IEEE Open Journal of Intelligent Transportation Systems最新文献

{"title":"Fuzzy Logic-Enhanced Sustainable and Resilient EV Public Transit Systems for Rural Tourism","authors":"Rapeepan Pitakaso;Thanatkij Srichok;Surajet Khonjun;Peerawat Luesak;Chutchai Kaewta;Sarayut Gonwirat;Prem Enkvetchakul;Rerkchai Srivoramas","doi":"10.1109/OJITS.2025.3554204","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3554204","url":null,"abstract":"The integration of electric vehicles (EVs) into public transit systems is crucial for enhancing sustainability and operational efficiency, particularly in rural tourism regions where demand is highly variable and infrastructure constraints pose unique challenges. Traditional transportation planning approaches often lack the adaptability required to handle the fluctuating nature of tourist mobility, leading to inefficiencies in service coverage and resource utilization. While fuzzy logic-based models have been extensively applied in urban transit optimization, their applicability to rural EV public transit remains underexplored. This study addresses this gap by developing the Fuzzy-Artificial Multiple Intelligence System (F-AMIS), an enhanced version of the Artificial Multiple Intelligence System (AMIS). F-AMIS integrates new intelligence boxes and an optimized selection formula, allowing for real-time adaptive decision-making in EV bus networks. A real-world case study demonstrates that F-AMIS significantly outperforms conventional optimization methods, achieving a 20% reduction in operational costs and increasing service coverage from 75% to 90%, while also enhancing resilience and sustainability indices. These findings highlight the potential of F-AMIS as a scalable, intelligent optimization framework for improving the efficiency and sustainability of rural EV transit systems. Future research should explore integrating F-AMIS with advanced AI-driven decision models, refining fuzzy logic techniques for rural-specific constraints, and assessing the model’s adaptability across diverse global tourism networks to further enhance its applicability and impact.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"407-432"},"PeriodicalIF":4.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800743","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":"Modeling and Simulation of Automotive FMCW RADAR Sensor for Environmental Perception","authors":"Arsalan Haider;Abdulkadir Eryildirim;Marcell Pigniczki;Lukas Haas;Birgit Schlager;Thomas Zeh;David Nickel;Alexander W. Koch","doi":"10.1109/OJITS.2025.3554452","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3554452","url":null,"abstract":"Frequency-modulated continuous wave (FMCW) radio detection and ranging (RADAR) sensors have become indispensable technologies for automated driving systems (ADS) due to their reliability in adverse weather conditions and their ability to simultaneously measure the distance to objects, relative radial velocity, and azimuth and elevation angles. The automotive industry has increasingly considered simulation-based testing of autonomous vehicles due to safety, cost, and time constraints. This raises the need for virtual environmental perception sensors that provide results close to reality. This work presents the design and structure of a ray-tracing-based, high-fidelity, tool-independent baseband FMCW RADAR sensor model. The RADAR sensor model is developed using the standardized functional mock-up interface (FMI) and open simulation interface (OSI) and is integrated into the co-simulation environment of commercial software to demonstrate its exchangeability. The RADAR FMU model incorporates a multiple input and multiple output (MIMO) 2D linear spacing virtual antenna array, non-coherent integration (NCI) of range-Doppler maps (RDMs) over receiver antennas, a constant false alarm rate (CFAR) to obtain an interim object detection list, and density-based spatial clustering of applications with noise (DBSCAN) to provide a single detection per object. The presented RADAR FMU model also includes RADAR sensor-specific impairments such as phase noise (PN), radio frequency (RF) group delay, phase imbalance (PI) of transmitter antennas, mixer non-linearity including third-order intermodulation products (IM3), and noise figure (NF) of receiver antennas. Additionally, this work presents a methodology for plausibly verifying the RADAR sensor model at the raw data level (range map (RM) and RDM) and object detection list level. The simulation results are compared with real sensor measurements to validate the modeling of sensor-specific impairments. The mean absolute percentage error (MAPE) metric is used to quantify the difference between the simulation and real sensor measurements. The results demonstrate that the complete signal processing toolchain and sensor-specific impairments of the RADAR sensor must be considered to achieve simulation results that closely resemble those of the real sensor.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"433-455"},"PeriodicalIF":4.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938201","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792843","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":"Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey","authors":"Victor M. G. Martinez;Divanilson R. Campelo;Moises R. N. Ribeiro","doi":"10.1109/OJITS.2025.3553696","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3553696","url":null,"abstract":"Intelligent transportation systems (ITS) have been attracting the attention of industry and academia alike for addressing issues raised by the 2030 agenda for sustainable development goals (SDG) approved by the United Nations. However, the diversity and dynamics of present-day transportation scenarios are already very complex, turning the management of ITS into a virtually impossible task for conventional traffic control centers. Recently, the digital twin (DT) paradigm has been presented as a modern architectural concept to tackle complex problems, such as the ones faced by ITS. This survey aims to provide a piece-wise approach to introducing DTs into sustainable ITS by addressing the following cornerstone aspects: i) Why should one consider DTs in ITS applications? ii) What can DTs represent from ITS’ new physical environments? And iii) How can one use DTs to address ITS SDG related to efficiency, safety, and ecology? Our methodological approach for surveying the literature addresses these questions by categorizing contributions and discriminating their ITS elements and agents against the SDG they addressed. Thus, this survey provides an in-depth and contextualized overview of the challenges when approaching ITS through DTs, including scenarios involving autonomous and connected vehicles, ITS infrastructure, and traffic agents’ behavior. Moreover, we propose a functional reference framework for developing DTs of ITS. Finally, we also offer research challenges regarding standardization, connectivity infrastructure, security and privacy aspects, and business management for properly developing DTs for sustainable ITS.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"363-392"},"PeriodicalIF":4.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792783","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":"Pedestrian Vision Language Model for Intentions Prediction","authors":"Farzeen Munir;Shoaib Azam;Tsvetomila Mihaylova;Ville Kyrki;Tomasz Piotr Kucner","doi":"10.1109/OJITS.2025.3554387","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3554387","url":null,"abstract":"Effective modeling of human behavior is crucial for the safe and reliable coexistence of humans and autonomous vehicles. Traditional deep learning methods have limitations in capturing the complexities of pedestrian behavior, often relying on simplistic representations or indirect inference from visual cues, which hinders their explainability. To address this gap, we introduce PedVLM, a vision-language model that leverages multiple modalities (RGB images, optical flow, and text) to predict pedestrian intentions and also provide explainability for pedestrian behavior. PedVLM comprises a CLIP-based vision encoder and a text-to-text transfer transformer (T5) language model, which together extract and combine visual and text embeddings to predict pedestrian actions and enhance explainability. Furthermore, to complement our PedVLM model and further facilitate research, we also publicly release the corresponding dataset, PedPrompt, which includes the prompts in the Question-Answer (QA) template for pedestrian intention prediction. PedVLM is evaluated on PedPrompt, JAAD, and PIE datasets demonstrates its efficacy compared to state-of-the-art methods. The dataset and code will be made available at <uri>https://github.com/munirfarzeen/Ped_VLM</uri>.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"393-406"},"PeriodicalIF":4.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792931","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 5G Roadside Infrastructure Assisting Connected and Automated Vehicles in Vulnerable Road User Protection","authors":"Raffaele Viterbo;Federico Campolo;Mattia Cerutti;Satyesh Shanker Awasthi;Stefano Arrigoni;Mattia Brambilla;Monica Nicoli","doi":"10.1109/OJITS.2025.3552849","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3552849","url":null,"abstract":"Road safety is one of the major concerns when considering road vehicles, with issues referring to both drivers and Vulnerable Road Users (VRUs) (e.g., pedestrians). The role of roadside infrastructures in augmenting VRU protection is gaining popularity thanks to the development of smart and connected sensing systems, which enable the detection of critical events and their forwarding to nearby Connected and Automated Vehicles (CAVs) by Vehicle-to-Everything (V2X) communications. In this context, the fifth generation (5G) of mobile radio networks is emerging as the communication enabler for advanced V2X services for CAVs. In this work we propose the design, development, and assessment on road of a 5G roadside infrastructure for VRU protection, performing an in-depth analysis of the single components and their integration into the service. More specifically, we present a Roadside Unit (RSU) capable to detect, localize, and track VRUs; a communication architecture that exploits 5G V2X connectivity for sending VRUs detections; a CAV with on-board algorithms warning the driver of the presence of the VRU and an Autonomous Emergency Braking (AEB) solution triggered by the 5G V2X architecture. We validate the efficacy of the proposed solution in a road scenario inside the campus of Politecnico di Milano.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"346-362"},"PeriodicalIF":4.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10934058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800770","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":"Security Enhancement in AAV Swarms: A Case Study Using Federated Learning and SHAP Analysis","authors":"Sushmitha Halli Sudhakara;Lida Haghnegahdar","doi":"10.1109/OJITS.2025.3550792","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3550792","url":null,"abstract":"As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging federated learning and FedAvg for weight averaging, combined with SHAP analysis to identify key contributing features. This AI-based system requires less human intervention and is more effective in detecting novel attacks than traditional intrusion detection systems (IDS). Using the IEEE DataPort AAV Attack Dataset, this study aims to develop a robust distributed ML security solution for AAV swarms, significantly advancing the cybersecurity landscape for CPSs.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"335-345"},"PeriodicalIF":4.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769405","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":"Integrating Multimodality and Partial Observability Solutions Into Decentralized Multiagent Reinforcement Learning Adaptive Traffic Signal Control","authors":"Kareem Othman;Xiaoyu Wang;Amer Shalaby;Baher Abdulhai","doi":"10.1109/OJITS.2025.3550312","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3550312","url":null,"abstract":"Adaptive Traffic Signal Control (ATSC) systems leverage sensor data to dynamically adjust signal timings based on real-time traffic conditions but they often suffer from partial observability (PO) due to sensor limitations and restricted detection ranges. This study addresses PO in fully decentralized ATSC systems by introducing eMARLIN-T, a controller designed to enhance performance by incorporating historical information in the decision-making process. Additionally, ATSC systems are commonly optimized to improve the performance of the general traffic, ignoring the impact on transit. On the other hand, traditional transit signal priority (TSP) strategies, which overlay preferential strategies for transit vehicles onto general traffic fixed signal plans, often lead to negative impacts on the general traffic. Thus, this paper tackles the challenge of optimizing traffic signals to benefit both public transit and general vehicular traffic. To address this, a novel decentralized multimodal multiagent reinforcement learning (RL) signal controller, eMARLIN-T-MM, is developed. This controller integrates a transformer-based encoder for transforming the state observations into a latent space and an executor Q-network for decision-making. Tested on a simulation of five intersections in North York, Toronto, eMARLIN-T-MM significantly reduces the total person delays by 58% to 74% across various bus occupancy levels compared to pre-timed signals, outperforming the other decentralized RL-based ATSCs. In addition, eMARLIN-T-MM can automatically adapt to changes in the levels of occupancy, allowing it to optimize the intersection performance in response to varying transit and traffic demands.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"322-334"},"PeriodicalIF":4.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10922202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716453","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":"May the ODD Be With You: A Stakeholder Analysis on Operational Design Domains of Automated Driving Systems","authors":"Marcel Aguirre Mehlhorn;Hauke Dierend;Andreas Richter;Yuri A. W. Shardt","doi":"10.1109/OJITS.2025.3548161","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3548161","url":null,"abstract":"Developing an automated driving system (ADS) is a complex task requiring collaboration between various stakeholders. To support this process, the concept of operational design domain (ODD) has emerged to define the conditions under which an ADS is designed to operate safely. Nonetheless, stakeholders require variable levels of information from an ODD to effectively integrate it into their work processes. Considering the ODD as a crucial asset in developing an ADS, a general overview of how stakeholders use and update the ODD is missing. Thus, this paper focuses on the identification and thorough investigation of eight main stakeholder categories, including self-driving system developers, regulators, and ADS customers. Each of the stakeholder categories will be further subdivided to provide a comprehensive understanding of all the ODD stakeholders involved. Furthermore, a stakeholder analysis is used to assess the ODD stakeholders’ expectations, interests, and influence through qualified interviews with experts of each domain. The analysis identifies the inputs received and the outputs supplied based on the corresponding levels of detail. These findings are summarised in a comprehensive overview to map all necessary ODD engineering requirements and deliverables for all the stakeholders that have been identified. Furthermore, this analysis explains the importance and implications of an ODD for the associated academic and business contexts.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"310-321"},"PeriodicalIF":4.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912497","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716543","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":"An Adaptive Hierarchical Framework With Contrastive Aggregation for Traffic Sign Classification","authors":"Rodolfo Valiente;Jiejun Xu;Alireza Esna Ashari","doi":"10.1109/OJITS.2025.3544262","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3544262","url":null,"abstract":"Autonomous vehicles rely on accurate traffic sign classification, which is typically achieved through supervised learning. However, the diversity and complexity of traffic signs make it impractical to rely solely on large labeled datasets. While abundant data exists for common signs such as stop and yield signs, less common signs often lack sufficient representation in existing datasets. Few-shot learning has been proposed as an alternative solution for such cases in which there is not enough training data, but its effectiveness decreases as the number of classes increases. To address these challenges, our research introduces an innovative adaptive hierarchical framework with contrastive aggregation (HF-CA). This framework strategically reduces class dimensionality and enriches the dataset with more examples per category through contrastive aggregation. We validated our approach using modified versions of the GTSRB and Mapillary datasets, demonstrating that our method consistently outperforms existing baselines. By simplifying the classification process, our solution enhances classification accuracy and provides a scalable approach for scenarios with numerous classes but limited labels.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"230-243"},"PeriodicalIF":4.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10907807","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611809","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":"Vehicle Egomotion Estimation Through IMU-RADAR Tight-Coupling","authors":"Stijn Harbers;Jens Kalkkuhl;Tom van der Sande","doi":"10.1109/OJITS.2025.3546685","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3546685","url":null,"abstract":"The current state-of-the-art vehicle egomotion state estimation systems are limited in their usage for advanced driver-assistance systems (ADAS), as the estimation accuracy is limited in driving scenarios where large amounts of wheel slip occur. Addressing the fundamental limitations of vehicle egomotion state estimation, this study investigates the usage of RADAR in vehicle egomotion state estimation through a tight-coupling between an Inertial Measurement Unit (IMU) and RADAR. The limitations of the state-of-the-art are caused by the usage of automotive grade sensors, which provide limited accuracy. RADAR is a type of sensor, which is already used extensively in ADAS, however, not yet in egomotion estimation. A reason for not using RADAR in this context is that it requires knowledge of the motion of the detected targets. In literature statistical methods are suggested to reject moving detections, but these are per definition not robust. This research, therefore, answers the question: How can RADAR be used in vehicle egomotion state estimation to improve performance and expand the capabilities of the system in a robust way? A new method is developed, which through IMU-RADAR tight-coupling, is able to reject moving detections. These stationary detections are then integrated into a Kalman filter to obtain the vehicle motion. This new method is compared to the state-of-the-art methods and the results are validated on a real data set of a vehicle driving in urban and highway setting. The findings demonstrate that the newly introduced method enhances the accuracy of vehicle egomotion state estimation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"244-255"},"PeriodicalIF":4.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10907934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611808","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}