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":"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":"Exploring the Impact of Bistatic Target Reflectivity in ISAC-Enabled V2V Setup Across Diverse Geometrical Road Layouts","authors":"Aamir Ullah Khan;Saw James Mint;Syed Najaf Haider Shah;Christian Schneider;Joerg Robert","doi":"10.1109/OJVT.2025.3554365","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3554365","url":null,"abstract":"Integrated Sensing and Communication (ISAC) is an intriguing emerging research area that combines radar sensing and communication functionalities in a unified platform, capitalizing on shared aspects of signal processing, spectrum utilization, and system design. For sensing applications, the reflectivity of objects between Transmitter (TX) and Receiver (RX) is crucial. It is normally modeled as a uniform scatterer or a group of uniform scatterers in wireless channels. These models do not take into account the dependence of reflectivity on the aspect angles of incident and scattering waves, the composed material, and the geometry of the objects. Therefore, we model the reflectivity of target vehicles using their bistatic Radar Cross Section (RCS), as in radar sensing, within a Vehicle to Vehicle (V2V) setup under the Integrated Sensing and Communication (ISAC) framework. Moreover, we consider constant and variable bistatic Target Reflectivity (TR) integrated setups with two diverse traffic scenarios. These traffic scenarios are modeled to be realistic, with diverse geometrical road layouts, variable vehicle velocities, distinct vehicle positions, and the presence of Diffuse (DI) scattering components. Then, we inspect the impact of the bistatic TR on the behavior of the wireless channel and target detection capability. The variable TR integrated setup leads to a more accurate realization of the scenario, leading to outcomes that closely resemble real-world conditions. The results show the substantial impact of the geometrical setup on the distribution of TR, which emphasizes the need to integrate TR into ISAC-enabled V2V channel models.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"948-968"},"PeriodicalIF":5.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830467","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":"Analysis of the Impact of Rain on Perception in Automated Vehicle Applications","authors":"Tim Brophy;Darragh Mullins;Ashkan Parsi;Jonathan Horgan;Enda Ward;Patrick Denny;Ciarán Eising;Brian Deegan;Martin Glavin;Edward Jones","doi":"10.1109/OJVT.2025.3553718","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3553718","url":null,"abstract":"The reliable performance of object detection perception algorithms in automated vehicles under adverse conditions such as rain is critical for maintaining vulnerable road user safety. Visible-spectrum cameras provide a rich source of information and are cost-effective compared with other sensors; however, their performance can degrade under adverse environmental conditions. Despite the general consensus that the object detection performance in computer vision is adversely affected by rain, there is a relative lack of research investigating this relationship in detail. This study investigates the performance of object detection under rain conditions, focusing on algorithm performance and low-level object characteristics. Using the publicly available BDD100 k dataset, this study examines object detection performance across multiple deep-learning object detection architectures, analyzing error types and image characteristics under rain and no rain conditions. In addition, statistical methods were used to compare image-level metrics to determine statistical significance. The results reveal that rain is not detrimental to object detection performance, and in some cases, better performance is observed. For some models, medium-sized objects experience improved detection and classification under rain conditions, while large objects experience a slight decline in performance. The error analysis shows an increase in localization errors and a decrease in classification errors. The object-level analysis revealed statistically significant changes in the contrast-to-noise ratio, entropy, mean pixel value, pixel variance, hue, saturation, and value, with hue and saturation experiencing the most significant changes. This study highlights the need for more detailed weather labeling in datasets to fully understand the nuances of the relationship between rain and object detection.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1018-1032"},"PeriodicalIF":5.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937197","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856345","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":"Aerial-Terrestrial Heterogeneous Networks for Urban Air Mobility: A Performance Analysis","authors":"Abdullah Abu Zaid;Baha Eddine Youcef Belmekki;Mohamed-Slim Alouini","doi":"10.1109/OJVT.2025.3551209","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3551209","url":null,"abstract":"Urban air mobility (UAM) is increasingly capturing the attention of researchers and industry experts, as it holds the promise of providing faster and more economical solutions for urban commuting. Ensuring reliable communication for UAM aircraft is of paramount importance in maintaining operational safety. To that end, we use stochastic geometry tools to analyze the joint uplink-downlink coverage probability of an integrated aerial-terrestrial heterogeneous network (HetNet) for UAM aircraft, specifically electric vertical takeoff and landing (eVTOL) vehicles. We assume eVTOLs travel on predefined air corridors which are modeled as a Poisson line process (PLP). Furthermore, we model the spatial distribution of eVTOLs as a Matern hardcore process (MHCP) with a designated safety distance. We model the aerial base stations (ABSs) as a two-dimensional (2D) binomial point process (BPP), and the terrestrial base stations (TBSs) as a 2D Poisson point process (PPP). We use a suitable air-to-ground channel model to include line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions. In the paper, we derive distance distributions to the closest ABS, LOS TBS, and NLOS TBS to a typical eVTOL, then we provide the association probability of each BS. Furthermore, we characterize the uplink interference and derive Laplace transforms for the PLP-MHCP distributed eVTOLs. Finally, we derive the coverage probability of the overall HetNet and carry out Monte Carlo simulations to validate our expressions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"912-926"},"PeriodicalIF":5.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10925893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817904","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":"URLLC Over Slow Fading Channels: K-Repetition Versus Multi-Connectivity","authors":"Qingjiao Song;Fu-Chun Zheng;Jingjing Luo","doi":"10.1109/OJVT.2025.3569187","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3569187","url":null,"abstract":"In this work, we analyze the performance of the uplink ultra-reliable and low-latency communications (URLLC) for the low-mobility users (UEs) in a factory environment. The <inline-formula><tex-math>$K$</tex-math></inline-formula>-repetition scheme is a promising approach for improving URLLC reliability, but most studies so far have been carried out assuming fast fading. A UE with low mobility typically experiences a time-correlated slow fading channel. We therefore consider the effects of time correlation in analyzing the performance of a <inline-formula><tex-math>$K$</tex-math></inline-formula>-repetition scheme. On the other hand, multi-connectivity (MC) schemes can overcome such effects and improve reliability through space diversity. We then apply differential modulation to both schemes to support URLLC services, and compare their performance. The simulation results show that such slow fading channels severely decreases the time diversity of a <inline-formula><tex-math>$K$</tex-math></inline-formula>-repetition scheme, and the MC scheme outperforms the <inline-formula><tex-math>$K$</tex-math></inline-formula>-repetition scheme in terms of reliability and transmission latency.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1280-1286"},"PeriodicalIF":5.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10999067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178923","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":"Transforming Highway Safety With Autonomous Drones and AI: A Framework for Incident Detection and Emergency Response","authors":"Muhammad Farhan;Hassan Eesaar;Afaq Ahmed;Kil To Chong;Hilal Tayara","doi":"10.1109/OJVT.2025.3549387","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3549387","url":null,"abstract":"Highway accidents pose serious challenges and safety risks, often resulting in severe injuries and fatalities due to delayed detection and response. Traditional accident management methods heavily rely on manual reporting, which can be sometime inefficient and error-prone resulting in valuable life loss. This paper proposes a novel framework that integrates autonomous aerial systems (drones) with advanced deep learning models to enhance real-time accident detection and response capabilities. The system not only dispatch the drones but also provide live accident footage, accident identification and aids in coordinating emergency response. In this study we implemented our system in Gazebo simulation environment, where an autonomous drone navigates to specified location based on the navigation commands generated by Large Language Model (LLM) by processing the emergency call/transcript. Additionally, we created a dedicated accident dataset to train YOLOv11 m model for precise accident detection. At accident location the drone provides live video feeds and our YOLO model detects the incident, these high-resolution captured images after detection are analyzed by Moondream2, a Vision language model (VLM), for generating detailed textual descriptions of the scene, which are further refined by GPT 4-Turbo, large language model (LLM) for producing concise incident reports and actionable suggestions. This end-to-end system combines autonomous navigation, incident detection and incident response, thus showcasing its potential by providing scalable and efficient solutions for incident response management. The initial implementation demonstrates promising results and accuracy, validated through Gazebo simulation. Future work will focus on implementing this framework to the hardware implementation for real-world deployment in highway incident system.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"829-845"},"PeriodicalIF":5.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777883","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":"Cyber Threat Susceptibility Assessment for Heavy-Duty Vehicles Based on ISO/SAE 21434","authors":"Narges Rahimi;Beth-Anne Schuelke-Leech;Mitra Mirhassani","doi":"10.1109/OJVT.2025.3550307","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3550307","url":null,"abstract":"TARA, which stands for Threat Analysis and Risk Assessment, serves as the foundational stage of cybersecurity implementation, particularly in the context of vehicular systems. While various considerations and risk assessment frameworks have been discussed in recent years, there is a notable lack of TARA models specifically designed for heavy-duty (HD) vehicles. The security considerations and vulnerabilities in HD vehicles differ significantly from those in light-duty (LD) vehicles, leading to different security impacts and varying attack feasibility. This makes existing models inadequate for accurately assessing risks in the context of HD vehicles. This study introduces a novel risk assessment model tailored for HD vehicles, addressing gaps in existing TARA frameworks such as EVITA, HEAVENS, and ISO/SAE 21434. The key contribution of this work lies in the customization of impact and feasibility metrics within the ISO/SAE framework to better account for the unique security challenges posed by HD vehicles. Unlike prior models, this approach adapts the impact criteria to reflect the diverse range of security concerns specific to HD vehicles, which have been inadequately addressed in existing frameworks. Additionally, through a comprehensive analysis of threat vectors and vehicle interfaces, the model refines feasibility criteria, ensuring a more accurate and context-aware assessment of security risks. By adopting these enhancements, the proposed model offers more precise risk assessments that align with HD vehicle considerations, helping to prioritize threats and make optimal decisions regarding risk treatment.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"969-990"},"PeriodicalIF":5.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10921673","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830529","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}