IEEE Open Journal of Intelligent Transportation Systems最新文献_第2页

An Intelligent Agent-Based Resilient Framework for Marine Vessel Mission Adaptations

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-02-05 DOI: 10.1109/OJITS.2025.3539419

Nikos Kougiatsos;Evelien L. Scheffers;Marcel C. van Benten;Dingena L. Schott;Peter de Vos;Rudy R. Negenborn;Vasso Reppa

{"title":"An Intelligent Agent-Based Resilient Framework for Marine Vessel Mission Adaptations","authors":"Nikos Kougiatsos;Evelien L. Scheffers;Marcel C. van Benten;Dingena L. Schott;Peter de Vos;Rudy R. Negenborn;Vasso Reppa","doi":"10.1109/OJITS.2025.3539419","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3539419","url":null,"abstract":"Waterborne transport is very important for moving freight and passengers globally. To make this transport more efficient, vessel design must adapt to changing missions, regulations and the occurrence of malfunctions. This paper presents the design of an intelligent decision-support framework to assist marine engineers and vessel operators in updating the system and control architecture of marine vessels before and during a mission. The connection between the system architecture and control design perspectives is enabled using a semantics-based technique. To this end, the multi-level vessel control system is described by a semantic database, a knowledge graph used to connect the components automatically, and quantitative service criteria. Considering the system architecture, the optimal modification is deduced using modularity and complexity criteria, originating from the field of network theory. On the control side, an intelligent automation supervisor is designed to make offline and online decisions regarding the energy deficit to execute a new mission and the active automation configuration during operation. For offline decisions, system architecture modifications are requested by the vessel designers to cover the energy deficit. During operation, switching between hardware and virtual sensors as well as switching between energy management controllers is implemented to handle the effects of sensor faults. The framework is successfully applied to a case study of a tugboat used to adapt to missions with different power requirements, while simulation results are used to indicate its application in supporting the decisions of vessel designers and human vessel operators.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"184-203"},"PeriodicalIF":4.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10876184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564008","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 Re-Identification and Tracking: Algorithmic Approach, Challenges and Future Directions

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-02-03 DOI: 10.1109/OJITS.2025.3538037

Ashutosh Holla B.;Manohara M. M. Pai;Ujjwal Verma;Radhika M. Pai

{"title":"Vehicle Re-Identification and Tracking: Algorithmic Approach, Challenges and Future Directions","authors":"Ashutosh Holla B.;Manohara M. M. Pai;Ujjwal Verma;Radhika M. Pai","doi":"10.1109/OJITS.2025.3538037","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3538037","url":null,"abstract":"Vehicle re-identification and tracking play a vital role in intelligent transportation systems as they enhance traffic management, improve safety, and optimize flow by precisely monitoring and analyzing vehicle movements across various locations. This technology enables the collecting of data in real-time, which allows for effective identification of incidents, enforcement of laws, and decision-making in urban planning. Deep learning techniques used in vehicle re-identification extract distinct characteristics to identify and match a vehicle across different camera perspectives. This bridges the non-overlapping field of camera views and forms a relationship between the detected vehicles. Tracking enhances this process by assigning a distinct identifier to the recognized vehicle, allowing for the creation of a continuous trajectory across the network for further analysis. Vehicle re-identification and tracking have made substantial progress in recent years as a result of the accelerated development of deep learning. Consequently, it is imperative to conduct a thorough examination of these chores. To provide a detailed picture of the research towards vehicle re-identification and tracking, this study provides the recent advancements of various datasets, and frameworks and strategies undertaken to perform these tasks. Specifically, the paper provides a comprehensive review of the different modes of re-identification of vehicles and further analysis. The paper also discusses the challenges and directions that can be taken in future for vehicle re-identification and tracking.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"155-183"},"PeriodicalIF":4.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870125","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564006","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

Freeway Traffic Modeling by Physics-Regularized Gaussian Processes

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-01-31 DOI: 10.1109/OJITS.2025.3532796

Kleona Binjaku;C. Pasquale;E. K. Meçe;S. Sacone

{"title":"Freeway Traffic Modeling by Physics-Regularized Gaussian Processes","authors":"Kleona Binjaku;C. Pasquale;E. K. Meçe;S. Sacone","doi":"10.1109/OJITS.2025.3532796","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3532796","url":null,"abstract":"Effective traffic management and control are essential for mitigating congestion and minimizing environmental impacts on road transportation systems. In this paper, we propose a novel approach for traffic modeling that integrates physics-based dynamics with machine learning techniques. Our method leverages Gaussian Processes (GPs) and a multi-class second-order discrete traffic model known as METANET to develop a Physics-Regularized Machine Learning framework. Furthermore, the proposed approach includes for the first time multi-class on/off ramps within the modeling framework, enhancing the realism of the predictive model. We systematically evaluate the performance of the hybrid model across varying dataset sizes to determine optimal data requirements for accurate traffic predictions. Experimental results indicate the improved predictive performance of the proposed approach compared to traditional machine learning and physics-based models. Our findings underscore the potential of Physics-Regularized Machine Learning for enhancing traffic management and control strategies in real-world scenarios.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"116-130"},"PeriodicalIF":4.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10859260","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512935","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

Designing Directional Traffic Flow With Edge Mode Combination in 2-D Topological Structures

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-01-30 DOI: 10.1109/OJITS.2025.3536469

Hiroya Tanaka;Keita Funayama

引用次数: 0

2024 Index IEEE Open Journal of Intelligent Transportation Systems Vol. 5

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-01-27 DOI: 10.1109/OJITS.2025.3534516

引用次数: 0

Safety-Critical Oracles for Metamorphic Testing of Deep Learning LiDAR Point Cloud Object Detectors

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-01-22 DOI: 10.1109/OJITS.2025.3532777

Simon Speth;Maximilian Trien;Dominik Kufer;Alexander Pretschner

{"title":"Safety-Critical Oracles for Metamorphic Testing of Deep Learning LiDAR Point Cloud Object Detectors","authors":"Simon Speth;Maximilian Trien;Dominik Kufer;Alexander Pretschner","doi":"10.1109/OJITS.2025.3532777","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3532777","url":null,"abstract":"Robustness testing is crucial for verifying autonomous vehicles, especially for safety-critical deep learning components like light detection and ranging (LiDAR) object detectors. Metamorphic testing (MT) assesses the robustness by automatically generating test cases based on abstract system specifications known as metamorphic relations (MRs). However, a key challenge is ensuring a traceable safety argumentation for MRs that is in line with industry standards. To ensure this traceability, we derive seven traceable metamorphic transformations from defects identified through interviews with industry experts. Another challenge is prioritizing failures by safety criticality, as not all failing test cases, as evaluated by current intersection over union (IoU)-based metamorphic oracles, pose the same safety risk. We address this by introducing novel egocentric test oracles based on traffic participants’ bounding boxes shifted into or out of the ego vehicle’s expected lane. Testing five LiDAR object detection systems working on two datasets by executing half a million metamorphic test cases (MTCs) shows that the number of failures decreases from 48k using IoU metrics to 342 safety-critical failures with our novel test oracle “shift out of ego lane.” This reduction enables testers to stay within the test analysis budget and, hence, manually analyze each failed MTC by prioritizing safety-critical test failures.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"95-108"},"PeriodicalIF":4.6,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10849578","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361044","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

Evaluation of CNN-Based Approaches to Adverse Weather Image Classification for Autonomous Driving Systems

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-01-21 DOI: 10.1109/OJITS.2025.3532389

Viktoria Afxentiou;Tanya Vladimirova

{"title":"Evaluation of CNN-Based Approaches to Adverse Weather Image Classification for Autonomous Driving Systems","authors":"Viktoria Afxentiou;Tanya Vladimirova","doi":"10.1109/OJITS.2025.3532389","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3532389","url":null,"abstract":"Weather image classification is a critical component of the vision systems in autonomous driving systems (ADSs), facilitating accurate decision-making across diverse driving conditions. Adverse weather conditions (AWCs) can significantly impair sensor data quality, diminishing the ADSs’ ability to interpret the surrounding environment. It is, therefore, essential for ADSs to effectively perceive and adapt to AWCs, ensuring enhanced performance and safety. This paper introduces a novel evaluation methodology for classifying AWC images using Convolutional Neural Network (CNN) models, with the goal of assessing their effectiveness for use in ADSs. The methodology provides a structured process for evaluating CNN models, taking into account key factors such as architectural designs, model sizes, diverse datasets, AWC scenarios, and real-time performance. A bespoke design framework is developed to guide the experimental modelling work, incorporating a range of representative CNN-based classification approaches and a variety of AWCs datasets and weather scenarios. This is followed by a comprehensive comparative performance analysis for both single-label and multi-label classification of AWCs images, which is grounded in an extensive experimental modelling effort and serves the purpose of validating the proposed novel evaluation methodology. The analysis systematically evaluates the performance of the targeted CNN approaches under consistent conditions, utilizing the same datasets and weather scenarios to provide a thorough and reliable comparison. Additionally, it includes performance testing on a small-scale embedded computing platform to examine real-time applicability. The findings and insights from this study aim to help researchers identify the most suitable CNN-based weather image classification approaches for their ADS application, ensuring alignment with their performance and operational requirements.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"204-229"},"PeriodicalIF":4.6,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10848144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570887","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

IEEE OPEN JOURNAL OF THE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY 《Ieee智能交通系统学会开放期刊》

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-01-20 DOI: 10.1109/OJITS.2025.3525887

引用次数: 0

Cooperative Localization of Multi-Agent Autonomous Aerial Vehicle (AAV) Networks in Intelligent Transportation Systems

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-01-20 DOI: 10.1109/OJITS.2025.3531363

S. Shahkar

{"title":"Cooperative Localization of Multi-Agent Autonomous Aerial Vehicle (AAV) Networks in Intelligent Transportation Systems","authors":"S. Shahkar","doi":"10.1109/OJITS.2025.3531363","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3531363","url":null,"abstract":"GNSS-independent localization is one of the most prominent research problems in aerial autonomous systems navigation, especially in certain applications where Simultaneous Localization and Mapping (SLAM) methods are inapplicable due to the complexity of the environment, or in open-air spaces where a flock of Autonomous Aerial Vehicles (AAVs) navigate in a GNSS-independent fashion. This paper introduces a filter through which AAVs form a multi-agent Cellular Vehicle-to-Everything (C-V2X) network to exchange their estimated positions, and eventually achieve a group consensus over the true position of each vehicle. The localization error correction takes place in the filter with reference to the AAV’s relative range from neighbouring vehicles, that is measured by onboard ranging devices. It is shown that in ideal situations where rangefinder errors can be neglected, cooperative localization yields perfect localization, if the network is sufficiently large and sufficiently connected. It is also shown that the accuracy of cooperative localization is superior to the existing least-mean-square-error based techniques, where a centralized controller augments the positioning accuracy of the flock. Cooperative localization is also favourable due to the fact that the process is computationally affordable and fully distributed. Theoretical derivations and results have been validated through case studies and Monte Carlo simulations, and suggest cooperative localization as a complementary navigation technique to odometery, and other advanced solutions that are available in the literature.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"49-66"},"PeriodicalIF":4.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10845814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184134","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

IEEE Open Journal of Intelligent Transportation Systems Instructions for Authors IEEE智能交通系统开放杂志作者指南

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-01-20 DOI: 10.1109/OJITS.2025.3525889

引用次数: 0