IEEE Open Journal of Intelligent Transportation Systems最新文献

{"title":"Anytime Optimal Trajectory Repairing for Autonomous Vehicles","authors":"Kailin Tong;Martin Steinberger;Martin Horn;Selim Solmaz;Daniel Watzenig","doi":"10.1109/OJITS.2025.3563823","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3563823","url":null,"abstract":"Adapting to dynamically changing situations remains a pivotal challenge for automated driving systems, which demand robust and efficient solutions. Occasional perception errors inherent in artificial intelligence further complicate the task. Whereas traditional motion planning algorithms address this challenge by replanning the entire trajectory, a significantly more efficient strategy is to repair only the flawed segments. Our paper introduces a groundbreaking approach by formulating an optimal trajectory repairing problem and proposing an innovative and efficient framework for critical timing detection and trajectory repairing. This trajectory repairing specifically employs Bernstein basis polynomials in both 2D distance-time and 3D spatiotemporal spaces. A distinctive feature of our method is the use of an anytime grid search to determine a sub-optimal time-to-repair, which contrasts with previous methods that relied on manually tuned or fixed repair times, limiting both flexibility and robustness. A statistical analysis of 100 scenarios demonstrates that our trajectory-repairing framework outperforms the path-speed decoupled repairing framework in terms of scenario success rate. Furthermore, we introduce a novel algorithm for driving corridor generation that more accurately approximates the collision-free space than state-of-the-art work. The proposed approach has broad potential for application in embedded systems across various autonomous platforms.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"537-553"},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979545","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908427","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":"Harnessing Machine Learning for Intelligent Networking in 5G Technology and Beyond: Advancements, Applications and Challenges","authors":"Kristi Dulaj;Abdulraqeb Alhammadi;Ibraheem Shayea;Ayman A. El-Saleh;Mohammad Alnakhli","doi":"10.1109/OJITS.2025.3564361","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3564361","url":null,"abstract":"A revolutionary age in telecommunications is being ushered in by the confluence of machine learning (ML) with fifth-generation (5G) wireless communication technologies and beyond. This research investigates ML approaches in 5G networks for adaptive spectrum usage, quality of service (QoS) management, predictive maintenance, and network optimization. By leveraging ML algorithms, 5G networks can forecast user behavior, allocate resources optimally, and dynamically adjust to changing conditions, enhancing performance and dependability. Additionally, ML-driven methods improve cybersecurity in 5G settings. Furthermore, the integration of ML in 5G networks is pivotal for advancing intelligent transportation systems, enabling dynamic route optimization, adaptive traffic management, and enhanced vehicular communication. Intelligent networks will transform wireless communication by replacing traditional processing with end-to-end solutions, utilizing cognitive radio systems and deep reinforcement learning for optimized spectrum sharing and efficiency. Despite significant potential, challenges such as interoperability, security, scalability, and energy efficiency must be addressed. This paper discusses these challenges and highlights future trends beyond 5G, emphasizing ML's critical role in shaping the future of wireless communication systems.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"605-633"},"PeriodicalIF":4.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10977045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108343","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":"Analyzing and Mitigating Bias for Vulnerable Road Users by Addressing Class Imbalance in Datasets","authors":"Dewant Katare;David Solans Noguero;Souneil Park;Nicolas Kourtellis;Marijn Janssen;Aaron Yi Ding","doi":"10.1109/OJITS.2025.3564558","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3564558","url":null,"abstract":"Vulnerable road users (VRUs), including pedestrians, cyclists, and motorcyclists, account for approximately 50% of road traffic fatalities globally, as per the World Health Organization. In these scenarios, the accuracy and fairness of perception applications used in autonomous driving become critical to reduce such risks. For machine learning models, performing object classification and detection tasks, the focus has been on improving accuracy and enhancing model performance metrics; however, issues such as biases inherited in models, statistical imbalances and disparities within the datasets are often overlooked. Our research addresses these issues by exploring class imbalances among vulnerable road users by focusing on class distribution analysis, evaluating model performance, and bias impact assessment. Using popular CNN models and Vision Transformers (ViTs) with the nuScenes dataset, our performance evaluation shows detection disparities for underrepresented classes. Compared to related work, we focus on metric-specific and cost-sensitive learning for model optimization and bias mitigation, which includes data augmentation and resampling. Using the proposed mitigation approaches, we see improvement in IoU(%) and NDS(%) metrics from 71.3 to 75.6 and 80.6 to 83.7 for the CNN model. Similarly, for ViT, we observe improvement in IoU and NDS metrics from 74.9 to 79.2 and 83.8 to 87.1. This research contributes to developing reliable models while addressing inclusiveness for minority classes in datasets. Code can be accessed at: BiasDet.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"590-604"},"PeriodicalIF":4.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10977047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072774","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":"Domain Adaptation for Vehicle Detection Under Adverse Weather","authors":"Huei-Yung Lin;Yi-Chao Huang;Jing-Xian Lai;Ting-Ting You","doi":"10.1109/OJITS.2025.3563373","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3563373","url":null,"abstract":"The images captured under varying illumination or adverse weather conditions exhibit distinct distributions in the high-dimensional feature space, hindering the performance of object detection networks. To address this issue, we propose a domain adaptation method based on adversarial learning. This approach ensures that extracted features have a similar distribution, even when input images originate from different data acquisition domains. Due to the lack of driving images recorded under a variety of weather conditions in existing datasets, we incorporate a semi-supervised learning framework to enhance detection performance by training with unlabeled images. Experimental results on public and our latest datasets demonstrate that the proposed adversarial learning technique surpasses recent traffic scene object detection networks across various driving scenarios. Code and datasets are available at <uri>https://github.com/daniel851218/all-weather-vehicle-detector</uri>.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"568-578"},"PeriodicalIF":4.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908398","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 Road Friction-Aware Anti-Lock Braking System Based on Model-Structured Neural Networks","authors":"Mattia Piccinini;Matteo Zumerle;Johannes Betz;Gastone Pietro Rosati Papini","doi":"10.1109/OJITS.2025.3563347","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3563347","url":null,"abstract":"The anti-lock braking system (ABS) is a vital safety feature in modern vehicles, preventing wheel lock during emergency braking. However, the performance of conventional ABS is often limited by the lack of real-time road friction information. This paper introduces a novel road friction-aware ABS, leveraging model-structured neural networks (MS-NNs) to learn the vehicle longitudinal dynamics in different road conditions. Our framework uses a robust criterion to dynamically select from a set of pre-trained MS-NNs based on the available sensor data, enabling real-time road friction estimation and autonomous adaptation of the ABS parameters. Simulation experiments demonstrate that the proposed MS-NN-based ABS significantly improves safety and performance across varying road conditions: the braking distances are reduced by 3.0%-40.4% compared to a conventional ABS, tuned for a specific road condition. Furthermore, the MS-NN’s architecture shows better accuracy, generalization and sample-efficiency compared to other neural networks in the literature, and is suitable for real-time deployment on automotive-grade hardware. Our implementation is open source and available in a public repository.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"522-536"},"PeriodicalIF":4.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925089","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":"Adopting Graph Neural Networks to Understand and Reason About Dynamic Driving Scenarios","authors":"Peng Su;Conglei Xiang;Dejiu Chen","doi":"10.1109/OJITS.2025.3563428","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3563428","url":null,"abstract":"With advances in Deep Neural Networks (DNN), Automated Driving Systems (ADS) enable the vehicle to perceive their surroundings in dynamic driving scenarios and perform behaviors by collecting operational data from sensors such as LiDAR and cameras. Current DNN typically detect objects by analyzing and classifying unstructured data (e.g., image data), providing critical information for ADS planning and decision-making. However, advanced ADS, particularly those required to perform the Dynamic Driving Task (DDT) autonomously, are expected to understand driving scenarios across various Operational Design Domains (ODD). This capability requires the support for a continuous comprehension of driving scenarios according to operational data collected by sensors. This paper presents a framework that adopts Graph Neural Networks (GNN) to describe and reason about dynamic driving scenarios via analyzing graph-based data based on collected sensor inputs. We first construct the graph-based data using a meta-path, which defines various interactions among different traffic participants. Next, we propose a design of GNN to support both the classification of the node types of objects and predicting relationships between objects. As results, the performance of the proposed method shows significant improvements compared to the baseline method. Specifically, the accuracy of node classification increases from 0.77 to 0.85, while that of relationships prediction rises from 0.74 to 0.82. To further utilize graph-based data constructed from dynamic driving scenarios, the proposed framework supports reasoning about operational risks by analyzing the observed nodes and relationships in the graph-based data. As a result, the model achieves a MRR of 0.78 in operational risks reasoning. To evaluate the practicality of the proposed framework in real-world systems, we also conduct a real-time performance evaluation by measuring the average process time and the Worst Case Execution Time (WCET). Compared to the baseline models, the results demonstrate the proposed framework presents acceptable real-time performance in analyzing graph-based data.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"579-589"},"PeriodicalIF":4.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949180","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":"OppIN: Optimal Path Intervention for Emergency Response Leveraging IoT and Big Data Technologies","authors":"Yassine Gacha;Takoua Abdellatif","doi":"10.1109/OJITS.2025.3563310","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3563310","url":null,"abstract":"In this paper, we introduce the Optimal Path Intervention System (OppIN), a solution designed to support multiple emergency services, including fire response, civil protection, and emergency medical assistance, to reach crisis locations as quickly as possible by harnessing Big Data technologies and IoT infrastructure. OppIN computes quasi-real-time optimal intervention paths using a multi-criteria approach, incorporating both static factors (such as road network geometry, road conditions, and service locations) and dynamic data (including crisis locations captured by IoT sensors and real-time traffic conditions monitored through surveillance cameras). Using the IoT infrastructure and local data for quasi-real-time updates, OppIN adapts effectively to dynamic changes in context, ensuring the use of up-to-date information alongside Big Data technologies and AI for real-time processing. Compared to existing solutions such as Google Maps, our system uses a broader set of data sources and criteria, such as weather conditions, distance, traffic dynamics, and road status, to provide a more comprehensive and tailored analysis for specialized service navigation. Additionally, OppIN offers superior scalability and performance, using a Big Data-driven system design to handle high data volumes and real-time processing demands effectively. Furthermore, our system uses AI programs to estimate different criteria and to aggregate these criteria for quasi-real-time paths calculation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"484-502"},"PeriodicalIF":4.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973160","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900565","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":"Multi-Hop Upstream Anticipatory Traffic Signal Control With Deep Reinforcement Learning","authors":"Xiaocan Li;Xiaoyu Wang;Ilia Smirnov;Scott Sanner;Baher Abdulhai","doi":"10.1109/OJITS.2025.3562757","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3562757","url":null,"abstract":"Coordination in traffic signal control is crucial for managing congestion in urban networks. Existing pressure-based control methods focus only on immediate upstream links, leading to suboptimal green time allocation and increased network delays. However, effective signal control inherently requires coordination across a broader spatial scope, as the effect of upstream traffic should influence signal control decisions at downstream intersections, impacting a large area in the traffic network. Although agent communication using neural network-based feature extraction can implicitly enhance spatial awareness, it significantly increases the learning complexity, adding an additional layer of difficulty to the challenging task of control in deep reinforcement learning. To address the issue of learning complexity and myopic traffic pressure definition, our work introduces a novel concept based on Markov chain theory, namely multi-hop upstream pressure, which generalizes the conventional pressure to account for traffic conditions beyond the immediate upstream links. This farsighted and compact metric informs the deep reinforcement learning agent to preemptively clear the multi-hop upstream queues, guiding the agent to optimize signal timings with a broader spatial awareness. Simulations on synthetic and realistic (Toronto) scenarios demonstrate controllers utilizing multi-hop upstream pressure significantly reduce overall network delay by prioritizing traffic movements based on a broader understanding of upstream congestion.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"554-567"},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970747","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908362","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":"Digital Twin Technologies for Vehicular Prototyping: A Survey","authors":"Md Rafiul Kabir;Bhagawat Baanav Yedla Ravi;Sandip Ray","doi":"10.1109/OJITS.2025.3562504","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3562504","url":null,"abstract":"Digital Twin (DT) technology is widely regarded as one of the most promising tools for industry development, demonstrating substantial application across numerous cyber-physical systems. Gradually, this technology has been introduced into modern vehicular systems focusing on its application in intelligent driving, connected vehicles, automotive engineering, aircraft health, and many more. By creating dynamic, virtual replicas of physical vehicles and their associated components, DT enables unprecedented levels of analysis, simulation, and real-time monitoring, thereby enhancing performance, safety, and sustainability. This paper offers a comprehensive review, extending beyond digital twins to include various prototyping approaches for target-specific applications focusing on the smart vehicular systems across automotive, aviation, and maritime domains driving the evolution of next-generation vehicular infrastructure.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"503-521"},"PeriodicalIF":4.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969983","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900566","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":"Joint Optimization of Transportation-Energy Systems Through Electric Vehicle Charging Pricing in the Morning Commute","authors":"Kevin Freymiller;Junjie Qin;Sean Qian","doi":"10.1109/OJITS.2025.3557038","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3557038","url":null,"abstract":"We investigate how electric vehicles (EV) market share and EV charging pricing would impact the joint transportation and grid system during the morning commute. Using a simplified network consisting of a single corridor, we analytically derive time-varying flow patterns for both EV and internal combustion engine vehicle (ICV) groups, as a result of travelers’ departure time choices upon travel time, schedule delay and EV charging fee at an arbitrary morning time. For cities with a small or moderate portion of electricity generated from solar, one primary cost for the grid system during the morning commute is power generation ramping in addition to energy cost. By imposing a single charging price change during the morning commute period, we solve for the optimal charging price change time and magnitude to minimize joint system cost. We show that a price increase during morning commute is always preferred. There is a trade-off between transportation and grid costs with respect to when/how grid and transportation infrastructure are utilized by vehicles, particularly electric vehicles. Increasing EV peak charge would increase the grid ramping cost, as more EVs would depart home earlier. However, the same EV peak charge would reduce the transportation cost when the charge is mild or EV penetration is relatively low. When the energy generation ramping is considerable, there always exists an optimal EV peak charge balancing transportation cost and grid cost. We mathematically show the benefits of replacing ICVs with EVs in reducing transportation cost on top of emission/energy reductions, which can be achieved by imposing optimal EV charging prices alone. In addition, we would impose a higher peak charging price during winter for high latitude areas, or areas on the western end of a time zone, as such a price would reduce transportation cost without burdening the grid.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"465-483"},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947487","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845547","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}