IEEE Open Journal of Intelligent Transportation Systems最新文献

An Equilibrium-Seeking Search Algorithm for Integrating Large-Scale Activity-Based and Traffic Assignment Models 基于活动的大规模交通分配模型的均衡搜索算法

IF 5.3

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-08-20 DOI: 10.1109/OJITS.2025.3600918

Serio Agriesti;Claudio Roncoli;Bat-Hen Nahmias-Biran

{"title":"An Equilibrium-Seeking Search Algorithm for Integrating Large-Scale Activity-Based and Traffic Assignment Models","authors":"Serio Agriesti;Claudio Roncoli;Bat-Hen Nahmias-Biran","doi":"10.1109/OJITS.2025.3600918","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3600918","url":null,"abstract":"This paper proposes an iterative methodology to integrate large-scale behavioral activitybased models with mesoscopic traffic assignment models. The proposed approach fully decouples the two parts, allowing the ex-post integration of multiple models as long as certain assumptions are satisfied. A measure of error is defined to characterize a search space easily explorable within its boundaries. Within it, a joint distribution of the number of trips and travel times is identified as the equilibrium distribution, i.e., the distribution for which trip numbers and travel times are bound in the neighborhood of the equilibrium between supply and demand. The approach is tested on a medium-sized city of 400,000 inhabitants and the results suggest that the proposed iterative approach does perform well, reaching equilibrium between demand and supply in a limited number of iterations thanks to its perturbation techniques. Overall, 15 iterations are needed to reach values of the measure of error lower than 5%. The equilibrium identified this way is then validated against baseline distributions to demonstrate the goodness of the results.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1156-1170"},"PeriodicalIF":5.3,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11131172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934536","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}

引用次数: 0

Automated Driving System Challenges in Rural Appalachia 阿巴拉契亚农村地区自动驾驶系统面临的挑战

IF 5.3

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-08-20 DOI: 10.1109/OJITS.2025.3600966

Michael A. Tonkovich;Travis W. Moleski;Sam Fayez;Andrew Wallace;Preeti Choudhary;Jay P. Wilhelm

{"title":"Automated Driving System Challenges in Rural Appalachia","authors":"Michael A. Tonkovich;Travis W. Moleski;Sam Fayez;Andrew Wallace;Preeti Choudhary;Jay P. Wilhelm","doi":"10.1109/OJITS.2025.3600966","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3600966","url":null,"abstract":"The development of autonomous driving technology has predominantly focused on urban and suburban areas. Deployment of automated driving systems in regions such as rural Appalachia present unique challenges such as narrow and winding roads and degradation of localization. Scenarios in rural Appalachia that required manual intervention by a driver during autonomous driving experiments were investigated across three unique routes. The research identified the technological and environmental limitations that contributed to these interventions and how they may differ from urban settings. The goal was to provide insights into the factors that hinder autonomous vehicle performance in rural areas and guide the development of more adaptable and robust systems capable of operating reliably in diverse environments, extending the benefits of autonomous driving to rural populations and ensuring equitable access to advancements in transportation. Driving experiments resulted in 1,884 total interventions and revealed trends in the reasons and locations for intervention across the three routes. In rural areas the leading causes of takeover were localization issues, accounting for 30.4% of total events, environmental traffic uncertainties, responsible for 20.3%, and object detection challenges, comprising 15.2%. Whereas urban settings saw roundabouts, environmental traffic uncertainties, and stoplight detection errors as the most common reasons with respective percentages of 19.5%, 17.7%, and 15.4%, revealing key differences between environments.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1121-1132"},"PeriodicalIF":5.3,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11131208","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934412","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}

引用次数: 0

Vehicle Target Detection Model Based on CBAM-BiFPN and Improved CenterNet Coding 基于CBAM-BiFPN和改进中心网编码的车辆目标检测模型

IF 5.3

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-08-20 DOI: 10.1109/OJITS.2025.3600667

Xue Xing;Fahui Luo;Bin Wang;Yufei Huang;Lei Tang

{"title":"Vehicle Target Detection Model Based on CBAM-BiFPN and Improved CenterNet Coding","authors":"Xue Xing;Fahui Luo;Bin Wang;Yufei Huang;Lei Tang","doi":"10.1109/OJITS.2025.3600667","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3600667","url":null,"abstract":"The study introduces a new method to enhance vehicle type recognition rates in Internet of Vehicles environment. The approach integrates a vehicle target detection model that utilizes bidirectional feature fusion of a hybrid attention mechanism and an enhanced CenterNet encoding technique, with ResNet18 as the base network. By decoupling detection and classification processes, the model focuses on vehicle characteristics and unique model differences, boosting accuracy. Additionally, Scale feature information is incorporated to improve CenterNet vehicle target detection by learning width, height, and shape details. To address low detection rates of specific vehicle models like buses and vans, a bidirectional feature fusion mechanism is employed, combining a hybrid attention mechanism (CBAMBiFPN) to enhance feature utilization and detection accuracy. Experimental results on UA-DETRAC and BDD datasets demonstrated an average accuracy increase, validating the model’s effectiveness. Compared to the original model, the new model showed improvements in mean average precision, F1-Score, and detection speed. Specifically, the UA-DETRAC data set saw a 1.6 percentage point increase in mean average precision and a 1.8 percentage point increase in F1-Score, with a detection speed of 68 frames/s. On the BDD100K data set, the model improved mean average precision by 1.1 percentage points. The study showcases enhanced accuracy without compromising real-time performance.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1143-1155"},"PeriodicalIF":5.3,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11131167","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990064","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}

引用次数: 0

Monocular Depth Estimation by Non-Local Decoder-Squeeze-and-Excitation Network With Adaptive Depth List 基于自适应深度表的非局部译码-压缩-激励网络单目深度估计

IF 5.3

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-07-25 DOI: 10.1109/OJITS.2025.3592628

Tsung-Han Tsai;Wei-Chung Wan

{"title":"Monocular Depth Estimation by Non-Local Decoder-Squeeze-and-Excitation Network With Adaptive Depth List","authors":"Tsung-Han Tsai;Wei-Chung Wan","doi":"10.1109/OJITS.2025.3592628","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3592628","url":null,"abstract":"Monocular depth estimation is an important topic in computer vision. Recently the CNNs (Convolutional Neural Networks) based model shows a reasonable result from an end-to-end encoder-decoder architecture. For the encoder part, most of the research is based on a robust feature extractor to get good features. With a strong encoder, it was found that even simple up-sampling processes can achieve good accuracy. However, the decoder part is more critical in a high-quality depth estimation task. Even now, there is no intuitive way to calibrate the feature map for the upsampling process. In this paper, we present a novel monocular depth estimation design. We propose an innovative CNN-based network module that considers the whole up-sampling process globally. This design is based on the concept of SE-Net, and properly recalibrated the feature maps with a global perspective attention mechanism. We further combine it with Non-local network attention mechanisms to design the Non-Local Decoder-Squeeze-and-Excitation (NL-DSE) module for the whole up-sampling process. Furthermore, we also propose an output limiting range method called Adaptive Depth List (ADL) to enhance the precision of the near-distance estimation. Combining these proposed techniques, our results are evaluated on the NYU Depth V2 dataset and outperform the state-of-the-art CNN-based approaches in accuracy.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1069-1083"},"PeriodicalIF":5.3,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11096588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887810","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}

引用次数: 0

Introducing Intelligent Data Sharing to Vehicular Cooperative Federated Learning 将智能数据共享引入车辆协同联邦学习

IF 5.3

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-07-16 DOI: 10.1109/OJITS.2025.3589612

Levente Alekszejenkó;Péter Antal;Tadeusz Dobrowiecki

{"title":"Introducing Intelligent Data Sharing to Vehicular Cooperative Federated Learning","authors":"Levente Alekszejenkó;Péter Antal;Tadeusz Dobrowiecki","doi":"10.1109/OJITS.2025.3589612","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3589612","url":null,"abstract":"This paper proposes a simple yet unexplored measurement and federated learning system architecture for connected vehicles. The novelty of the introduced system is to combine the real-time data-sharing of crowdsensing with federated learning of global traffic models, providing up-to-date information for decision-making, facilitating faster learning, improving communicational channel usage, and possibly enhancing data privacy. This multi-level cooperative federated learning system generally supports operational, tactical, and strategic planning; therefore, we demonstrate its merits with a case study of parking monitoring in a simulated town as well as average speed prediction in a simulated part of Hannover, Germany. However, real-time data-sharing is essential for decision-making; it might also contain privacy-sensitive information regarding the trajectory of the vehicles. To mitigate the risk of privacy leakage, we experimented with different data selection methods for data exchange, introducing an optimization method inspired by Zeuthen’s negotiation strategy. We also checked the privacy impact of real-time data-sharing on federated learning. Our results indicate only negligible differences in privacy leakage between the proposed data selection methods. On the other hand, real-time data-sharing improves the reaction time of the federated learning system. The Zeuthen-inspired optimization method can efficiently supply valuable information for the communication partners. Moreover, it can enhance privacy protection in federated learning in some cases.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1009-1026"},"PeriodicalIF":5.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082276","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758451","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}

引用次数: 0

Machine Learning Advancements in Urban Traffic Simulation: A Comprehensive Survey 城市交通模拟中的机器学习进展：综合调查

IF 5.3

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-07-14 DOI: 10.1109/OJITS.2025.3589208

Harshit Maheshwari;Li Yang;Richard W. Pazzi

{"title":"Machine Learning Advancements in Urban Traffic Simulation: A Comprehensive Survey","authors":"Harshit Maheshwari;Li Yang;Richard W. Pazzi","doi":"10.1109/OJITS.2025.3589208","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3589208","url":null,"abstract":"Urban traffic simulation is useful in many ways to understand, manage, and predict the growing complexities of traffic dynamics within a city. Traditional simulation models often struggle to capture the intricacies of urban traffic patterns, leading to unrealistic simulations, which negatively affect traffic management and urban planning. In recent years, Machine Learning solutions have emerged to enhance various aspects of urban traffic simulation, which is possible by utilizing vast amounts of data and extracting valuable insights. This survey systematically reviews the state-of-the-art Machine Learning techniques applied to urban traffic simulation. By focusing on the practical application of Machine Learning techniques in various studies, we aim to analyze the current research direction, highlight the effectiveness of existing approaches, identify their limitations, and propose potential strategies to improve the performance and applicability of these techniques in real-world scenarios. Another key contribution of this survey is a proof-of-concept case study, which utilizes a basic Reinforcement Learning algorithm to control traffic lights across multiple intersections. The results from this case study demonstrate a significant improvement in vehicle wait time compared to the static baseline method. The code developed for this case study is publicly available, providing a valuable resource for researchers interested in replicating this work or building upon it. This survey aims to bridge the gap between simulation and reality by providing a comprehensive foundational understanding of the subject, critically evaluating the existing limitations in current methodologies, and suggesting future directions to improve performance, adaptability, and usability.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1027-1052"},"PeriodicalIF":5.3,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11079941","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758429","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}

引用次数: 0

xFedCAV: Cyberattacks on Leader and Followers in Automated Vehicles With Cooperative Platoons Using Federated Agents xFedCAV：使用联邦代理的协作排自动驾驶车辆对领导者和追随者的网络攻击

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-07-04 DOI: 10.1109/OJITS.2025.3581617

Guanyu Lin;Sean Qian;Zulqarnain H. Khattak

{"title":"xFedCAV: Cyberattacks on Leader and Followers in Automated Vehicles With Cooperative Platoons Using Federated Agents","authors":"Guanyu Lin;Sean Qian;Zulqarnain H. Khattak","doi":"10.1109/OJITS.2025.3581617","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3581617","url":null,"abstract":"The increasing prevalence of connected and autonomous vehicles (CAVs) in smart cities requires robust cyberattack and anomaly detection systems to ensure safety and resilience. Cyberattacks on leader and follower in cooperative driving can result in differing impacts, however, their impacts on security and resilience of cooperative driving are largely unknown. Traditional anomaly detection methods, which aggregate data centrally, compromise driver privacy and are insufficient to address real-world challenges due to limitations of being compromised by adversarial attacks. To overcome these limitations, we propose Explainable Fine-Grained Cyberattacks and Anomaly Detection with Federated Agents for connected autonomous vehicles (xFedCAV). Our framework leverages federated learning to enhance privacy and security, using Shapley Additive exPlanations (SHAP) for interpretable detection. Unlike existing methods, xFedCAV focuses on fine-grained detection by simulating cyberattacks on individual vehicles rather than the entire fleet, allowing for more precise identification and response. Experimental results, conducted on a real-world CAV dataset, demonstrate that xFedCAV not only explains the relationship between vehicle characteristics and detection outputs, but also effectively detects cyberattacks in a decentralized manner. This research offers knowledge about the cybersecurity impacts of the leader and follower within cooperative driving and provides a significant advancement in federated learning applications for CAVs, contributing to the development of safer and more resilient smart city applications for transportation systems.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"898-914"},"PeriodicalIF":4.6,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11071968","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646590","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}

引用次数: 0

A New Perspective on AI Safety Through Control Theory Methodologies 基于控制理论方法的人工智能安全新视角

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-07-02 DOI: 10.1109/OJITS.2025.3585274

Lars Ullrich;Walter Zimmer;Ross Greer;Knut Graichen;Alois C. Knoll;Mohan M. Trivedi

{"title":"A New Perspective on AI Safety Through Control Theory Methodologies","authors":"Lars Ullrich;Walter Zimmer;Ross Greer;Knut Graichen;Alois C. Knoll;Mohan M. Trivedi","doi":"10.1109/OJITS.2025.3585274","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3585274","url":null,"abstract":"While artificial intelligence (AI) is advancing rapidly and mastering increasingly complex problems with astonishing performance, the safety assurance of such systems is a major concern. Particularly in the context of safety-critical, real-world cyber-physical systems, AI promises to achieve a new level of autonomy but is hampered by a lack of safety assurance. While data-driven control takes up recent developments in AI to improve control systems, control theory in general could be leveraged to improve AI safety. Therefore, this article outlines a new perspective on AI safety based on an interdisciplinary interpretation of the underlying data-generation process and the respective abstraction by AI systems in a system theory-inspired and system analysis-driven manner. In this context, the new perspective, also referred to as data control, aims to stimulate AI engineering to take advantage of existing safety analysis and assurance in an interdisciplinary way to drive the paradigm of data control. Following a top-down approach, a generic foundation for safety analysis and assurance is outlined at an abstract level that can be refined for specific AI systems and applications and is prepared for future innovation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"938-966"},"PeriodicalIF":4.6,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11066173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704947","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}

引用次数: 0

Fault Tolerance and Fallback Strategies in Connected and Automated Vehicles: A Review 网联与自动驾驶汽车的容错与回退策略综述

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-06-27 DOI: 10.1109/OJITS.2025.3583787

Mario Rodríguez-Arozamena;Jose Matute;Javier Araluce;Joshué Pérez Rastelli;Asier Zubizarreta

{"title":"Fault Tolerance and Fallback Strategies in Connected and Automated Vehicles: A Review","authors":"Mario Rodríguez-Arozamena;Jose Matute;Javier Araluce;Joshué Pérez Rastelli;Asier Zubizarreta","doi":"10.1109/OJITS.2025.3583787","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3583787","url":null,"abstract":"Connected and Automated Vehicles (CAVs) are considered the future of transportation, offering increased safety, efficiency, and convenience. However, their reliance on sophisticated sensors and complex algorithms poses challenges, especially in scenarios with uncertainties, constraints, or failures. Dynamic Driving Task (DDT) fallback and fault tolerance strategies serve as critical mechanisms to ensure safe operation when primary systems fail or face functional insufficiencies. This paper provides an analysis of the fault-related taxonomy established by international standards and a comprehensive review of the DDT fallback and fault tolerance strategies used in CAVs, focusing on their strategy, classification, and implementation methods. Moreover, the challenges and future research directions for the development and improvement of fault tolerance strategies are discussed. The analysis shows that the main trends are to avoid the termination of the CAV operation in case of a failure or functional insufficiency, or at least to be able to guide the vehicle to a safe state. However, there is a tendency towards the possibility of continuing the operation. This review contributes to a deeper understanding of the role of DDT fallback and fault tolerance strategies for CAVs and future trends.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"915-937"},"PeriodicalIF":4.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11054273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680901","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}

引用次数: 0

Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A Survey 利用3GPP特性和优化技术实现5G NR-V2X资源分配

IF 5.3

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-06-27 DOI: 10.1109/OJITS.2025.3584024

Chaeriah Bin Ali Wael;El Hadj Dogheche;Nasrullah Armi;Agus Subekti;Iyad Dayoub

{"title":"Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A Survey","authors":"Chaeriah Bin Ali Wael;El Hadj Dogheche;Nasrullah Armi;Agus Subekti;Iyad Dayoub","doi":"10.1109/OJITS.2025.3584024","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3584024","url":null,"abstract":"Cellular Vehicle-to-everything (C-V2X) communication is critical for Intelligent Transportation Systems (ITS), facilitating information exchange among road users and infrastructure. Since its first introduction in rel-15 by 3GPP, 5G NR-V2X features have continued to evolve, aiming to support increasingly advanced V2X services. Addressing diverse service requirements, spectrum scarcity, dynamic vehicular environments, and radio interference necessitates efficient resource allocation strategies for the 5G NR-V2X system. However, dealing with resource allocation problems involving various conflicting objectives and constraints while accomplishing the Quality of Services (QoS) requirements of the V2X system remains a challenging issue. In this direction, this survey examines state-of-the-art resource allocation strategies for 5G NR-V2X, focusing on 3GPP features associated with V2X communication and their implications, along with optimization techniques employed in designing resource allocation strategies. Specifically, we present the benefits and challenges of each 3GPP feature and optimization technique, and their application to communication and computing resource allocation problems. Finally, we discuss issues tied to 3GPP features and optimization techniques, then highlight future research opportunities for efficient 5G NR-V2X resource allocation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"967-994"},"PeriodicalIF":5.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11054065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725185","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}

引用次数: 0