IEEE Open Journal of Intelligent Transportation Systems最新文献

{"title":"Long-Short Term Memory Networks and Synthetic Data for Heavy Vehicle Rollover Prevention","authors":"Guido Perboli;Antonio Tota;Filippo Velardocchia","doi":"10.1109/OJITS.2025.3579653","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3579653","url":null,"abstract":"Heavy vehicle rollover plays a pivotal role in road safety scenarios. Numerous researchers addressed the topic, with particular focus on drivers related injuries. Considering the same and other connected implications, the necessity for techniques able to estimate and predict overturning eventualities appears evident. Different methodologies were explored, with notable achievements obtained by neural network-based algorithms. At the same time, their heavy requirements in terms of data needs to be addressed to allow practical applications in terms of time and costs. Consequently, exploring the interaction between simulation and experimental data becomes extremely important, motivating the methodology proposed by this paper. In details, an heavy vehicle model was designed in IPG Carmaker®, while experimental data on its physical alter ego were acquired. This led to the generation of a synthetic dataset and the collection of an empirical one. Both were used to define a Long Short-Term Memory architecture, with a dual purpose. First, as typical rollover indicator, estimate the vehicle roll angle. Second, compare the performance of the neural networks, aiming to obtain at least the same order of magnitude in terms of RMSE, MSE and MAE. The goal was to demonstrate that synthetic data can not only be used in combination with real data, but also as substitutes able to address time and cost constraints inevitably linked to the latter, allowing more efficient experiments for overtipping prevention.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"792-798"},"PeriodicalIF":4.6,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11036550","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536565","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 Self-Learning Framework for Resilient Vehicle Classification Through the Integration of Inductive Loops and LiDAR Sensors","authors":"Yiqiao Li;Andre Y. C. Tok;Stephen G. Ritchie","doi":"10.1109/OJITS.2025.3575808","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3575808","url":null,"abstract":"Inductive loop sensors are widely deployed across the U.S. and can provide vehicle classification data with comparable accuracy to the current axle-based sensor systems when they are enhanced with the inductive signature technology and advanced machine learning models. However, the existing truck population is expected to turnover and be replaced with newer models that may generate distinct inductive signature characteristics. Consequently, legacy inductive signature-based models may not perform optimally in classifying newer trucks operating on the highways over time. To enhance the resilience of the signature-based classification system, this paper investigated a self-learning framework to address the classification system obsolescence through the integration of two complementary sensor technologies: Inductive loop sensors and Light Detection and Ranging (LiDAR) sensors. In this framework, the LiDAR-based Federal Highway Administration (FHWA) classification model served as a data labeling platform to generate class labels for validating and updating the legacy signature-based model. Next, an adaptive transfer learning framework was implemented to improve the performance of a legacy inductive signature-based classification model without compromising computation efficiency. This framework demonstrates the resilience enhancement of the inductive signature-based FHWA classification model with an intelligent system update to accommodate vehicle transition over time while retaining legacy knowledge of the pre-existing population using a methodology that significantly reduces the overall burden of periodic model calibration by utilizing the information stored in the legacy model. The experiment demonstrates that this adaptive self-learning framework achieves an overall correct classification rate of 0.89 on a dataset with distinctively different truck configurations.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"768-780"},"PeriodicalIF":4.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11021459","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367055","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 Hyperheuristic Approach to Multi-Echelon Hub and Routing Optimization: Model, Valid Inequalities, and Case Study","authors":"Kassem Danach;Hassan Harb;Badih Baz;Abbass Nasser","doi":"10.1109/OJITS.2025.3565209","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3565209","url":null,"abstract":"Efficient logistics management is critical in the modern global supply chain, and this study introduces an advanced hyperheuristic approach to the Multi-Echelon Hub and Routing Optimization (MEHRO) problem. The MEHRO problem encompasses optimizing hub locations and vehicle routes while balancing cost efficiency, service quality, and environmental sustainability. A novel mathematical model integrates transportation, hub setup, and inventory costs, strengthened by valid inequalities to enhance computational efficiency. The hyperheuristic framework dynamically selects from a pool of low-level heuristics, adapting strategies to varying problem instances. A real-world case study validates the model’s effectiveness, demonstrating significant cost reductions, improved service levels, and minimized environmental impact compared to traditional methods. This work sets a foundation for scalable and adaptive solutions in logistics and combinatorial optimization, catering to the evolving demands of global supply chain management.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"781-791"},"PeriodicalIF":4.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979948","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481830","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":"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}