Search Results (413)

Addressing critical bottlenecks in traditional hydrogen refueling station operations—specifically supply–demand imbalances and suboptimal scheduling—this paper presents a systematic review of the advancements and practical implementations of intelligent transportation platforms (ITPs). We explore how these platforms catalyze enhancing operational efficiency within the hydrogen ecosystem. This paper first outlines the technical foundations of Vehicle-to-Everything communication, edge computing, and multi-source data fusion, and provides an in-depth analysis of core challenges, such as demand uncertainty and resource scheduling complexity, as well as existing optimization algorithms. Through typical case studies, the significant value of such platforms in breaking down data silos, reducing equipment idle rates, and achieving end-to-end energy efficiency optimization is demonstrated. This study notes that current bottlenecks include fragmented standards, difficulties in implementing algorithms, commercial challenges, and the retrofitting of existing infrastructure. Moving forward, efforts should shift from isolated technological breakthroughs to ecosystem development. This includes improving demand forecasting accuracy in low-penetration regions, implementing lightweight retrofits to revitalize the existing market, establishing cross-domain data collaboration standards, building a trustworthy cross-platform settlement system, and exploring innovative pathways that integrate “hydrogen, carbon, and computing.” Full article

The rapid development of renewable energy vehicles (REVs) has deepened the coupling between transportation and power systems, leading to the formation of the vehicle–station–traffic–grid (VSTG) coupled system. This paper provides a systematic review of multi-agent intelligent interaction technologies for REVs under the VSTG framework, covering the evolutionary process of VSTG systems, the composition and coupling mechanisms of vehicle–station–traffic–grid subsystems, the objectives and constraints of heterogeneous agents, representative V2X interaction modes, deployment-related standards, and collaborative optimization methods. First, the development trajectory of VSTG systems is traced, from independent planning and uncoordinated charging to V2G integration and V2X multi-network interaction. Second, a multi-agent interaction framework is established to characterize vehicle agents, charging station agents, grid agents, traffic management agents, user/operator agents, aggregator/platform agents, and roadside infrastructure agents. In addition, representative vehicle-to-everything (V2X) modes, including V2L, V2H, V2B, V2mG, and V2G, are compared in terms of their operating principles, application scenarios, and technical characteristics. Moreover, various optimization methods for the coupled system are reviewed. Finally, key challenges, including cross-domain coupling complexity, operational uncertainty, interoperability, battery degradation, and engineering deployment, are discussed, and future research directions are proposed. This review provides a structured reference for the modeling, optimization, and practical deployment of intelligent VSTG systems. Full article

The Internet of Vehicles (IoV) is reshaping intelligent transportation through pervasive connectivity, real-time data exchange, cooperative perception, and vehicle–edge–cloud services, while also expanding cybersecurity and privacy risks across heterogeneous cyber–physical environments. This paper presents a PRISMA 2020-informed systematic review of IoV security and privacy protection research. A cross-layer and lifecycle-oriented analytical framework is developed by integrating a four-layer IoV architecture—sensing layer, network access layer, coordinative computing layer, and application layer—with a five-stage data lifecycle covering data collection, transmission, storage, usage, and disposal. Based on this framework, the paper examines representative threat surfaces, vehicle-to-everything (V2X) communication security, public key infrastructure (PKI) based authentication, trust management, privacy-preserving data sharing, intrusion detection, active defense, and AI-assisted security analytics. Privacy-preserving mechanisms, including differential privacy, federated learning, blockchain, homomorphic encryption, and secure multi-party computation, are further compared in terms of deployment layer, lifecycle stage, real-time suitability, and representative performance evidence. In addition, the review discusses the engineering relevance of UNECE WP.29 R155/R156, ISO/SAE 21434, and related national standards, with emphasis on compliance evidence, over-the-air (OTA) governance, supply-chain coordination, and lifecycle cybersecurity management. The review shows that no single protection mechanism can simultaneously satisfy the requirements of real-time performance, scalability, privacy preservation, trustworthiness, and regulatory compliance in dynamic IoV environments. Future research should emphasize lightweight and adaptive protection, cross-layer trust coordination, privacy–utility co-optimization, trustworthy AI-assisted security operations, and evidence-based lifecycle governance. This review provides a structured reference for researchers and a practical basis for secure and privacy-aware IoV system design. Full article

Accurate road-event detection and timely alert message dissemination are essential for the safety of connected and automated vehicles. In many scenarios, alert messages must reach not only nearby vehicles but also remote stakeholders, such as traffic management centers, cloud services, and infrastructure operators. This requirement motivates the adoption of cellular-based communication technologies in addition to short-range vehicle-to-everything (V2X) communications for data dissemination. In this work, we investigate vehicle-to-network-to-everything (V2N2X) communications for the dissemination of alert messages generated after the on-board detection of hazardous road events through machine learning (ML) algorithms. Although V2N2X connectivity is well suited for extending data dissemination beyond the local vehicular environment, its capability to guarantee prompt message delivery under strict latency constraints remains an open challenge, particularly when ML inference is integrated into the end-to-end processing pipeline. To address this issue, we develop and experimentally evaluate a proof-of-concept (PoC) platform that combines real-time road-event detection with relevant message dissemination towards both nearby and remote recipients. The proposed framework leverages 5G connectivity and publish/subscribe messaging protocols. The experimental results showcase that dissemination latency is highly influenced by both the adopted type of 5G deployment (private versus commercial networks) and the load conditions at the message broker. Full article

Resource allocation for cognitive Vehicle-to-Everything (V2X) networks is challenging due to dynamic spectrum sharing, strong interference coupling, and stringent latency constraints for safety-critical Vehicle-to-Vehicle (V2V) traffic. Although recent Multi-Agent Reinforcement Learning (MARL) approaches report promising gains, many evaluations are conducted at limited and fixed network scales, which restricts insights into scalability under dense spectrum reuse. This paper investigates cooperative multi-agent learning for interference-aware and deadline-constrained V2X resource management. We propose a Q-value Transformation (QTRAN)-based value decomposition framework under centralized training with decentralized execution (CTDE) for joint resource-block and power allocation among V2V agents. The proposed approach is implemented in a realistic V2V/V2I simulator incorporating Manhattan grid mobility, fast fading, explicit cross-tier and co-channel interference, and per-link payload/deadline dynamics. Beyond communication-level performance, improved timely delivery of V2V safety messages can support cooperative maneuvering, collision avoidance, platooning, and infrastructure-assisted traffic management. Extensive simulations across varying numbers of V2V agents benchmark QTRAN against independent learning baselines including MARL and centralized single-agent learning (SARL). Results show that QTRAN improves performance compared with the selected learning baselines and enhances the throughput–reliability trade-off under interference-coupled spectrum reuse. For instance, at $N_{\mathrm{V}2\mathrm{V}}=20$, QTRAN achieves a V2V rate of $0.194\pm 0.004$ and a V2I rate of $9.117\pm 0.213$, while reaching a V2V success rate of $0.812\pm 0.017$ with a low Deadline Miss Ratio of $0.001\pm 0.000$. At higher density ($N_{\mathrm{V}2\mathrm{V}}=50$), QTRAN sustains strong reliability (V2V success rate of $0.719\pm 0.006$ and Completion Ratio of $0.716\pm 0.006$) while maintaining competitive infrastructure throughput (V2I rate of $9.251\pm 0.114$). These results indicate that QTRAN effectively captures non-linear interference interactions, enabling coordinated decentralized spectrum and power decisions under the adopted density-based evaluation setting, thereby enhancing V2V reliability and throughput in cognitive Internet of Vehicles. Full article

This paper presents the field deployment and performance evaluation of a New Radio Vehicle-to-Everything (NR-V2X) Cooperative Intelligent Transportation System (C-ITS) test corridor over a 5G stand-alone (SA) private network, implemented on a 40 km highway in Istanbul, Turkey. The deployment integrates 19 dual-sector gNBs, commercial off-the-shelf (COTS) core network components, and an O-RAN-compatible Rel. 17 architecture and evaluates six ETSI-compliant C-ITS scenarios under a systematic 3 × 3 experimental matrix spanning three vehicle speeds and three traffic density categories. Key quantitative findings include the following: (i) 98.9% of the corridor achieves the target RSRP of −110 dBm, confirming coverage viability; (ii) five of the six scenarios satisfy ETSI end-to-end latency requirements across all tested conditions, with the packet delivery ratio remaining above 94% throughout; and (iii) the Emergency Vehicle Approaching (EVA) scenario meets its stringent 20 ms latency requirement exclusively under free-flow conditions (μ = 14.7 ms) and progressively exceeds it under medium- and high-density traffic (μ = 26.6 ms and μ = 40.1 ms, respectively). These results provide quantitative evidence that MEC integration is a necessary architectural complement to the 5G SA private network for ultra-low-latency safety services and establish a reproducible reference architecture for public highway C-ITS deployments. Full article

Vehicle-to-Everything (V2X) communication is a critical enabler of autonomous driving, supporting real-time information exchange among vehicles, roadside infrastructure, pedestrians, and cloud services. However, the security of current V2X systems largely relies on classical cryptographic mechanisms, which are expected to become vulnerable in the presence of large-scale quantum computers. Given the long operational lifespan and stringent safety requirements of autonomous vehicular networks, the transition toward quantum-resistant authentication and key management mechanisms has become increasingly important. This paper presents a comprehensive survey of post-quantum Authentication and Key Agreement (AKA) protocols for secure V2X communications. The survey systematically reviews V2X communication architectures, security and privacy requirements, existing authentication frameworks, and emerging post-quantum cryptographic approaches. Representative AKA schemes and NIST-standardized post-quantum algorithms are comparatively analyzed in terms of security strength, computational complexity, communication overhead, storage requirements, scalability, and deployment suitability for resource-constrained vehicular environments. The survey further examines practical implementation challenges, including latency constraints, bandwidth limitations, signature size expansion, memory consumption, and hardware resource requirements. The analysis reveals that achieving quantum-resistant security in V2X networks requires balancing strong cryptographic protection with the stringent performance demands of safety-critical vehicular applications. While recent post-quantum approaches offer promising security guarantees against quantum adversaries, their practical deployment remains constrained by computational and communication overhead. Finally, this survey identifies key research gaps and outlines future directions for the development of lightweight, scalable, and quantum-resilient AKA frameworks capable of supporting next-generation autonomous transportation systems. The findings provide researchers and practitioners with a structured understanding of the opportunities, limitations, and challenges associated with securing future V2X communications in the quantum era. Full article

In this work, we explore resilient cooperative localisation for electric vehicles subject to the hybrid attack of gradual global navigation satellite system (GNSS) drag-off spoofing along with received signal strength indicator (RSSI) jamming. In order to mitigate such attacks, a deep learning-based physical-layer security approach is presented. The presented approach includes a long short-term memory (LSTM) detector for attack detection, a regression-based RSSI signal purifier, and a cooperative fusion scheme, which decreases the dependence on the GNSS branch in case of attack detection. The proposed approach is validated via the Berlin Vehicle-to-Everything (V2X) dataset with respect to six scenarios, including benign GNSS-only and cooperative localisation, attacked localisation without defence, and attacked localisation with physical-layer security support. According to the experimental evaluation results, the considered hybrid attack significantly impacts the localisation accuracy, leading to an increase in the GNSS-only localisation error to root mean square error (RMSE) = 149.93 m, mean absolute error (MAE) = 129.81 m, and maximum error = 259.62 m. At the same time, the proposed cooperative localisation with physical-layer security decreases the attacked cooperative localisation error to RMSE = 4.00 m, MAE = 3.51 m, and maximum error = 12.01 m. Full article

The rapid increase in interest for Vehicle-to-Everything (V2X) networks has created significant challenges in efficient radio resource management. This paper addresses the problem of joint subcarrier assignment and power allocation to maximize the spectral efficiency of the system. First, this paper mathematically formulates resource allocation and power allocation as an optimization problem, which is solved using conventional optimization methodologies to establish a baseline for performance benchmarking. To overcome the high computational complexity associated with traditional optimization, we subsequently propose a Multi-Agent Deep Q-Network (Multi-DQN) agent framework based on deep reinforcement learning (DRL). The proposed agent learns optimal allocation strategies through interaction with the environment, enabling adaptive and real-time decision-making. The system performance is investigated in different environments under both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, addressing a gap in prior approaches. Simulation results demonstrate that the proposed Multi-DQN agent approach significantly outperforms the enhanced conventional benchmark, achieving higher spectral efficiency (SE) while substantially reducing the computational complexity. Full article

Vehicle-to-grid (V2G) can provide flexibility and storage for low-carbon power systems while supporting sustainable mobility, yet real-world deployment remains largely confined to pilots despite substantial technical progress. This article presents a PRISMA-guided systematic review of 974 V2G/V2X studies published between 2009 and 2025 to explain why implementation lags and how it can be accelerated. Within this corpus, a total of 162 implementation-critical articles are identified and, within these, 95 studies that primarily address non-technical dimensions such as policy, markets, user behavior, and ecosystem coordination. Drawing on full-text coding, a four-domain socio-technical framework is developed that clusters recurring non-technical barriers and enablers into business–economic, governance–policy, social, and infrastructure and ecosystem domains. The analysis reveals (i) a temporal shift from technical dominance to multidisciplinary acceleration after 2021; (ii) distinct regional priorities in which Europe emphasizes regulation and business models, Asia focuses on infrastructure scaling, and the Americas on frequency services and resilience; and (iii) persistent revenue uncertainty, regulatory gaps, user resistance, and grid unreadiness as cross-cutting obstacles. For each domain, concrete transition levers and indicative deployment key performance indicators (KPIs) are derived, such as multi-actor revenue-sharing mechanisms, aggregator recognition in market rules, privacy-by-design user participation models, and targeted bidirectional charging deployment in constrained grids. Synthesizing these insights, three archetypal V2G transition pathways are proposed—regulation-led, infrastructure-first, and service-driven—that reflect regional conditions and offer alternative routes to large-scale adoption. The framework and roadmap provide researchers, policymakers, system operators, and mobility providers with an integrated basis for designing, monitoring, and evaluating V2G policies, business models, and pilots in line with energy system decarbonization goals. Full article

Vehicle-to-everything (V2X) communication systems impose stringent latency and reliability requirements that are difficult to satisfy in highly dynamic wireless environments. Although reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicles (UAVs) have independently demonstrated potential in enhancing wireless coverage, most existing RIS–UAV frameworks rely on idealized assumptions such as perfect channel state information (CSI) and static user scenarios. In this paper, a multi-RIS-assisted UAV-enabled V2X communication framework is proposed that explicitly accounts for vehicular mobility, latency constraints, and mobility-induced CSI aging. Multiple RIS panels are cooperatively deployed to eliminate coverage blind spots and ensure link continuity in realistic V2X environments. A joint UAV mobility and RIS phase optimization approach is proposed under outdated CSI to improve link reliability. Additionally, a time-varying performance analysis is carried out for understanding the dynamic behavior of signal-to-noise ratio (SNR) and average bit error rate (ABER) for mobility-aware CSI aging. Simulation results demonstrate that the proposed framework reduces the ABER by approximately 75% compared to a conventional single-RIS system under outdated CSI at 20 dB SNR ($1.07\times {10}^{-1}$ vs. $4.32\times {10}^{-1}$), while substantially suppressing outage intervals in high-mobility V2X scenarios ($v=20$ m/s, CSI delay $\tau =20$ ms), confirming the effectiveness of cooperative multi-RIS assistance for safety-critical vehicular communications. Full article

Connected and autonomous vehicles (CAVs) increasingly rely on vehicle-to-everything (V2X) communication and distributed sensing infrastructures to support cooperative driving and intelligent transportation services. While these capabilities improve traffic efficiency and safety, they also expand the attack surface of vehicular networks and expose in-vehicle communication systems such as the Controller Area Network (CAN) bus to a wide range of cyber threats. Machine learning-based anomaly detection has emerged as a promising approach for identifying malicious CAN traffic patterns; however, conventional centralized learning requires large-scale data aggregation from vehicles, which raises privacy and scalability concerns. Federated learning (FL) enables collaborative model training across distributed vehicles without requiring the exchange of raw in-vehicle data, making it attractive for privacy-preserving vehicular security applications. Nevertheless, FL systems remain vulnerable to adversarial participants that manipulate local training data or model updates to poison the global model during aggregation. In this work, we present a systematic robustness evaluation of federated anomaly detection in connected vehicular networks under adversarial conditions. The study compares six aggregation strategies, including Federated Averaging (FedAvg), coordinate-wise Median, Trimmed Mean, Krum, Multi-Krum, and Geometric Median (GeoMed), within a non-IID federated CAN bus anomaly detection setting. The evaluation covers label-flipping attacks, gradient-scaling attacks, and a feature-triggered backdoor attack. In addition, the analysis examines malicious client participation, attack-strength variation, learning-rate sensitivity, Trimmed Mean beta sensitivity, multi-seed reliability, and server-side aggregation time. The results show that FedAvg is vulnerable under strong adversarial manipulation, while Trimmed Mean is sensitive to the selected trimming fraction. Median and GeoMed provide strong robustness against gradient-scaling attacks, whereas Multi-Krum achieves the strongest resistance to label-flipping and backdoor attacks. These findings demonstrate that no single aggregation strategy is optimal across all threat models. Instead, robust aggregation for federated CAV anomaly detection should be selected according to the expected attack type, reliability requirement, and computational overhead. Full article

In recent years, Intelligent Transportation Systems (ITSs) have emerged as a solution to mitigate the problem of traffic congestion. Understanding human driving styles such as aggressive, normal, and cautious is crucial for safe driving. In particular, predicting lane-change manoeuvres may be further supported by combining vehicle state information with driving style information. However, existing vehicle trajectory datasets lack driving style information, making classification challenging. To address this limitation, this paper proposes a fuzzy logic-based driving style classification framework in a Vehicle-to-Everything (V2X) environment. The model uses vehicle state information, including speed, longitudinal acceleration, lateral acceleration, and distance headway to classify style as cautious, normal, or aggressive. The proposed system is interpretable, aligns with human reasoning, and remains computationally efficient for real-time applications. The performance of the proposed work has been evaluated through comprehensive experiments on highway data. Results show a separation of driving styles, achieving 77% accuracy on a balanced dataset, showing moderate agreement with deterministic labelling while maintaining interpretability. In V2X-enabled lane-change prediction scenarios, computational latency is essential, as Roadside Units (RSUs) must understand driving style and update prediction models. Since lane-change intentions should be predicted around 3 s before manoeuvre, delays in inference reduce reaction time. The proposed classifier achieves an inference latency of approximately 8 ms, ensuring that it does not become a bottleneck in real-time systems. Furthermore, the usefulness of driving style information is tested by integrating it into a lane-change prediction task. Experimental results demonstrate that incorporating driving style enhances prediction accuracy from 75% to 84%. Lastly, the proposed method provides a balanced result between interpretability, computational efficiency, and predictive performance, supporting RSUs to issue timely warnings and support safer decision-making in highway environments. Full article

The uplink/downlink (UL/DL) decoupled access, which allows users to associate with different base stations (BSs), including small BSs (SBSs) and macro BSs (MBSs), has emerged as a network architecture in heterogeneous cellular vehicle-to-everything (C-V2X) communications. It can be tailored to mitigate the signal interference and attenuation impairments that cell-edge vehicles face, while vehicles closer to a BS can opt for coupled access. Therefore, a UL/DL intelligent decoupled access network that integrates decoupled and coupled access approaches is urgently needed for C-V2X communications. In this paper, we present a novel framework for UL/DL intelligent decoupled access in C-V2X networks in the context of fifth-generation mobile communications (5G) and beyond 5G (B5G). We propose a joint optimization approach for radio resource allocation, power control, and user association to enhance the network throughput of UL and DL while meeting the service quality requirements of vehicle users. Specifically, we formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and transform it into a standard convex optimization problem by introducing various auxiliary variables. An efficient iterative algorithm based on successive convex optimization techniques is introduced to obtain a sub-optimal solution. The proposed framework uniquely integrates decoupled and coupled access modes within a unified optimization formulation, enabling dynamic mode selection based on network load. Extensive simulation results demonstrate a significant performance improvement of the proposed UL/DL intelligent decoupled access in C-V2X networks compared with benchmark schemes. Full article

The rapid growth of Intelligent Transportation Systems (ITSs) necessitates secure and efficient Vehicle-to-Everything (V2X) communication. However, existing Physical Unclonable Function (PUF)-based schemes often suffer from modeling vulnerabilities and high overheads. This paper proposes a decentralized, dynamic, anonymous authentication protocol tailored for Vehicular Ad Hoc Networks (VANETs). By integrating Elliptic Curve Cryptography (ECC) with highly reliable Self-Adaption Deviation Locking PUFs (SDL PUFs), we design a dynamic Challenge–Response Pair (CRP) obfuscation mechanism. This mechanism effectively mitigates modeling threats, reducing the prediction success rate of machine learning (ML) and deep learning (DL) attacks by approximately 35% compared to raw SDL PUFs. The protocol ensures identity untraceability and forward secrecy through anonymous identifiers and ephemeral session keys. Security is formally verified under the Real-or-Random (ROR) model and validated using the AVISPA tool. Simulations in SUMO and Omnetpp demonstrate that the protocol is highly efficient, achieving a low computational overhead of 6.77 ms per entity and a communication cost of 192 bytes. Compared to state-of-the-art approaches, our solution provides superior robustness against advanced modeling attacks and significantly reduces latency, making it suitable for resource-constrained V2X environments. Full article