IEEE Open Journal of Vehicular Technology最新文献

{"title":"Segmentation-Based Depth Correction Methods for Near Field iToF LiDAR in Motion State","authors":"Mena Nagiub;Thorsten Beuth;Ganesh Sistu;Heinrich Gotzig;Ciarán Eising","doi":"10.1109/OJVT.2025.3565811","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3565811","url":null,"abstract":"This paper presents two approaches to enhance depth correction of the indirect time-of-flight (iToF) LiDAR sensors during a motion state, addressing the challenges of depth ambiguity and motion blur noise. iToF sensors are a key component in modern automotive applications, providing dense depth information for short-range vision applications for autonomous driving and Advanced Driver-Assistance Systems (ADAS). However, the periodic nature of iToF signals leads to depth ambiguity, making it challenging to measure distances accurately, especially in complex environments. Moreover, iToF sensors suffer from motion blur noise when the vehicle is in motion, compromising the accuracy of depth measurements. The proposed methods, which rely on depth correction indirectly through predicting depth bins using segmentation techniques, offer a promising alternative to direct depth regression. By focusing on segmentation-driven prediction, these new methods open up possibilities for more robust and precise depth correction in LiDAR sensor technology, potentially revolutionizing various applications that rely on accurate depth sensing. The results demonstrate the superiority of segmentation methods for depth frames based on 4 DCS samples, highlighting the potential impact and significance of this research in the field and the potential revolutionizing effect of these solutions on various applications.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1262-1279"},"PeriodicalIF":5.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139941","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":"Leveraging Frozen Foundation Models and Multimodal Fusion for BEV Segmentation and Occupancy Prediction","authors":"Seamie Hayes;Ganesh Sistu;Ciarán Eising","doi":"10.1109/OJVT.2025.3563677","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3563677","url":null,"abstract":"In Bird's Eye View perception, significant emphasis is placed on deploying well-performing, convoluted model architectures and leveraging as many sensor modalities as possible to reach maximal performance. This paper investigates whether foundation models and multi-sensor deployments are essential for enhancing BEV perception. We examine the relative importance of advanced feature extraction versus the number of sensor modalities and assess whether foundation models can address feature extraction limitations and reduce the need for extensive training data. Specifically, incorporating the self-supervised DINOv2 for feature extraction and Metric3Dv2 for depth estimation into the Lift-Splat-Shoot framework results in a 7.4 IoU point increase in vehicle segmentation, representing a relative improvement of 22.4%, while requiring only half the training data and iterations compared to the original model. Furthermore, using Metric3Dv2’s depth maps as a pseudo-LiDAR point cloud within the Simple-BEV model improves IoU by 2.9 points, marking a 6.1% relative increase compared to the Camera-only setup. Finally, we extend the famous Gaussian Splatting BEV perception models, GaussianFormer and GaussianOcc, through multimodal deployment. The addition of LiDAR information in GaussianFormer results in a 9.4-point increase in mIoU, a 48.7% improvement over the Camera-only model, nearing state-of-the-art multimodal performance even with limited LiDAR scans. In the self-supervised GaussianOcc model, incorporating LiDAR leads to a 0.36-point increase in mIoU, representing a 3.6% improvement over the Camera-only model. This limited gain can be attributed to the absence of LiDAR encoding and the self-supervised nature of the model. Overall, our findings highlight the critical role of foundation models and multi-sensor integration in advancing BEV perception. By leveraging sophisticated foundation models and multi-sensor deployment, we can further model performance and reduce data requirements, addressing key challenges in BEV perception.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1241-1261"},"PeriodicalIF":5.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10974666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108299","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":"Optimal Planning of Electrified Road Structures Using Queuing Models","authors":"Eiman ElGhanam;Mohamed S. Hassan;Ahmed M. Benaya;Ahmed Osman","doi":"10.1109/OJVT.2025.3563109","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3563109","url":null,"abstract":"Dynamic wireless charging (DWC) of electric vehicles (EVs) is an attractive solution to the EV driving range limitations and the associated range anxiety problem. In DWC, charging lanes are deployed along city roads to wirelessly supply the needed charging power to EVs during their motion. However, due to the high construction costs of electrified road structures (ERS) with wireless charging lanes and the likely increase in the energy demand by EV owners, an optimal deployment plan is essential to maximize the net returns to the infrastructure owners and ensure maximal demand coverage. Therefore, to formulate a reliable optimization framework, accurate modeling of the charging lane operation is needed at each potential lane location. In this work, the traffic behavior at different locations is modeled analytically using queuing theory. This accurately represents the desired flow of vehicles on the charging lanes and provides a reliable estimate of the EV charging demand, particularly due to the lack of EV traffic flow datasets with the currently low but expanding penetration of EVs. A multi-objective optimization framework is then developed based on the established traffic model to determine the most optimal locations for the deployment of DWC lanes within a smart city infrastructure. The model is tested on 24 candidate roads selected from the United Arab Emirates map and the corresponding optimal locations are determined by solving the optimization problem on GAMS/CONOPT solver. Sensitivity analysis is also conducted to validate the results of the proposed model.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1222-1240"},"PeriodicalIF":5.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10971955","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108334","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":"Collision Avoidance Strategies for Uncrewed Aircraft Systems in Structured Airspace Using a Roundabout Intersection","authors":"Skyler Hawkins;Jaya Sravani Mandapaka;Logan McCorkendale;Zachary McCorkendale;Kamesh Namuduri;Shane Nicoll","doi":"10.1109/OJVT.2025.3562581","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3562581","url":null,"abstract":"The increasing size of the Uncrewed Aircraft System (UAS) ecosystem necessitates effective infrastructure and Collision Avoidance (CA) systems to facilitate high-density UAS traffic in urban environments. Unfortunately, current-generation Air Traffic Management (ATM) and CA systems used for crewed aircraft cannot be used with UAS due to scalability issues and operational constraints. This paper introduces a novel UAS intersection called the Roundabout, specifically designed for facilitating UAS traffic in structured airspace. This paper also proposes the methodology for a CA system based on Vehicle-to-Vehicle (V2V) communications, specifically UAS-to-UAS (U2U) communications, for Tactical Deconfliction (TD) between UAS in real-time. Simulation results demonstrate the system's efficacy in handling the deconfliction process between two quadrotor UAS and can be expected to generalize to deconfliction scenarios involving UAS of all types, given that the proper control systems and trajectory generation methods are available. Overall, these findings highlight the Roundabout's potential for enhancing UAS operations in the National Airspace System (NAS).","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1193-1208"},"PeriodicalIF":5.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970436","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131710","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":"Performance Analyses of MRT/MRC in NOMA Full-Duplex Relay Networks With Residual Hardware Impairments","authors":"Mesut Toka;Eray Güven;Güneş Karabulut Kurt;Oğuz Kucur","doi":"10.1109/OJVT.2025.3560515","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3560515","url":null,"abstract":"This paper analyzes the performance of maximum-ratio transmission (MRT)/maximum-ratio combining (MRC) scheme in a dual-hop non-orthogonal multiple access (NOMA) full-duplex (FD) relay networks in the presence of residual hardware impairments (RHIs). The effects of channel estimation errors (CEEs) and imperfect successive interference cancellation are also considered to deal with a more realistic scenario. In the network, the base station and multiple users utilize MRT and MRC, respectively, while a dedicated relay operates in amplify-and-forward mode. Exact outage probability (OP) expression is derived for Nakagami-<inline-formula><tex-math>$m$</tex-math></inline-formula> fading channels. Furthermore, tight lower bound and asymptotic expressions are also derived to provide further insights in terms of diversity order and array gain. The investigated network has been compared to half-duplex (HD)-NOMA and FD-orthogonal multiple access counterparts. The analytical results validated by simulations and test-bed implementations (by using software defined radios) demonstrate the importance of loop-interference cancellation process in the FD relay for the investigated system to perform better than HD-NOMA counterpart. Also, a performance trade-off between the MRT and MRC schemes is observed under CEE effects among users. Furthermore, it is shown that RHIs have a significant effect on the performance of users with lower power coefficients, however it does not change the diversity order. RHIs and CEEs have the most and least deterioration effects on the system performance, respectively.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1178-1192"},"PeriodicalIF":5.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964195","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131615","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":"An Effective Tree-Structured AI Model for Reducing Overhead of Life Cycle Management in Wireless Communication","authors":"Yingshuang Bai;Zhaohui Huang;Chen Sun;Yujie Zhang;Tao Cui;Samuel Atungsiri","doi":"10.1109/OJVT.2025.3560189","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3560189","url":null,"abstract":"Artificial intelligence (AI) has been widely applied across various industries, including wireless communication. AI has been a topic of extensive discussion within the 3rd Generation Partnership Project (3GPP), particularly in the context of the physical layer. It involves applications such as beam management, positioning accuracy enhancement, and channel state information (CSI) feedback improvement. Evaluation results from various companies indicate significant gains in beam management and positioning through AI integration. While AI can replace traditional mechanisms to enhance performance, it also introduces new overheads. For example, the introduction of AI models necessitates addressing model life cycle management (LCM) issues, such as model identification, activation/deactivation, monitoring, updating/finetuning, selection, switching. These operations result in a significant amount of overhead. In this paper, we present the progress of model LCM in 3GPP, and also propose a tree-structured model to reduce the overhead associated with LCM operations. This model can be used in scenarios where multiple models serve the same functionality by merging similar structures, thereby saving storage space, and making the processes of model switching, expansion, and deletion more effective. We also conduct simulations to demonstrate that our approach maintains stable AI model performance while simplifying the model structure.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1100-1107"},"PeriodicalIF":5.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10962556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902662","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 Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and Tracking","authors":"Mario Rodríguez-Arozamena;Jose Matute;Javier Araluce;Lukas Kuschnig;Christoph Pilz;Markus Schratter;Joshué Pérez Rastelli;Asier Zubizarreta","doi":"10.1109/OJVT.2025.3560198","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3560198","url":null,"abstract":"Integrating Automated Vehicles (AVs) into everyday traffic is an ongoing challenge. Ensuring the safety of all involved agents, even in the presence of system failures, is crucial, especially in urban environments. This paper introduces a fallback-oriented localization algorithm for AVs designed to operate during main localization source failures. The method leverages stationary vehicles as dynamic landmarks, identified through the perception module, despite their initially unknown positions. By tracking relative positions before failure and applying trilateration, the algorithm estimates the ego vehicle's position. The proposed algorithm is evaluated through simulations, a real-world dataset, and practical tests on two vehicle models. The results include an average trajectory error of 0.62 m and 1.58 deg compared to the ground truth over different fallback maneuvers. This translates into an average relative translational error of 1.65% and a relative rotational error of 0.05 deg/m, improving the performance of an IMU-based dead reckoning and, hence, providing localization for performing safe stop maneuvers.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1085-1099"},"PeriodicalIF":5.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10963758","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902711","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":"Performance Analysis of Sensing-Assisted Communications in Aerial-to-Ground Networks","authors":"Petros S. Bithas;Athanasios G. Kanatas;Konstantinos Maliatsos","doi":"10.1109/OJVT.2025.3559698","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3559698","url":null,"abstract":"Joint communication and sensing (JCAS) technology allows the coexistence of sensing and communication capabilities within the same frequency band without causing mutual interference. Aerial-to-ground wireless communication networks offer additional flexibility for communication users and sensing targets through the dynamic positioning of uncrewed aerial vehicles (UAVs), resulting in an extra degree of freedom to alleviate the challenges imposed by the dynamic characteristics of the wireless propagation channel. In this paper, the performance of a sensing-assisted aerial communication network is analytically investigated in scenarios with realistic assumptions for the channel and system model. Indeed, the presented analysis considers independent but non-identically distributed shadowing effects, non-isotropic antennas, and a generic statistical distribution for the radar cross section of sensing targets. Analytical expressions are derived for the statistics of the received signal-to-interference plus noise ratio (SINR), for both sensing and communication functionalities, while simpler expressions for special cases and asymptotic results are also obtained. Based on the analytical derivations, communication and sensing performance have been evaluated using, respectively, the outage and coverage probabilities and the ergodic radar estimation rate and detection probability. Numerical and simulation results demonstrate the accuracy of the proposed analysis and reveal how factors like non-identical distributed statistics of shadowing, small scale fading, and interference influence the performance of the system.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1140-1151"},"PeriodicalIF":5.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960654","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913396","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":"Smart Electric Vehicle Charging Algorithm to Reduce the Impact on Power Grids: A Reinforcement Learning Based Methodology","authors":"Federico Rossi;Cesar Diaz-Londono;Yang Li;Changfu Zou;Giambattista Gruosso","doi":"10.1109/OJVT.2025.3559237","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3559237","url":null,"abstract":"The increasing penetration of electric vehicles (EVs) presents a significant challenge for power grid management, particularly in maintaining network stability and optimizing energy costs. Existing model predictive control (MPC)-based approaches for EV charging and discharging scheduling often struggle to balance computational efficiency with real-time operationability. This gap highlights the need for more advanced methods that can effectively mitigate the impact of EV activities on power grids without oversimplifying system dynamics. Here, we propose a novel scheduling methodology using a pre-trained Reinforcement Learning (RL) framework to address this challenge. The method integrates real grid simulations to monitor critical electrical points and variables while simplifying analysis by excluding the influence of real grid dynamics. The proposed approach formulates the scheduling problem to minimize costs, maximize rewards from ancillary service delivery, and mitigate network overloads at specified grid nodes. The methodology is validated on a benchmark electric grid, where realistic charging station utilization scenarios are simulated. The results demonstrate the method's robustness and ability to efficiently cope with the EV smart scheduling problem.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1072-1084"},"PeriodicalIF":5.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902642","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":"Advanced Temporal Convolutional Network Framework for Intrusion Detection in Electric Vehicle Charging Stations","authors":"Ikram Benfarhat;Vik Tor Goh;Chun Lim Siow;It Ee Lee;Muhammad Sheraz;Eng Eng Ngu;Teong Chee Chuah","doi":"10.1109/OJVT.2025.3559421","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3559421","url":null,"abstract":"Electric Vehicle Charging Station (EVCS) systems have become increasingly critical to the energy and transportation sectors. The detection of various attacks in EVCS, including data interception in the Open Charge Point Protocol (OCPP), poses substantial cybersecurity challenges that existing deep learning methods struggle to address effectively. This work investigates the impact of 16 types of attacks on EVCS, such as denial-of-service (DoS), reconnaissance, cryptojacking, and backdoor attacks. To address these threats, we propose an innovative model designed to identify diverse cyber threats targeting EVCS. The proposed Temporal Convolutional Network (TCN)-based Intrusion Detection System (IDS) architecture integrates four key innovations: multi-receptive fields, a gating mechanism, iterative dilation, and a self-attention mechanism combined with a Squeeze-and-Excitation (SE) block to recalibrate feature responses by explicitly modeling interactions between different channels. The proposed model effectively processes multiple temporal scales, regulates the flow of information, adapts to varying time steps, and focuses on essential components of time-series data. Experimental evaluations demonstrate that the proposed model outperforms state-of-the-art methods in terms of accuracy and detection rates across all 16 attack types in the CICEVSE2024 dataset, which comprises extensive attack vectors and variants associated with the OCPP. The proposed approach achieves higher accuracy compared to other TCN variants and exhibits high resilience against complex attacks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1033-1050"},"PeriodicalIF":5.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888328","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}