IEEE Open Journal of Vehicular Technology最新文献_第5页

Indoor Localization and Tracking in Reconfigurable Intelligent Surface Aided mmWave Systems 可重构智能表面辅助毫米波系统的室内定位与跟踪

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2025-06-25 DOI: 10.1109/OJVT.2025.3582885

Kunlun Li;Mohammed El-Hajjar;Chao Xu;Lajos Hanzo

{"title":"Indoor Localization and Tracking in Reconfigurable Intelligent Surface Aided mmWave Systems","authors":"Kunlun Li;Mohammed El-Hajjar;Chao Xu;Lajos Hanzo","doi":"10.1109/OJVT.2025.3582885","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3582885","url":null,"abstract":"Millimeter wave (mmWave) carriers have a high available bandwidth, which can be beneficial for high-resolution localization in both the angular and temporal domains. However, the limited coverage due to severe path loss and line-of-sight (LoS) blockage are considered to be major challenges in mmWave. A promising solution is to employ reconfigurable intelligent surfaces (RIS) to circumvent the lack of line-of-sight paths, which can assist in localization. Furthermore, radio localization and tracking are capable of accurate real-time monitoring of the UE's locations and trajectories. In this paper, we propose a three-stage indoor tracking scheme. In the first stage, channel sounding is harnessed in support of the transmitter beamforming and receiver combining design. Based on the estimation in the first stage, a simplified received signal model is obtained, while using a discrete Fourier transform (DFT) matrix for the configuration of the RIS phase shifter for each time block. Based on the simplified received signal model, tracking initialization is carried out. Finally, in the third stage, Kalman filtering is employed for tracking. Our results demonstrate that the proposed scheme is capable of improving both the accuracy and robustness of tracking compared to single-shot successive localization. Additionally, we derive the position error bounds (PEB) of single-shot localization.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1815-1831"},"PeriodicalIF":5.3,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11050944","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of High-Energy Density and High-Power Density Batteries for an Electric Vehicle 电动汽车用高能量密度和高功率密度电池的比较

IF 4.8

IEEE Open Journal of Vehicular Technology Pub Date : 2025-06-25 DOI: 10.1109/OJVT.2025.3583070

Salma Fadili;Ronan German;Alain Bouscayrol;Clément Mayet;Philippe Fiani;Eric Noirtat

引用次数: 0

Hardware Evaluation of Interference Alignment Techniques Under Different Channel State Information Updating Rates 不同信道状态信息更新速率下干扰对准技术的硬件评价

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2025-06-24 DOI: 10.1109/OJVT.2025.3581878

David Alejandro Urquiza Villalonga;Alejandro López Barrios;Máximo Morales-Céspedes;M. Julia Fernández-Getino García

{"title":"Hardware Evaluation of Interference Alignment Techniques Under Different Channel State Information Updating Rates","authors":"David Alejandro Urquiza Villalonga;Alejandro López Barrios;Máximo Morales-Céspedes;M. Julia Fernández-Getino García","doi":"10.1109/OJVT.2025.3581878","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3581878","url":null,"abstract":"Wireless networks are evolving to provide high data rates, ultra-low latency, reliable communications, and the connectivity of multiple devices in a reduced area. However, massive densification of networks leads to an increase in interfering signals. In this context, interference alignment (IA) algorithms have been proposed to manage interference while increasing the achievable degrees of freedom. However, the practical implementation of IA algorithms faces several issues such as the lack of perfect channel state information (CSI), network synchronization, or modeling a highly heterogeneous signal-to-interference-plus-noise (SINR) distribution. In this work, we propose an experimental evaluation of IA emulating an interference-limited network but focusing on the user perspective. In contrast to previous works, a hardware testbed with universal software radio peripherals (USRPs) is implemented to model heterogeneous SINR networks. The role of both closed and open loops for providing CSI is evaluated. Then, the impact of CSI updating on the spectral efficiency and also on the bit error rate (BER) is analyzed. Furthermore, precoding techniques such as zero-forcing (ZF) or singular value decomposition (SVD) are also considered for comparison purposes. All the results are based on real measurements providing valuable insights into the performance of IA algorithms in real wireless networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1760-1773"},"PeriodicalIF":5.3,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11046342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144641054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed RL-Based Resource Allocation and Task Offloading for Vehicular Edge of Things Computing 基于分布式rl的车辆物联网边缘计算资源分配与任务卸载

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2025-06-23 DOI: 10.1109/OJVT.2025.3582035

Ghada Afifi;Bassem Mokhtar

{"title":"Distributed RL-Based Resource Allocation and Task Offloading for Vehicular Edge of Things Computing","authors":"Ghada Afifi;Bassem Mokhtar","doi":"10.1109/OJVT.2025.3582035","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3582035","url":null,"abstract":"Smart vehicles are increasingly equipped with advanced sensors and computational resources which enable them to detect surroundings and enhance driving safety. VEoTC (Vehicular Edge of Things Computing) solutions aim to exploit these embedded sensors and resources to provide computational services to other users. VEoTC can enhance the Quality of Experience (QoE) of vehicle and mobile users requesting computational tasks by providing context-aware services closer to the users that are otherwise not easily accessible in real time. Additionally, such solutions can extend the computational coverage to areas lacking Roadside Unit (RSU) infrastructure. However, VEoTC frameworks face several challenges in effectively localizing and allocating the distributed resources and offloading tasks successfully due to the high mobility of vehicles and fluctuating user densities. The paper proposes a distributed Machine Learning (ML)-based solution which optimizes task scheduling to smart vehicles and/or RSUs through joint resource allocation and task offloading. We formulate a belief-based optimization problem to maximize the QoE of vehicular users while providing performance guarantees that account for geospatial uncertainty associated with the availability of embedded resources. We propose a Deep Reinforcement Learning (DRL)-based solution to solve the formulated problem in real-time adapting to the dynamic network conditions. We analyze the performance of the proposed approach as compared to benchmark optimization and other ML-based techniques. Furthermore, we conduct hardware-based field test experiments to verify the effectiveness of our proposed algorithm to satisfy the stringent real-time latency requirements for various vehicular applications. According to our extensive simulation and experimental results, the proposed solution has the potential to satisfy the stringent QoE guarantees required for critical road safety applications.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1796-1814"},"PeriodicalIF":5.3,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045983","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the Electrical Network Impact of EV Charging Strategies Used by Operators Across Residential, Workplace, and Public Users 评估住宅、工作场所和公共用户运营商使用的电动汽车充电策略对电网的影响

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2025-06-20 DOI: 10.1109/OJVT.2025.3581803

Cesar Diaz-Londono;Gibran David Agundis-Tinajero;Paolo Maffezzoni;Giambattista Gruosso;Josep M. Guerrero

{"title":"Evaluating the Electrical Network Impact of EV Charging Strategies Used by Operators Across Residential, Workplace, and Public Users","authors":"Cesar Diaz-Londono;Gibran David Agundis-Tinajero;Paolo Maffezzoni;Giambattista Gruosso;Josep M. Guerrero","doi":"10.1109/OJVT.2025.3581803","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3581803","url":null,"abstract":"The increasing penetration of Electric Vehicles (EVs) presents significant challenges to the electrical grid, including potential overloads and voltage drops. This study assesses the impact of various EV charging strategies on grid performance within residential, workplace, and public charging environments. The strategies evaluated, either currently in use or readily adaptable by Charge Point Operators (CPOs), include the dispatch of maximum, mean, and intermediate power. The research focuses on key factors such as voltage stability, line loading, and network losses, while also exploring the flexibility that each charging strategy offers in energy service provision. Using comprehensive simulations of the IEEE 69-Bus test system, both with and without EV integration, the study identifies strategies that effectively enhance grid stability and efficiency. The results provide valuable insights for CPOs and grid managers, contributing to the improvement of EV charging infrastructure to ensure a resilient and efficient electrical grid.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1721-1735"},"PeriodicalIF":5.3,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045421","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Optimal Energy Management Strategy With Time-Varying Equivalent Factor Based on Transformer for Multi-Mode Hybrid Electric Mining Trucks 基于变压器的多模混合动力矿用卡车时变等效因子优化能量管理策略

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2025-06-19 DOI: 10.1109/OJVT.2025.3581072

Xinxin Zhao;Jiaqi Li;Menglei Liu;Jinggang Zhang;Nasser Lashgarian Azad

{"title":"An Optimal Energy Management Strategy With Time-Varying Equivalent Factor Based on Transformer for Multi-Mode Hybrid Electric Mining Trucks","authors":"Xinxin Zhao;Jiaqi Li;Menglei Liu;Jinggang Zhang;Nasser Lashgarian Azad","doi":"10.1109/OJVT.2025.3581072","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3581072","url":null,"abstract":"Incorporating hybrid electric technology in mining trucks efficiently allocates energy between the diesel engine and electric motors, significantly reducing fuel consumption and emissions. This paper introduces an advanced Transformer network model aimed at an adaptive Energy Management Strategy (EMS) to improve the fuel efficiency of Hybrid Electric Mining Trucks (HEMTs). Initially, we outline models representing various HEMT subsystems, including its complex powertrain. Subsequently, leveraging the established vehicle model, we employ Dynamic Programming (DP) to ascertain the globally optimal control strategy for a standard driving cycle. Utilizing the Transformer model with ProbSparse self-attention, we determine the state of charge (SOC) associated with this globally optimal control strategy. We then implement a Proportional-Integral (PI)-based SOC tracking controller to achieve a time-varying Equivalent Factor (EF). Additionally, we compare the proposed strategy across various driving cycles, underscoring the Transformer network's superior generalization capabilities over the Long-Short Term Memory (LSTM) model. Compared to the rule-based (RB) strategy and the traditional Equivalent Consumption Minimization Strategy (ECMS), our proposed EMS with time-varying EF based on the Transformer model shows substantial performance improvements of 15% and 12%, respectively. Furthermore, our strategy exhibits a 2% fuel economy advantage over an LSTM-based EMS algorithm with time-varying EF.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1749-1759"},"PeriodicalIF":5.3,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autonomous Vehicle Security: Hybrid Threat Modeling Approach 自动驾驶汽车安全：混合威胁建模方法

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2025-06-17 DOI: 10.1109/OJVT.2025.3580538

Amal Yousseef;Yu-Zheng Lin;Shalaka Satam;Banafsheh Saber Latibari;Jesus Pacheco;Soheil Salehi;Salim Hariri;Pratik Satam

{"title":"Autonomous Vehicle Security: Hybrid Threat Modeling Approach","authors":"Amal Yousseef;Yu-Zheng Lin;Shalaka Satam;Banafsheh Saber Latibari;Jesus Pacheco;Soheil Salehi;Salim Hariri;Pratik Satam","doi":"10.1109/OJVT.2025.3580538","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3580538","url":null,"abstract":"Autonomous vehicles (AVs) are poised to revolutionize modern transportation, offering enhanced safety, efficiency, and convenience. However, AV architectures' increasing connectivity and complexity have introduced significant cybersecurity risks. This survey provides a comprehensive review of AV security challenges, focusing on widely adopted threat modeling frameworks such as STRIDE, DREAD, andMITRE ATT&CK. By examining common attack vectors and real-world case studies, including the Jeep Cherokee and Tesla Model S exploits, we highlight the urgent need for robust cybersecurity in in-vehicle systems and external interfaces. To complement existing modeling practices, we introduce Hybrid-SCDM, a novel framework that combines STRIDE-based threat classification with CVSS-derived DREAD scoring. This model transforms qualitative threat identification into quantitative risk prioritization by mapping CVSS metrics to DREAD dimensions through normalization. Applied to a generic multi-layered AV architecture, our findings show that intra-vehicle networks, especially CAN bus spoofing and fuzzing attacks, and suspension attacks, represent the most critical vulnerabilities due to their high exploitability and systemic impact. Beyond technical modeling, the survey explores emerging defense mechanisms such as blockchain-enabled Vehicle-to-Everything (V2X) communication, AI-driven anomaly detection, and secure Over-The-Air (OTA) updates. We also examine legal and ethical considerations surrounding data privacy, user safety, and regulatory compliance. By integrating analytical modeling with broad system insights, this work provides actionable recommendations for advancing the cybersecurity posture of autonomous vehicles.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1774-1795"},"PeriodicalIF":5.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11039067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144641055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RIS-Based DOA Estimation for Communication-Assisted Sensing Systems Under Hardware Impairments 硬件损伤下基于ris的通信辅助传感系统DOA估计

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2025-06-16 DOI: 10.1109/OJVT.2025.3580041

Xue Zhang;Ngoc Phuc Le;Mohamed-Slim Alouini

{"title":"RIS-Based DOA Estimation for Communication-Assisted Sensing Systems Under Hardware Impairments","authors":"Xue Zhang;Ngoc Phuc Le;Mohamed-Slim Alouini","doi":"10.1109/OJVT.2025.3580041","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3580041","url":null,"abstract":"Reconfigurable intelligent surface (RIS)-based localization has received significant attention in wireless communication systems due to its potential for enabling non-line-of-sight (NLoS) wireless sensing. However, hardware impairments (HWIs) in non-ideal hardware adversely affect the localization performance. To address the issue, this work studies a RIS-aided passive sensing system with the presence of HWIs, where communications signals are used to sense targets. We propose a method to address a DOA estimation problem in three-dimensional (3D) space. Specifically, a low-rank matrix is first recovered by solving an atomic norm minimization (ANM) problem, which is not affected by interference signal and additive noise. Subsequently, a block Hankel matrix is constructed using the correlation information of the low-rank matrix. The elevation and azimuth angles between targets and RIS are then estimated by applying the modified matrix pencil (MMP) method on the block Hankel matrix. We also derive the Cramér-Rao Bound (CRB) with closed-form expressions for DOA estimation based on the proposed system model. The proposed algorithm can sense multiple targets and achieve parameter automatic pairing. Numerical results verify the superiority of the proposed algorithm over the traditional schemes.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1736-1748"},"PeriodicalIF":5.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11037246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Codebook Design for Parallel Dynamic Subarrays in Millimeter Wave Massive MIMO Systems 毫米波大规模MIMO系统中并行动态子阵列的码本设计

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2025-06-16 DOI: 10.1109/OJVT.2025.3579690

Qi Li;Fu-Chun Zheng;Ke Xu;Lianming Li

引用次数: 0

Towards Robust Autonomous Driving: Out-of-Distribution Object Detection in Bird's Eye View Space 面向鲁棒自动驾驶：鸟瞰空间的非分布目标检测

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2025-06-12 DOI: 10.1109/OJVT.2025.3579341

Muhammad Asad;Ihsan Ullah;Ganesh Sistu;Michael G. Madden

{"title":"Towards Robust Autonomous Driving: Out-of-Distribution Object Detection in Bird's Eye View Space","authors":"Muhammad Asad;Ihsan Ullah;Ganesh Sistu;Michael G. Madden","doi":"10.1109/OJVT.2025.3579341","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3579341","url":null,"abstract":"In autonomous driving, understanding the surroundings is crucial for safety. Since most object detection systems are designed to identify known objects, they may miss unknown or novel objects, which can be dangerous. This study addresses Out-Of-Distribution (OOD) detection for vehicle-like unknown objects within the Bird's Eye View (BeV) space, a top-down representation of the environment that provides a comprehensive spatial layout crucial for scene understanding. Enhancing the model's adaptability to unfamiliar objects, we present two novel methods for detecting unknown objects in BeV space. Specifically, we introduce random patches and OOD objects in the environment to help the model identify both known objects, such as vehicles, and OOD objects. We also introduce a new dataset, NuScenesOOD, derived from the NuScenes dataset, which augments vehicles with patterns and shapes to challenge the model. Additionally, we address challenges such as patch size inconsistency and occlusion from moving frames in BeV space. Our method targets vehicle-shaped anomalies in the planar driving space, maintaining high accuracy for known and enhancing detection of unknown objects. This research contributes to making future autonomous vehicles safer by improving their ability to detect diverse vehicle like OOD objects in their environment.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1673-1685"},"PeriodicalIF":5.3,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11031213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0