IEEE Open Journal of Vehicular Technology最新文献

{"title":"UAP: A System Authentication Protocol for UAV Relay Communication by UAV-Assisted","authors":"Chunpeng Liu;Tao Huang;Maode Ma","doi":"10.1109/OJVT.2025.3567079","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3567079","url":null,"abstract":"In recent years, advancements in Unmanned Aerial Vehicle (UAV) technology have led to the emergence of the Internet of Drones (IoD), further enhanced by the capabilities of 5th generation mobile network (5G). UAVs are widely used in various applications, such as disaster assessment, material delivery, and environmental monitoring, due to their flexibility. However, operating in insecure open environments presents significant risks, making it crucial to complete tasks without exposing sensitive information to attackers or unauthorized users. Additionally, the communication range between UAVs and ground stations is often limited, and if a UAV flies beyond this range, the likelihood of mission failure increases substantially. To address these challenges, we propose a UAV-assisted Authentication Protocol (UAP) based on Physical Unclonable Functions (PUFs), leveraging 5G to enable secure relay communication between UAVs and ground stations. We provide formal proof of the protocol's logical correctness and perform cryptanalysis, demonstrating that it effectively resists various security threats, including masquerade and replay attacks. Furthermore, we evaluate the resilience of UAP against multiple security vulnerabilities using the Scyther tool. Finally, we compare our protocol with existing authentication methods in terms of application scenarios, security features, and both computational and communication overhead. We have performed simulations on a Raspberry Pi. The experiments show that UAP has a computational overhead of 0.0826 ms and a communication overhead of 0.0408 ms. The application scenarios and security features of UAP are also considered, making it a solution for UAV applications.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1539-1550"},"PeriodicalIF":5.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10988496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367043","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":"Deep Learning-Based UWB-IMU Data Fusion for Indoor Positioning in Industrial Scenario","authors":"Karthik Muthineni;Alexander Artemenko;Josep Vidal;Montse Nájar;Marisa Catalan;Josep Paradells","doi":"10.1109/OJVT.2025.3566888","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3566888","url":null,"abstract":"Accurate and precise wireless infrastructure-based positioning systems become crucial as industries move towards flexible, portable, and autonomous transportation systems such as Automated Guided Vehicles (AGVs). Multipath-dominant dynamic environments like industries present significant challenges for wireless signal propagation and affect wireless positioning accuracy due to the interplay of reflected signals from obstacles. The achievable indoor positioning accuracy of the target AGV can be enhanced by fusing the measurements from the wireless infrastructure with the target's onboard sensor data. Nevertheless, the lack of correspondence between the wireless infrastructure and the target's onboard sensors causes the measurements from these two systems to arrive at irregular time steps. Using asynchronous measurements in the data fusion process can degrade the overall positioning accuracy of the target AGV. This paper proposes a novel deep learning-based data fusion approach to deal with asynchronous measurements from the wireless infrastructure Ultra-Wideband (UWB) and the target's onboard Inertial Measurement Unit (IMU) sensor to achieve enhanced positioning accuracy of the target AGV. In particular, a two-stage cascaded Deep Neural Network (DNN) is proposed to deal with the asynchronized measurements from UWB and IMU sensors. The first stage of the DNN is used to obtain the initial position estimate of the AGV by processing the measurements from UWB. Subsequently, the second stage of the DNN fuses the initial position estimate of the AGV with the IMU sensor data to obtain the final enhanced position estimate. The proposed approach is validated with real-world experiments in an indoor industrial scenario using UWB technology in channel 2 (<inline-formula><tex-math>$3.7text{--}4.2,text{GHz}$</tex-math></inline-formula>) and an IMU sensor placed on an AGV. Moreover, the achievable positioning accuracy and the computational runtime to provide the position estimates with the proposed approach are analyzed. The experimental results show that the proposed approach achieves a mean absolute error of less than <inline-formula><tex-math>$text{10},text{cm}$</tex-math></inline-formula>, outperforming the considered baseline methods, Extended Kalman Filter (EKF) and Long Short-Term Memory (LSTM).","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1209-1221"},"PeriodicalIF":5.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10985825","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099970","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":"Adaptive RNN Hyperparameter Tuning for Optimized IDS Across Platforms","authors":"Kamronbek Yusupov;Md Rezanur Islam;Ibrokhim Muminov;Mahdi Sahlabadi;Kangbin Yim","doi":"10.1109/OJVT.2025.3547761","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3547761","url":null,"abstract":"Modern vehicles are increasingly vulnerable to cyber-attacks due to the lack of encryption and authentication in the Controller Area Network, which coordinates communication between Electronic Control Units. This study investigates the use of Recurrent Neural Networks to improve the accuracy and efficiency of Intrusion Detection Systems in vehicular networks. Focusing on sequential CAN data, we compare the performance of different RNN architectures, including SimpleRNN, LSTM, and GRU, in detecting common attack types like Denial-of-Service, Fuzzing, Replay, and Malfunction. Sixty-three RNN models were tested with various hyperparameters, including optimizers and learning rates. Our findings indicate that GRU models achieve superior detection performance, particularly in resource-constrained environments, offering near 99% accuracy in identifying cyber threats. The study also explores the implications of six different hardware choices, revealing that devices like Jetson and Raspberry Pi, when paired with optimal hyperparameters, can deliver efficient real-time IDS performance at a lower cost. These results contribute to the ongoing effort to secure vehicular communication systems and highlight the importance of balancing accuracy, resource usage, and system cost in IDS deployment.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"991-1004"},"PeriodicalIF":5.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10909606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845520","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":"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":"A Study on the Impact of Rain on Object Detection for Automotive Applications","authors":"Diarmaid Geever;Tim Brophy;Dara Molloy;Enda Ward;Brian Deegan;Martin Glavin;Edward Jones","doi":"10.1109/OJVT.2025.3566251","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3566251","url":null,"abstract":"Visible spectrum cameras have emerged as a key technology in Advanced Driving Assistance Systems (ADAS) and automated vehicles. An important question to be answered is how these sensors perform in challenging adverse weather conditions, such as rain. Although progress has been made in determining the impact of rain on computer vision performance, previous studies have generally focused on end-to-end object detection system performance and have not addressed the specific impact of rain in detail. Moreover, the lack of image datasets with detailed labeling acquired under rain conditions means that the impact of rain remains a relatively under-researched question. The purpose of this study is to examine the impact of rain in the propagation path on perception tasks, where other factors affecting performance are removed or controlled as far as possible. This study presents the results of controlled experimental testing designed to measure the impact of rain on automated vehicle perception performance. Object detection is performed on the captured data to determine the impact of rain on performance. Four object detection algorithms, a segmentation algorithm, and an optical character recognition algorithm are used as representative examples of typical algorithms used in ADAS. It is shown that the impact of rain varies between models, and at larger distances, rain has a greater impact. In the case of the OCR algorithm, rain is shown to have a larger impact at certain distances. The findings of this study are useful for ADAS design, as they provide more detailed insight into the impact of rain on ADAS and provide guidance on potential breaking points for algorithms typically used in this type of system.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1287-1302"},"PeriodicalIF":5.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10981986","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178484","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":"Optimized Electric Vehicles Wireless Charging: Applicative Models for Supporting Decision Makers","authors":"Cristian Giovanni Colombo;Ryosuke Ota;Michela Longo","doi":"10.1109/OJVT.2025.3546805","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3546805","url":null,"abstract":"Wireless Power Transfer is one of the most promising technologies in the private transport sector. With the large-scale deployment of electric vehicles for decarbonization policies, the number of charging stations to be deployed will increase and may not be sufficient for the service, causing network instability. The use of wireless charging in urban and highway contexts could facilitate the service by reducing the network peaks associated with DC fast charging stations. This paper guides a decision-maker interested in implementing wireless charging models in urban and highway contexts. The work proposes an optimization algorithm for each context and identifies outputs for 3 different car models with different heights above the ground (0.10 m, 0.20 m and 0.30 m). This will allow to identify 3 optimized scenarios for wireless charging for each model. A sensitivity analysis will show the percentage improvement in performance as the number of transmitters is increased. In the urban model, it will be possible to increase the energy charged per stop by up to 4.2% by varying between the minimum and maximum number of transmitters. In the highway model, it will be possible to increase the recharged energy in a 1 km section by up to 26.5% by varying the number of transmitters between the 3 optimal configurations obtained. These results can provide a quantitative guide for decision-makers wishing to implement a wireless charging system in the two contexts analyzed.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"897-911"},"PeriodicalIF":5.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10907929","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808972","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":"105 GHz Multipath Propagation Measurement and Comparison With 60 GHz in Office Desk Environment for Ultra-High Speed Sub-THz WPAN Systems","authors":"Masaki Maeda;Yusuke Koda;Norichika Ohmi;Hiroshi Harada","doi":"10.1109/OJVT.2025.3545608","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3545608","url":null,"abstract":"This study presents wideband propagation measurements of 105 GHz multipath characteristics, encompassing a full 360<inline-formula><tex-math>$^circ $</tex-math></inline-formula> in a real office desktop environment. High-speed wireless personal area network (WPAN) systems operating in such environments represent a promising use case for sub-terahertz (THz) communication systems owing to the short-range nature of such networks. Additionally, selecting a frequency band close to the millimeter-wave spectrum increases the feasibility of sub-THz WPAN systems compared to the widely recognized 300 GHz band, mainly because of the availability of low-cost hardware. However, the multipath propagation characteristics at the 105 GHz band, specifically within a 360<inline-formula><tex-math>$^circ $</tex-math></inline-formula> range in a real office desktop environment has not been thoroughly investigated. To address this gap, we evaluate the 105 GHz multipath propagation characteristics, considering both delay and angular profiles and compare them with our concurrent 60 GHz measurements in the same environment. The results indicate a notable distinction between the two bands: a physical partition maintaining personal space causes the multipath power at 105 GHz to deviate by 10 dB relative to the 60 GHz band. Furthermore, our system-oriented analysis highlights the similarity of propagation characteristics in both bands, as nearly all multipath waves at 105 GHz exhibit power levels comparable to those observed at 60 GHz. In both frequency bands, the delay spread extends up to 5 ns, while the angular spread reaches up to 40<inline-formula><tex-math>$^circ $</tex-math></inline-formula>. These findings suggest that the current 60 GHz WPAN system standards could be effectively extended to the 105 GHz band for sub-THz WPAN applications.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"853-867"},"PeriodicalIF":5.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777775","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}