IEEE Open Journal of Vehicular Technology最新文献

{"title":"Improving SNR for NLoS Target Detection Using Multi-RIS-Assisted Monostatic Radar","authors":"Salman Liaquat;Ijaz Haider Naqvi;Faran Awais Butt;Saleh Alawsh;Nor Muzlifah Mahyuddin;Ali Hussein Muqaibel","doi":"10.1109/OJVT.2025.3547163","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3547163","url":null,"abstract":"The use of a reconfigurable intelligent surface (RIS) in radar systems significantly enhances target detection, particularly in challenging non-line-of-sight (NLoS) scenarios. In urban environments, where structures frequently obstruct line-of-sight (LoS) paths, the integration of RISs with existing radars can offer a viable solution for enhancing signal-to-noise ratio (SNR) and improving target detection. Approaches utilizing a single RIS can still fail in scenarios where a link cannot be established. This paper presents a novel approach for deriving a comprehensive expression for the received power, SNR and path loss (PL) in systems where multiple RISs assist a monostatic radar. We analyze the power received in dual RIS configurations and extend this to include additional RISs, demonstrating how each additional RIS placement affects the system's performance. Moreover, the analysis explores the impact of different Swerling target models on the SNR and PL, highlighting the optimal angles for target detection. This multi-RIS strategy offers a substantial performance boost over conventional radars and single RIS-assisted systems, particularly in environments with obstacles. Simulation results demonstrate a significant improvement in SNR with a dual RIS-assisted radar, with up to 14.42 dB gains observed when employing a <inline-formula><tex-math>$46 times 46$</tex-math></inline-formula> element RIS configuration at L-band and 65.47 dB gain when employing a <inline-formula><tex-math>$328 times 328$</tex-math></inline-formula> element RIS configuration at X-band, corresponding to a RIS size of <inline-formula><tex-math>$ 5text{ m} times 5text{ m}$</tex-math></inline-formula> at both frequencies, showing the efficacy of the proposed multi-RIS strategy.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"774-789"},"PeriodicalIF":5.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908879","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698350","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}

{"title":"THz-Enabled UAV Communications Under Pointing Errors: Tractable Statistical Channel Modeling and Security Analysis","authors":"Mohammad Javad Saber;Mazen Hasna;Osamah S. Badarneh","doi":"10.1109/OJVT.2025.3547244","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3547244","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are increasingly being utilized as mobile base stations for rapidly establishing temporary wireless coverage in emergency situations and remote locations. Their high mobility and flexibility make UAVs ideal for quickly deployed communication systems, but these features also introduce unique challenges, particularly in maintaining stable and reliable communication links. The highly directional nature of terahertz (THz) antennas introduces challenges in UAV communication systems. Combined with the mobility of UAVs, this can cause significant issues, such as beam misalignment and signal degradation. Thus, developing accurate radio channel models that address these challenges is critical to ensure reliable communication. In this study, we present an analytical framework focused on evaluating the security performance of highly directional THz-enabled UAV communication links. The challenges analyzed include misalignment of directional beams, path loss, small-scale fading, and UAV-induced vibrations. The small-scale fading is modeled using the <inline-formula><tex-math>$alpha$</tex-math></inline-formula>–<inline-formula><tex-math>$mu$</tex-math></inline-formula> distribution, which accurately represents various fading environments. Using the Meijer G-function, we derive closed-form expressions for key statistical functions, including the probability density function (PDF) and cumulative distribution function (CDF) of the channel gain. Furthermore, a detailed physical-layer security analysis is provided, focusing on metrics such as average secrecy capacity, secrecy outage probability, and the probability of strictly positive secrecy capacity, particularly in the presence of UAV eavesdroppers. Numerical results validate the analytical expressions under different operational conditions, such as beam misalignment and fading, providing valuable insights into the security and performance of THz-enabled UAV communication systems. These results provide important guidelines for optimizing future wireless networks using UAVs and THz frequencies to ensure secure and reliable data transmission in dynamic environments.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"801-811"},"PeriodicalIF":5.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908855","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706650","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}

{"title":"Infrastructure Assisted Autonomous Driving: Research, Challenges, and Opportunities","authors":"Roshan George;Joseph Clancy;Tim Brophy;Ganesh Sistu;William O'Grady;Sunil Chandra;Fiachra Collins;Darragh Mullins;Edward Jones;Brian Deegan;Martin Glavin","doi":"10.1109/OJVT.2025.3542213","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3542213","url":null,"abstract":"Despite advancements in perception technology, achieving full autonomy in vehicles remains challenging partly due to limited situational awareness. Even with their sophisticated sensor arrays, autonomous vehicles often struggle to comprehend complex real-world environments due to the challenges associated with occlusion. A possible solution for addressing this limitation lies in the concept of vehicle-to-infrastructure cooperative driving, which enables vehicles to interact with various sensors implemented in the surrounding infrastructure. The infrastructure can share real-time data, such as traffic conditions, road hazards, and weather updates, facilitating safer and more efficient navigation. Within this framework, cooperative sensing is a crucial component, augmenting the onboard sensing capabilities of autonomous vehicles. Cooperative sensing surpasses traditional onboard sensors by leveraging a shared sensor network among vehicles and infrastructure. This approach mitigates challenges posed by occlusion, where objects are obscured from a vehicle's direct view. By pooling information from multiple sources, autonomous vehicles can gain a more comprehensive understanding of their surroundings, leading to enhanced safety and performance on the road. This study addresses a literature gap regarding information flow from real-world scenes to environmental models for cooperative V2I systems. It explores three core concepts essential for understanding the environment: sensing, perception, and mapping. This paper identifies the specific information required from infrastructure nodes, proposes an optimized sensor suite, discusses data processing algorithms, and investigates effective spatial model representations for cooperative sensing. This research informs the reader about the different challenges and opportunities associated with a V2I cooperative sensing system.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"662-716"},"PeriodicalIF":5.3,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594293","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}

{"title":"Convolutional Neural Network-Based Classification of Lithium-Ion Battery CAN Messages","authors":"Tero Niemi;Tomi Pitkäaho;Juha Röning","doi":"10.1109/OJVT.2025.3541382","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3541382","url":null,"abstract":"The lithium-ion battery Controller Area Network (CAN) messages are essential to battery monitoring, recycling, and second-life applications. However, the proprietary nature of database connection (DBC) files and the diversity of CAN message encodings across manufacturers pose significant challenges. This study proposes a convolutional neural network (CNN) based approach to classify battery-related CAN messages without reliance on proprietary DBC files. By analyzing data from four manufacturers and categorizing messages into three key groups—voltage and current, temperature and State of Charge (SoC), and configuration or other battery parameters, the CNN achieved an accuracy of 94.87% on unseen data. The model demonstrated robust performance, effectively generalizing across diverse CAN message formats. Practical validation confirmed the model's ability to identify key battery metrics reliably. This publication highlights the potential of deep learning to address proprietary data barriers, facilitating accessible and scalable battery monitoring and health assessment approaches. The findings contribute to advancing sustainable battery management practices, particularly for companies focusing on battery recycling and second-life applications, and pave the way for further research on leveraging temporal and expanded datasets to enhance classification accuracy and scope.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"790-800"},"PeriodicalIF":5.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10884024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706651","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}

{"title":"A Comparison of Spherical Neural Networks for Surround-View Fisheye Image Semantic Segmentation","authors":"Anam Manzoor;Reenu Mohandas;Anthony Scanlan;Eoin Martino Grua;Fiachra Collins;Ganesh Sistu;Ciarán Eising","doi":"10.1109/OJVT.2025.3541891","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3541891","url":null,"abstract":"Please check and confirm whether the authors affiliation in the first The automotive industry has made significant strides in enhancing road safety and enabling automated driving features through advanced computer vision techniques. This is particularly true for short-range vehicle automation, where non-linear fisheye cameras are commonly used. However, these cameras are challenged by optical distortions, known as fisheye geometric distortions, which lead to object deformation within the image and significant pixel distortion, particularly at the image periphery. Based on the observation that fisheye and spherical images exhibit at least superficially similar geometric characteristics, we investigate the applicability of spherical models—including Spherical Convolutional Neural Networks (CNNs) and Spherical Vision Transformers (ViTs)—to fisheye images, even though fisheye images are not truly spherical. We perform our comparison using fisheye datasets—<italic>Woodscape, SynWoodscape, and SynCityscapes</i> in autonomous driving scenarios, with a specific focus on the ability of spherical methods (Spherical CNNs and ViTs) to manage fisheye distortions and compared them against traditional non-spherical methods. Our findings indicate that spherical methods effectively address fisheye distortions without needing extra data augmentations. This results in better mean Intersection over Union (mIoU) scores, pixel accuracy, and better surround-view perception than other modern approaches for fisheye semantic segmentation. However, we also find that spherical methods have a greater tendency to overfit smaller datasets compared with non-spherical models. These advancements highlight how non-linear camera images can take advantage of spherical approximations through spherical models in autonomous driving.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"717-740"},"PeriodicalIF":5.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10884975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594274","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}

{"title":"Interference-Aware AAV-TBS Coordinated NOMA: Joint User Scheduling, Power Allocation and Trajectory Design","authors":"Haiyong Zeng;Rui Zhang;Xu Zhu;Yufei Jiang;Zhongxiang Wei;Fu-Chun Zheng","doi":"10.1109/OJVT.2025.3542088","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3542088","url":null,"abstract":"We propose an autonomous aerial vehicle (AAV)-terrestrial base station (TBS) coordinated non-orthogonal multiple access (NOMA) scheme where AAV as an air BS coordinates with TBS to serve the users at cell edge via coordinated multi-point, and investigate its resource allocation problem for interference management and system performance enhancement. With the proposed scheme, the interference links between TBS and AAV-served users are enabled to carry useful information, therefore, an enhanced degree of freedom is achieved, leading to a much higher sum-rate over the non-coordinated AAV-assisted NOMA systems where the interference of AAV-served users from TBS is extensively suppressed. Moreover, joint optimization of user scheduling, power allocation and AAV three-dimensional (3D) trajectory design is conducted to maximize the sum-rate of edge users while maintaining a high quality of service at cell-center users, with the consideration of imperfect channel estimation: a) A user scheduling principle dedicated for AAV-TBS coordinated NOMA systems is presented, based on which a two-step user scheduling and power allocation (USPA) algorithm is proposed, with the derivation of optimal power allocation solution; b) A joint USPA algorithm is proposed with closed-form results; c) Considering the line of sight (LoS) and non-LoS factors in the air to ground channel, the 3D trajectory of AAV is designed based on successive convex approximation. It achieves an enhanced system performance, while requiring a lower complexity than the former two-step algorithm.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"812-828"},"PeriodicalIF":5.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748908","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}

{"title":"A 3D Spatial Information Compression Based Deep Reinforcement Learning Technique for UAV Path Planning in Cluttered Environments","authors":"Zhipeng Wang;Soon Xin Ng;Mohammed El-Hajjar","doi":"10.1109/OJVT.2025.3540174","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3540174","url":null,"abstract":"Unmanned aerial vehicles (UAVs) can be considered in many applications, such as wireless communication, logistics transportation, agriculture and disaster prevention. The flexible maneuverability of UAVs also means that the UAV often operates in complex 3D environments, which requires efficient and reliable path planning system support. However, as a limited resource platform, the UAV systems cannot support highly complex path planning algorithms in lots of scenarios. In this paper, we propose a 3D spatial information compression (3DSIC) based deep reinforcement learning (DRL) algorithm for UAV path planning in cluttered 3D environments. Specifically, the proposed algorithm compresses the 3D spatial information to 2D through 3DSIC, and then combines the compressed 2D environment information with the current UAV layer spatial information to train UAV agents for path planning using neural networks. Additionally, the proposed 3DSIC is a plug and use module that can be combined with various DRL frameworks such as Deep Q-Network (DQN) and deep deterministic policy gradient (DDPG). Our simulation results show that the training efficiency of 3DSIC-DQN is 4.028 times higher than that directly implementing DQN in a <inline-formula><tex-math>$100 times 100 times 50$</tex-math></inline-formula> 3D cluttered environment. Furthermore, the training efficiency of 3DSIC-DDPG is 3.9 times higher than the traditional DDPG in the same environment. Moreover, 3DSIC combined with fast recurrent stochastic value gradient (FRSVG), which can be considered as the most state-of-the-art DRL algorithm for UAV path planning, exhibits 2.35 times faster training speed compared with the original FRSVG algorithm.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"647-661"},"PeriodicalIF":5.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10878448","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583247","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}

{"title":"2024 Index IEEE Open Journal of Vehicular Technology Vol. 5","authors":"","doi":"10.1109/OJVT.2025.3536782","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3536782","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1766-1790"},"PeriodicalIF":5.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105977","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}

{"title":"Design Issues of Hybrid Energy Storage Systems of Electric Vehicles According to Driving Profiles","authors":"Michal Frivaldsky;Matúš Danko;Tristan Schoenfelder;Martin Zavrel","doi":"10.1109/OJVT.2025.3536176","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3536176","url":null,"abstract":"This article examines the design challenges of hybrid energy storage systems (HESS) for electric vehicles (EVs), focusing on optimization based on driving profiles. Rising carbon dioxide emissions, especially in road traffic, require advanced energy solutions. In particular, electric vehicles offer great potential for reducing emissions in the transport sector. However, existing energy storage technologies such as lithium batteries have significant limitations in terms of power and energy density as well as cost efficiency, etc. To address these limitations, this study examines battery-SC hybrid systems, which represent a form of HESS. Using MATLAB Simulink, energy and power requirements are calculated for selected urban, combined, and motorway driving cycles. Based on the simulation results, which are obtained from mathematical vehicle dynamics models, this paper determines optimal configurations for battery and SC modules. Key findings highlight the trade-offs in performance, weight, and cost when designing HESS for varying vehicle classes. The study concludes that while HESS solutions significantly enhance energy efficiency and extend battery lifespan, their implementation remains complex and cost-intensive for medium and high-class vehicles. For lower-class vehicles, HESS offers an effective strategy to balance energy and power demands, contributing to sustainable transport solutions. The results provide important insights for the development of scalable and efficient hybrid storage systems that are designed for specific driving conditions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"619-631"},"PeriodicalIF":5.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10857387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521473","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}

{"title":"Stabilized Multi-Hop Route Construction Using a Modified Link Metric for Wi-SUN FAN Systems","authors":"Ryuichi Nagao;Daiki Hotta;Hiroko Masaki;Keiichi Mizutani;Hiroshi Harada","doi":"10.1109/OJVT.2025.3534842","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3534842","url":null,"abstract":"Wireless Smart Utility Network Field Area Network (Wi-SUN FAN) is a technical specification of Wi-SUN that introduces multi-hop machine-to-machine transmission for advanced smart city infrastructure. Wi-SUN FAN uses the Internet Protocol Version 6 (IPv6) Routing Protocol for Low-Power and Lossy Network (RPL) as the routing protocol and expected transmission count (ETX) as the routing metric to build multi-hop networks. ETX is used to convert number of communications into a link metric, which measures the quality of communication between nodes. This metric measures the relative distance to the root node via adjacent nodes to determine the parent node. However, this method of determining link metrics may cause nodes to frequently change their parents. If a node selects a parent with poor link quality, the communication reliability deteriorates; therefore, each node must appropriately select a candidate parent node. This article presents the transmission characteristics of Wi-SUN FANs and highlights the problems of conventional link metrics. Based on this, a novel method is proposed for calculating the link metric. The performed computer simulations verified the superiority of the proposed metric when the packet generation rate remained unaffected by the generation of control frames that switched the parent nodes. Furthermore, the transmission success rate of the media access control (MAC) frame was experimentally measured in an office building using Wi-SUN FAN communication modules based on the proposed method. The evaluation confirmed that the proposed link metric improved the minimum MAC frame transmission success rate by 24.2% and the average success rate by 10.4%.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"607-618"},"PeriodicalIF":5.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855464","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489077","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}