resolved. ToD requires reliable and low latency communications between the vehicle and the ToD control center. This study analyzes the feasibility to support ToD with 5G networks. The study demonstrates that the feasibility strongly depends on the bandwidth and the Time Division Duplexing (TDD) frame structure that conditions how the bandwidth is distributed between uplink and downlink transmissions. The study also shows that scaling the number of 5G-supported ToD vehicles requires the vehicles to reduce the video bitrates. The study also shows that traditional centralized 5G network deployments may be challenged by some of the most stringent ToD latency requirements due to the latency introduced by the Internet connection to the ToD control center.

cs.NI↗

arXiv2026

FORESEE: A Cooperative Lane Change Model for Connected and Automated Driving

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.

cs.NI↗

arXiv2026

Importance of Intent-Sharing for V2X-based Maneuver Coordination

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.

cs.NI↗

arXiv2026

When Cooperation Should End: Maneuver Coordination Cancellation for Connected Automated Driving

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.

cs.NI↗

arXiv2026

Multi-Target Maneuver Coordinations: Unlocking Coordination Opportunities in Connected Automated Driving

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.

cs.NI↗

arXiv2026

Mind the Noise: Sensitivity of Transformer-based Interaction-Aware Trajectory Prediction Models to Noisy Data

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.

cs.AI↗

arXiv2026

The Search for Relevance: A Context-Aware Paradigm Shift in Semantic and Task-Oriented V2X Communications

The design of communication systems has traditionally prioritized the reliable and timely delivery of data. However, the scalability challenges faced by the evolution towards a data-driven hyper-connected society and economy demand new communication paradigms that carefully curate the content being transmitted. This paper proposes a joint semantic and task-oriented communication paradigm where connected devices transmit only the information necessary to convey the desired meaning that is relevant to the intended receivers, based on their context. We qualitatively and quantitatively analyze the potential benefits of the proposed semantic and task-oriented communication paradigm in the Vehicle-to-Everything (V2X) domain. The V2X domain offers a unique environment for the development and deployment of semantic and task-oriented V2X communications, as it is rich in contextual information and Connected and Autonomous Vehicles (CAVs) are native semantic devices. The qualitative analysis focuses on a cooperative perception use case and shows how semantic and task-oriented V2X communications can reduce the amount of information transmitted by each vehicle without compromising the situational awareness of its intended receivers. The quantitative analysis numerically demonstrates that semantic and task-oriented V2X communications can achieve a two-fold improvement in communication efficiency which can significantly benefit the scalability of future V2X networks.

cs.NI↗

arXiv2026

Predictive and Spatially Aware Scheduling in Flexible Duplexing for Deterministic Communications

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% in the number of successfully completed transmissions compared to reference FD schemes.

cs.NI↗

arXiv2026

Semantic and Task-Oriented V2X Communications: Pushing the Limits of V2X Networks Scalability

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% and lead to a twofold increase in the probability of successfully delivering all required relevant information to the intended receivers.

cs.NI↗

arXiv2026

Demand- and Priority-Aware Adaptive Congestion Control for Heterogeneous V2X Service Requirements

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.

cs.NI↗

arXiv2023

Multi-Channel Operation for the Release 2 of ETSI Cooperative Intelligent Transport Systems

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 towards 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 paper provides a comprehensive review of the new set of specifications, describing the main entities extending the C-ITS architecture at the different layers of the protocol stack, In addition, the paper 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 paper facilitate the understanding and motivation of the new set of Release 2 ETSI specifications for MCO and the identification of new research opportunities.

cs.NI↗

arXiv2021

Analytical Models of the Performance of IEEE 802.11p Vehicle to Vehicle Communications

The critical nature of vehicular communications requires their extensive testing and evaluation. Analytical models can represent an attractive and cost-effective approach for such evaluation if they can adequately model all underlying effects that impact the performance of vehicular communications. Several analytical models have been proposed to date to model vehicular communications based on the IEEE 802.11p (or DSRC) standard. However, existing models normally model in detail the MAC (Medium Access Control), and generally simplify the propagation and interference effects. This reduces their value as an alternative to evaluate the performance of vehicular communications. This paper addresses this gap, and presents new analytical models that accurately model the performance of vehicle-to-vehicle communications based on the IEEE 802.11p standard. The models jointly account for a detailed modeling of the propagation and interference effects, as well as the impact of the hidden terminal problem. The model quantifies the PDR (Packet Delivery Ratio) as a function of the distance between transmitter and receiver. The paper also presents new analytical models to quantify the probability of the four different types of packet errors in IEEE 802.11p. In addition, the paper presents the first analytical model capable to accurately estimate the Channel Busy Ratio (CBR) metric even under high channel load levels. All the analytical models are validated by means of simulation for a wide range of parameters, including traffic densities, packet transmission frequencies, transmission power levels, data rates and packet sizes. An implementation of the models is provided openly to facilitate their use by the community.

cs.NI↗

arXiv2020

Generation of Cooperative Perception Messages for Connected and Automated Vehicles

Connected and Automated Vehicles (CAVs) utilize a variety of onboard sensors to sense their surrounding environment. CAVs can improve their perception capabilities if vehicles exchange information about what they sense using V2X communications. This is known as cooperative or collective perception (or sensing). A frequent transmission of collective perception messages could improve the perception capabilities of CAVs. However, this improvement can be compromised if vehicles generate too many messages and saturate the communications channel. An important aspect is then when vehicles should generate the perception messages. ETSI has proposed the first set of message generation rules for collective perception. These rules define when vehicles should generate collective perception messages and what should be their content. We show that the current rules generate a high number of collective perception messages with information about a small number of detected objects. This results in an inefficient use of the communication channel that reduces the effectiveness of collective perception. We address this challenge and propose an improved algorithm that modifies the generation of collective perception messages. We demonstrate that the proposed solution improves the reliability of V2X communication and the perception of CAVs.

cs.NI↗

arXiv2020

Context-based Broadcast Acknowledgement for Enhanced Reliability of Cooperative V2X Messages

Most V2X applications/services are supported by the continuous exchange of broadcast messages. One of the main challenges is to increase the reliability of broadcast transmissions that lack of mechanisms to assure the correct delivery of the messages. To address this issue, one option is the use of acknowledgments. However, this option has scalability issues when applied to broadcast transmissions because multiple vehicles can transmit acknowledgments simultaneously. To control scalability while addressing reliability of broadcast messages, this paper proposes and evaluates a context-based broadcast acknowledgement mechanism where the transmitting vehicles selectively request the acknowledgment of specific/critical broadcast messages, and performs retransmissions if they are not correctly received. In addition, the V2X applications/services identify the situations/conditions that trigger the execution of the broadcast acknowledgment mechanism, and the receiver(s) that should acknowledge the broadcast messages. The paper evaluates the performance of the context-based broadcast acknowledgment mechanism for a Collective Perception Service. The obtained results show the proposed mechanism can contribute to improve the awareness of crossing pedestrians at intersections by increasing the reliability in the exchange of CPM messages between vehicles approaching the intersection. This solution is being discussed under IEEE 802.11bd, and thus can be relevant for the standardization process.

cs.NI↗

arXiv2020

Empirical Models for the Realistic Generation of Cooperative Awareness Messages in Vehicular Networks

Most V2X (Vehicle-to-Everything) applications rely on broadcasting awareness messages known as CAM (Cooperative Awareness Messages) in ETSI or BSM (Basic Safety Message) in SAE standards. A large number of studies have been devoted to guarantee their reliable transmission. However, to date, the studies are generally based on simplified data traffic models that generate awareness messages at periodic intervals or with a constant message size. These models do not accurately represent the real generation of CAM messages that follow specific mobility-based rules. Using simplified and unrealistic traffic models can significantly impact the results and validity of the studies, and hence accurate models for the generation of awareness messages are necessary. This paper proposes the first set of models that can realistically generate CAM messages. The models have been created from real traces collected by two car manufacturers in urban, sub-urban and highway test drives. The models are based on mth order Markov sources, and model the size of CAMs and the time interval between CAMs. The models are openly provided to the community and can be easily integrated into any simulator.

cs.NI↗

arXiv2019

Power and Packet Rate Control for Vehicular Networks in Multi-Application Scenarios

Vehicular networks require vehicles to periodically transmit 1-hop broadcast packets in order to detect other vehicles in their local neighborhood. Many vehicular applications depend on the correct reception of these packets that are transmitted on a common control channel. Vehicles will actually be required to simultaneously execute multiple applications. The transmission of the broadcast packets should hence be configured to satisfy the requirements of all applications while controlling the channel load. This can be challenging when vehicles simultaneously run multiple applications, and each application has different requirements that vary with the vehicular context (e.g. speed and density). In this context, this paper proposes and evaluates different techniques to dynamically adapt the rate and power of 1-hop broadcast packets per vehicle in multi-application scenarios. The proposed techniques are designed to satisfy the requirements of multiple simultaneous applications and reduce the channel load. The evaluation shows that the proposed techniques significantly decrease the channel load, and can better satisfy the requirements of multiple applications compared to existing approaches, in particular the Message Handler specified in the SAE J2735 DSRC Message Set Dictionary.

cs.NI↗