IEEE Open Journal of Vehicular Technology最新文献

{"title":"Detection of Multiple Small Biased GPS Spoofing Attacks on Autonomous Vehicles Using Time Series Analysis","authors":"Ahmad Mohammadi;Reza Ahmari;Vahid Hemmati;Frederick Owusu-Ambrose;Mahmoud Nabil Mahmoud;Parham Kebria;Abdollah Homaifar","doi":"10.1109/OJVT.2025.3559461","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3559461","url":null,"abstract":"This research introduces an algorithm to identify GPS spoofing attacks in Autonomous Vehicles (AV). It uses data from onboard sensors such as speedometers and gyroscopes, which are integrated and analyzed using a Neural Network (NN). This network predicts the vehicle's future displacement and compares these predictions with GPS data to identify potential spoofing attacks such as turn-by-turn, stop, and overshoot incidents. Additionally, the same sensor data is evaluated using an analytical model based on the vehicle's dynamic equations to assess its position and speed against GPS information. To facilitate real-time detection, a threshold is pre-established from clean datasets, which determines the largest expected differences between sensor readings and GPS data. This threshold is then used for ongoing real-time assessments to detect spoofing activities. Moreover, the algorithm can detect multiple small biased attacks, incremental attacks that may not initially exceed the established threshold but eventually result in significant discrepancies in GPS and Inertial Measurement Unit (IMU) reported displacement and speeds. This detection is facilitated through time series analysis at 25 and 50 s intervals to build a profile of data errors and distribution to predict the probability of such attacks. To evaluate the algorithm's effectiveness, five different test datasets depicting four types of spoofing scenarios—turn-by-turn, overshoot, stop, and multiple small biased attacks—were created using data from the publicly accessible Honda Research Institute Driving Dataset (HDD). The analysis shows that the model accurately detects these types of attacks with average accuracies of 98.62<inline-formula><tex-math>$pm$</tex-math></inline-formula>1%, 99.96<inline-formula><tex-math>$pm$</tex-math></inline-formula>0.1%, 99.88<inline-formula><tex-math>$pm$</tex-math></inline-formula>0.1%, and 95.92<inline-formula><tex-math>$pm$</tex-math></inline-formula>1.7% respectively.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1152-1163"},"PeriodicalIF":5.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937949","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":"Semi-Blind Channel Estimation and Achievable Rate Analysis for Uplink RIS-Enhanced Multi-User Networks","authors":"Samar I. Farghaly;Mohamed I. Ismail;Mostafa M. Fouda;Ahmed S. Alwakeel","doi":"10.1109/OJVT.2025.3557314","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3557314","url":null,"abstract":"Future wireless networks could benefit from the energy-efficient, low-latency, and scalable deployments that Reconfigurable Intelligent Surfaces (RISs) offer. However, the creation of an effective low overhead channel estimate technique is a major obstacle in RIS-assisted systems, especially given the high number of RIS components and intrinsic hardware constraints. This research examines the uplink of a RIS-empowered multi-user MIMO communication system and presents a novel semi-blind channel estimate approach. Unlike current approaches, which rely on pilot-based channel estimation, our methodology uses data to estimate channels, considerably enhancing the achievable rate. We provide a closed-form deterministic expression for the uplink achievable rate in actual settings where the channel state information (CSI) must be estimated rather than assumed perfect. The results of the simulations show that the formula obtained is accurate, with a close alignment between the deterministic and actual achievable rates (generally between 2 5% deviations). The proposed approach outperforms traditional approaches, resulting in rate increases of up to 35–40%, especially in instances with more RIS elements. These findings illustrate RIS technology's tremendous potential to improve system capacity and coverage, providing useful insights for optimizing RIS adoption in future wireless networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1108-1139"},"PeriodicalIF":5.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908387","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":"DelAwareCol: Delay Aware Collaborative Perception","authors":"Ahmed N. Ahmed;Siegfried Mercelis;Ali Anwar","doi":"10.1109/OJVT.2025.3556381","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3556381","url":null,"abstract":"Multi-agent collaborative perception has gained significant attention due to its ability to overcome the challenges stemming from the limited line-of-sight visibility of individual agents that raised safety concerns for autonomous navigation. Despite notable progress in collaborative perception, several persistent challenges hinder optimal performance, such as the size of data being shared, communication delays, computationally expensive collaboration mechanisms, and spatial misalignment. To address these challenges, we propose DelAwareCol, a versatile collaborative perception framework that tackles the transmission delay between connected agents in real-life autonomous driving. Our framework introduces three key modules designed to balance perception performance with communication bandwidth and delay. Firstly, an intra-agent information aggregation module captures valuable semantic cues within the temporal context to enhance the local representation of each ego agent. Secondly, an inter-agent information aggregation module manages inter-agent interactions and spatial relationships, addressing common vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) issues, such as spatial misalignment, asynchronous information sharing, and pose errors. Thirdly, an adaptive fusion mechanism integrates multi-source representations based on dynamic contributions from different agents. The proposed framework is validated on large-scale simulated and real-life collaborative perception datasets OPV2V, V2XSet, and V2VReal. Our experimental results demonstrate that DelAwareCol achieved state-of-the-art performance in collaborative object detection, maintaining robust performance in the presence of high latency and localization error.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1164-1177"},"PeriodicalIF":5.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10946103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937980","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":"Space-Air-Ground Integrated Networks: Their Channel Model and Performance Analysis","authors":"Chao Zhang;Qingchao Li;Chao Xu;Lie-Liang Yang;Lajos Hanzo","doi":"10.1109/OJVT.2025.3575360","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3575360","url":null,"abstract":"Given their extensive geographic coverage, low Earth orbit (LEO) satellites are envisioned to find their way into next-generation (6G) wireless communications. This paper explores space-air-ground integrated networks (SAGINs) leveraging LEOs to support terrestrial and non-terrestrial users. We first propose a practical satellite-ground channel model that incorporates five key aspects: 1) the small-scale fading characterized by the Shadowed-Rician distribution in terms of the Rician factor <inline-formula><tex-math>$K$</tex-math></inline-formula>, 2) the path loss effect of bending rays due to atmospheric refraction, 3) the molecular absorption modelled by the Beer-Lambert law, 4) the Doppler effects including the Earth's rotation, and 5) the impact of weather conditions according to the International Telecommunication Union Recommendations (ITU-R). Harnessing the proposed model, we analyze the long-term performance of the SAGIN considered. Explicitly, the closed-form expressions of both the outage probability and of the ergodic rates are derived. Additionally, the upper bounds of bit-error rates and of the Goodput are investigated. The numerical results yield the following insights: 1) The shadowing effect and the ratio between the line-of-sight and scattering components can be conveniently modelled by the factors of <inline-formula><tex-math>$K$</tex-math></inline-formula> and <inline-formula><tex-math>$m$</tex-math></inline-formula> in the proposed Shadowed-Rician small-scale fading model. 2) The atmospheric refraction has a modest effect on the path loss. 3) When calculating the transmission distance of waves, Earth's curvature and its geometric relationship with the satellites must be considered, particularly at small elevation angles. 3) High-frequency carriers suffer from substantial path loss, and 4) the Goodput metric is eminently suitable for characterizing the performance of different coding as well as modulation methods and of the estimation error of the Doppler effects.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1501-1523"},"PeriodicalIF":5.3,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018358","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314753","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":"Continuous Hairpin Winding Technology for Electric Machines Enabling Net Zero Transportation: A Comprehensive Review","authors":"Hailin Huang;Tianjie Zou;Mauro Di Nardo;Amedeo Vannini;Anh Thanh Huynh;Michele Degano;David Gerada;Tao Yang;Chris Gerada","doi":"10.1109/OJVT.2025.3575187","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3575187","url":null,"abstract":"Featured by low power losses and strong heat dissipation capability, hairpin winding has been widely accepted as a key enabler of boosting performance for traction/propulsion electrical machines in automotive and aerospace sectors. Continuous hairpin winding (CHW), distinguished by its further improvement brought to machines’ key performance indicators, being power density, efficiency, and reliability, is emerging as a more promising winding solution. This paper will provide a critical technology review on CHW, with focus on highlighting its different features compared with those of other typical winding types, manufacturing/assembly process, layout design rules, as well as current and future development trends. Based on illustration of the unique winding process, the so-called “radial shift” feature that inherently exists in CHW will be introduced. The key elements in winding layout development, including transposition, terminal and jumper connections, parallel branch number, will be summarised for CHW. Moreover, new technology bricks and research ideas that strive to tackle manufacturing challenges, enhance design flexibility, as well as improve overall performance have been highlighted. Finally, the paper concludes by proposing future research directions, with the vision of increasing its Technology Readiness Level (TRL) for future net-zero transportation.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1481-1500"},"PeriodicalIF":5.3,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314751","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":"Graph Neural Network Aided Detection for the Multi-User Multi-Dimensional Index Modulated Uplink","authors":"Xinyu Feng;Mohammed El-Hajjar;Chao Xu;Lajos Hanzo","doi":"10.1109/OJVT.2025.3574934","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3574934","url":null,"abstract":"The concept of Compressed Sensing-aided Space-Frequency Index Modulation (CS-SFIM) is conceived for the Large-Scale Multi-User Multiple-Input Multiple-Output Uplink (LS-MU-MIMO-UL) of Next-Generation (NG) networks. Explicitly, in CS-SFIM, the information bits are mapped to both spatial- and frequency-domain indices, where we treat the activation patterns of the transmit antennas and of the subcarriers separately. Serving a large number of users in an MU-MIMO-UL system leads to substantial Multi-User Interference (MUI). Hence, we design the Space-Frequency (SF) domain matrix as a joint factor graph, where the Approximate Message Passing (AMP) and Expectation Propagation (EP) based MU detectors can be utilized. In the LS-MU-MIMO-UL scenario considered, the proposed system uses optimal Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detectors as benchmarks for comparison with the proposed MP-based detectors. These MP-based detectors significantly reduce the detection complexity compared to ML detection, making the design eminently suitable for LS-MU scenarios. To further reduce the detection complexity and improve the detection performance, we propose a pair of Graph Neural Network (GNN) based detectors, which rely on the orthogonal AMP (OAMP) and on the EP algorithm, which we refer to as the GNN-AMP and GEPNet detectors, respectively. The GEPNet detector maximizes the detection performance, while the GNN-AMP detector strikes a performance versus complexity trade-off. The GNN is trained for a single system configuration and yet it can be used for any number of users in the system. The simulation results show that the GNN-based detector approaches the ML performance in various configurations.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1593-1612"},"PeriodicalIF":5.3,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11017516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481958","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":"Techno-Economic Impacts of Battery Replacement for Different EV Usage Patterns","authors":"Martin Chaud;Ronan German;Eric Hittinger;Alain Bouscayrol;Elodie Castex","doi":"10.1109/OJVT.2025.3574574","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3574574","url":null,"abstract":"The driving range of an electric vehicle is limited by the energy of the battery. It decreases over time due to ageing. The vehicle user can replace the battery after some operational period to restore the initial capacity. This paper studies the impact of battery replacements from the driving range and economic perspectives. A global vehicle model is defined. It considers the ageing, the economic value and the electro thermal model of the battery in interaction with the vehicle usage. A Renault Zoe is chosen as a reference vehicle. Three different usage scenarios are defined: low-intensity urban, medium-intensity rural and high-intensity motorway driving cycles for a 12-year vehicle lifespan. The results show that replacing the battery at least one time is necessary for high motorway daily usage. Frequently replacing the battery (every three years) has a positive impact on the driving range while increasing the battery cost of ownership. A high annual mileage can have more impact than battery replacements. User trade-offs between driving range and battery cost are presented.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1650-1660"},"PeriodicalIF":5.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11016803","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502855","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 Minimization in Beyond-Diagonal RIS-Assisted MIMO Interference Channels","authors":"Ignacio Santamaria;Mohammad Soleymani;Eduard Jorswieck;Jesús Gutiérrez","doi":"10.1109/OJVT.2025.3555425","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3555425","url":null,"abstract":"This paper proposes a two-stage approach for passive and active beamforming in multiple-input multiple-output (MIMO) interference channels (ICs) assisted by a beyond-diagonal reconfigurable intelligent surface (BD-RIS). In the first stage, the passive BD-RIS is designed to minimize the aggregate interference power at all receivers, a cost function called interference leakage (IL). To this end, we propose an optimization algorithm in the manifold of unitary matrices and a suboptimal but computationally efficient solution. In the second stage, users' active precoders are designed under different criteria such as minimizing the IL (min-IL), maximizing the signal-to-interference-plus-noise ratio (max-SINR), or maximizing the sum rate (max-SR). The residual interference not cancelled by the BD-RIS is treated as noise by the precoders. Our simulation results show that the max-SR precoders provide more than <inline-formula><tex-math>$20%$</tex-math></inline-formula> sum rate improvement compared to other designs, especially when the BD-RIS has a moderate number of elements and users transmit with high power, in which case the residual interference is still significant.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1005-1017"},"PeriodicalIF":5.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10943135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850876","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":"MMTraP: Multi-Sensor Multi-Agent Trajectory Prediction in BEV","authors":"Sushil Sharma;Arindam Das;Ganesh Sistu;Mark Halton;Ciarán Eising","doi":"10.1109/OJVT.2025.3574385","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3574385","url":null,"abstract":"Accurate detection and trajectory prediction of moving vehicles are essential for motion planning in autonomous driving systems. While traffic regulations provide clear boundaries, real-world scenarios remain unpredictable due to the complex interactions between vehicles. This challenge has driven significant interest in learning-based approaches for trajectory prediction. We present <bold>MMTraP:</b> <bold>M</b>ulti-Sensor and <bold>M</b>ulti-Agent <bold>Tra</b>jectory <bold>P</b>rediction in BEV. This method integrates camera, LiDAR, and radar data to create detailed Bird's-Eye-View representations of driving scenes. Our approach employs a hierarchical vector transformer architecture that first detects and classifies vehicle motion patterns before predicting future trajectories through spatiotemporal relationship modeling. This work specifically focuses on vehicle interactions and environmental constraints. Despite its significance, multi-agent trajectory prediction and moving object segmentation are still underexplored in the literature, especially in real-time applications. Our method leverages multisensor fusion to obtain precise BEV representations and predict vehicle trajectories. Our multi-sensor fusion approach achieves the highest vehicle Intersection over Union (IoU) of 63.23% and an overall mean IoU (mIoU) of 64.63%, demonstrating its effectiveness in utilizing all available sensor modalities. Additionally, we demonstrate vehicle segmentation and trajectory prediction capabilities across various lighting and weather conditions. The proposed approach has been rigorously evaluated using the nuScenes dataset. Results show that our method improves the accuracy of trajectory predictions and outperforms state-of-the-art techniques, particularly in challenging environments such as congested urban areas. For instance, in complex traffic scenarios, our approach achieves a relative improvement of 5% in trajectory prediction accuracy compared to baseline methods. This work advances vehicle-focused prediction systems by integrating multi-sensor BEV representation and interaction-aware transformers. Our approach shows promise in enhancing the reliability and accuracy of trajectory predictions for autonomous driving applications, potentially improving overall safety and efficiency in diverse driving environments.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1551-1567"},"PeriodicalIF":5.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11016806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367022","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":"OTFS-Assisted Sensing Adaptive Cruise Control for Highways: A Reinforcement Learning Approach","authors":"Yulin Liu;Xiaoqi Zhang;Jun Wu;Qingqing Cheng","doi":"10.1109/OJVT.2025.3574223","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3574223","url":null,"abstract":"In this paper, we propose a novel channel estimation approach and driving decision method for adaptive cruise control (ACC) systems for vehicular networks, leveraging the properties of deep learning, reinforcement learning, and orthogonal time frequency space (OTFS) modulation. To achieve that, we propose to leverage deep learning (DL) to estimate motion parameters. Subsequently, we develop a reinforcement learning method to process the obtained target motion information to enable adaptive vehicle-following strategies. This ensures robust decision-making and precise control under dynamic and uncertain driving conditions, achieving superior performance in terms of both accuracy and reliability.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1861-1871"},"PeriodicalIF":5.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11016009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671200","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}