Jindou Zhang, Zhiwen Wang, Long Li, Kangkang Yang, Yanrong Lu
{"title":"Trajectory tracking control of autonomous vehicles based on event-triggered model predictive control","authors":"Jindou Zhang, Zhiwen Wang, Long Li, Kangkang Yang, Yanrong Lu","doi":"10.1049/itr2.12589","DOIUrl":"https://doi.org/10.1049/itr2.12589","url":null,"abstract":"<p>This paper presents a lateral control scheme based on event-triggered model predictive control for trajectory tracking of autonomous vehicles. Firstly, the augmentation system is constructed based on the known road curvature information, and the model predictive controller is utilized to obtain the optimal control sequence. Then, an event-triggered mechanism is introduced to improve the real-time performance of the control system. The strategy targets to reduce the computational complexity and solving frequency of the optimization problem. In addition, a contraction constraint is structured using the backstepping control strategy to ensure the stability of the control system. Finally, experiments are conducted through the CarSim/Simulink joint simulation platform, and compared with the traditional model predictive control, the method proposed in this paper has better tracking accuracy and improves the real-time performance of the control system.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 S1","pages":"2856-2868"},"PeriodicalIF":2.3,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12589","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Facets of security and safety problems and paradigms for smart aerial mobility and intelligent logistics","authors":"Simeon Okechukwu Ajakwe, Dong-Seong Kim","doi":"10.1049/itr2.12579","DOIUrl":"https://doi.org/10.1049/itr2.12579","url":null,"abstract":"<p>The use of unmanned aerial vehicles (UAVs) for smart and speedy logistics is still relatively nascent compared to traditional delivery methods. However, it is witnessing sporadic and steady growth due to booming demands, technological advancement, and regulatory support. The intelligence and integrity of UAV systems depend largely on the underlying cognitive and cybersecurity models, which serve as both eyes and brains to perceive and respond to the myriad of scenarios around them. Smart mobility and intelligent logistic ecosystems (SMiLE) are complex and advanced technological networks which are exposed to several issues. The incorporation of UAVs for priority logistics, thereby extending the coverage and capacity of SMiLE, further heightens these vulnerabilities and questions its security, safety, and sustainability. This review scrutinizes the significant security disruptions, smartness dynamics, and sundry developments for the sustainable deployment of UAVs as an aerial logistics-based vehicle. Using the PRISMA-SPIDER methodology, 157 articles were selected for quantitative analysis and 20 review articles for qualitative evaluation. Security and safety issues in UAVs cut across all the layers of logistics operations: components, communication, network architecture, navigation, supply chain etc. Expanding the capacity of SMiLE using UAV demands an intentional and incremental convergence-based integration of an agile explainable artificial framework for reliable and safety-conscious smart mobility, a scalable and tamperproof blockchain for multi-factor authentication, and a zero trust cybersecurity paradigm for inclusive enterprise-based authorization.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 S1","pages":"2827-2855"},"PeriodicalIF":2.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Development of optimal real-time metro operation strategy minimizing total passenger travel time and train energy consumption","authors":"Yoonseok Oh, Ho-Chan Kwak, Seungmo Kang","doi":"10.1049/itr2.12582","DOIUrl":"https://doi.org/10.1049/itr2.12582","url":null,"abstract":"<p>The optimization of the total passenger travel time and total train energy consumption are critical factors in metro operation optimization. However, deriving an optimal train operation plan that incorporates both passenger travel time and total train energy consumption is a complex task because it should consider numerous variables representing the operational status of the urban railway, such as the number of boarding and alighting passengers, number of on-board passengers in each train, and entire train operation status along the line. Moreover, owing to the fluctuating nature of passenger demand, which can change rapidly over time, its optimization becomes challenging. To address this challenge, this study develops a recurrent neural network-based real-time metro operation optimization model trained using data representing the moments when the trains departed from the stations. These data are derived and reconstructed from various simulated operation plans while searching for optimal daily metro timetable. Consequently, the proposed model derives the real-time optimal operation strategies for trains departing from the next station within an average of 0.18 s. The result of metro operation simulations using proposed optimal operation strategies reveals a 7–14% improvement in efficiency compared to the current train operation strategies.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2440-2458"},"PeriodicalIF":2.3,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Spatio-temporal dynamic navigation for electric vehicle charging using deep reinforcement learning","authors":"Ali Can Erüst, Fatma Yıldız Taşcıkaraoğlu","doi":"10.1049/itr2.12588","DOIUrl":"https://doi.org/10.1049/itr2.12588","url":null,"abstract":"<p>This paper considers the real-time spatio-temporal electric vehicle charging navigation problem in a dynamic environment by utilizing a shortest path-based reinforcement learning approach. In a data sharing system including transportation network, an electric vehicle (EV) and EV charging stations (EVCSs), it is aimed to determine the most convenient EVCS and the optimal path for reducing the travel, charging and waiting costs. To estimate the waiting times at EVCSs, Gaussian process regression algorithm is integrated using a real-time dataset comprising of state-of-charge and arrival-departure times of EVs. The optimization problem is modelled as a Markov decision process with unknown transition probability to overcome the uncertainties arising from time-varying variables. A recently proposed on-policy actor–critic method, phasic policy gradient (PPG) which extends the proximal policy optimization algorithm with an auxiliary optimization phase to improve training by distilling features from the critic to the actor network, is used to make EVCS decisions on the network where EV travels through the optimal path from origin node to EVCS by considering dynamic traffic conditions, unit value of EV owner and time-of-use charging price. Three case studies are carried out for 24 nodes Sioux-Falls benchmark network. It is shown that phasic policy gradient achieves an average of 9% better reward compared to proximal policy optimization and the total time decreases by 7–10% when EV owner cost is considered.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2520-2531"},"PeriodicalIF":2.3,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A literature review on the applications of artificial intelligence to European rail transport safety","authors":"Habib Hadj-Mabrouk","doi":"10.1049/itr2.12587","DOIUrl":"https://doi.org/10.1049/itr2.12587","url":null,"abstract":"<p>In accordance with the current European railway regulations and particularly the two directives relating to the interoperability (Directive (EU) 2016/797) and safety (Directive (EU) 2016/798) of the railway system, this literature review proposes to classify artificial intelligence (AI) applications by distinguishing the structural elements (Infrastructure, Energy, Control-Command-Signalling and Rolling Stock) and the functional elements (Operation and Traffic Management, Maintenance and Telematics Applications) of the European railway system. Several “classic” AI techniques are implemented, including machine learning (supervised, semi-supervised, unsupervised), deep learning such as artificial neural networks (ANN), natural language processing (NLP), case-based reasoning (CBR), etc. However, the inadequacy of these approaches to capitalize, share and reuse the knowledge involved has oriented research towards the development of new approaches based on ontologies and knowledge graphs. This study shows that the stages of data acquisition, modeling, processing and interpretation pose a crucial problem in rail transport. In addition, with complex models described as “black boxes”, it is difficult to understand how the internal reasoning mechanisms of the AI system impact the solution and predictions. The new explainable AI (XAI) approach can possibly provide an element of response to this problem.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2291-2324"},"PeriodicalIF":2.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cooperative control of mixed vehicle platoon based on pinning consensus of heterogeneous multi-agent system","authors":"Wenju Du, Changxi Ma, Jiangang Zhang","doi":"10.1049/itr2.12585","DOIUrl":"https://doi.org/10.1049/itr2.12585","url":null,"abstract":"<p>This paper proposes a longitudinal controller for the mixed vehicle platoon in the presence of driver response time-delay and communication time-delay based on the pinning consensus of heterogeneous multi-agent system. Firstly, an adaptive pinning consensus control protocol and derive sufficient conditions for the heterogeneous non-linear multi-agent system are designed to achieve pinning consistency. Then, the heterogeneous characteristics of the vehicle are described based on the car-following model, then the mixed vehicle platoon system model is constructed and a mixed vehicle platoon controller based on heterogeneous multi-agent pinning consensus is proposed. Besides, the driver response time-delay and communication time-delay are introduced into the model, and a controller based on time-delay heterogeneous multi-agent pinning consensus is designed. Finally, the effectiveness of the proposed controller is verified by numerical simulations, and the effect of number of connected autonomous vehicle, different types of vehicle order, driver response time-delay, communication time-delay and the parameter of the controller on stability of mixed vehicle platoon is also quantitatively demonstrated.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2485-2501"},"PeriodicalIF":2.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12585","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Justin Moskolaï Ngossaha, Léonce Thérèse Pidy Pidy, Thenuka Karunathilake, Anna Förster, Samuel Bowong
{"title":"Advancing urban mobility in developing countries: A mobile RSU approach for sustainable transportation","authors":"Justin Moskolaï Ngossaha, Léonce Thérèse Pidy Pidy, Thenuka Karunathilake, Anna Förster, Samuel Bowong","doi":"10.1049/itr2.12586","DOIUrl":"https://doi.org/10.1049/itr2.12586","url":null,"abstract":"<p>The rapid and uncontrolled urbanization of cities in developing countries has engendered a plethora of urban mobility issues, including traffic congestion, accidents, and air pollution. To address these challenges, contemporary urban mobility trends are incorporating innovative technologies and sustainable governance practices. This article investigates how urban managers can leverage the opportunities presented by cost-effective technologies and the management of urban data to enhance urban mobility in developing countries. Within this discourse, an RSU-based approach is proposed that employs motorbike taxis for inter-vehicular communication, given their status as the most widely used form of public transportation. This approach substantially diminishes investment costs and reinforces the sustainability of urban mobility. Through the implementation of this solution, a noteworthy reduction is anticipated in the emission of gases such as CO2, and NOx, known contributors to climate change and various respiratory diseases. To validate the efficacy of the proposed solution, four distinct scenarios are scrutinized in a case study centered on the city of Douala in Cameroon, utilizing tools such as OMNET++, SUMO, Veins, and INET. The proposed framework offers significant benefits in terms of environmental sustainability and operational efficiency. It enables a 10% reduction in CO2 emissions, a 15% reduction in NOx emissions, an 11% drop in fuel consumption, and a 15% reduction in waiting time in traffic jams. The envisaged solution aims to aid urban managers in their decision-making processes, specifically in advancing sustainable urban mobility. Through the adoption of this approach, cities in developing countries can mitigate challenges associated with urban mobility and enhance the overall well-being of their residents.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2502-2519"},"PeriodicalIF":2.3,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Niloofar Minbashi, Jiaxi Zhao, C. Tyler Dick, Markus Bohlin
{"title":"Enhancing freight train delay prediction with simulation-assisted machine learning","authors":"Niloofar Minbashi, Jiaxi Zhao, C. Tyler Dick, Markus Bohlin","doi":"10.1049/itr2.12573","DOIUrl":"https://doi.org/10.1049/itr2.12573","url":null,"abstract":"<p>Boosting the rail freight modal share is an ambitious target in Europe and North America. Yards, where freight trains are arranged, can be crucial in realizing this target by reliable dispatching to the network. This paper predicts freight train departures by developing a simulation-assisted machine learning model with two concepts: general (adding all predictors at once) and step-wise (adding predictors as they become available in sub-yard operations) for hump yards with the conventional layout to provide a generalized model for European and North American contexts. The developed model is a decision tree algorithm, validated via 10-fold cross-validation. The model's performance on three data sets—a real-world European yard, a baseline simulation, and an ultimate randomness simulation for a comparable North American yard—shows a respective <span></span><math>\u0000 <semantics>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 <annotation>$R^2$</annotation>\u0000 </semantics></math> of 0.90, 0.87, and 0.70. Step-wise inclusion of the predictors results differently for the real-world and simulation data. The global feature importance highlights maximum planned length, departure weekday, the number of arriving trains, and minimum arrival deviation as key predictors for the real-world data. For the simulation data, the most significant predictors are departure yard predictors, the number of arriving trains, and the maximum hump duration. Additionally, utilization rates—except for the receiving yard—enhance the predictions.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2359-2374"},"PeriodicalIF":2.3,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12573","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Linzhen Nie, Meihe Lu, Zhiwei He, Jiachen Hu, Zhishuai Yin
{"title":"Multispectral pedestrian detection based on feature complementation and enhancement","authors":"Linzhen Nie, Meihe Lu, Zhiwei He, Jiachen Hu, Zhishuai Yin","doi":"10.1049/itr2.12562","DOIUrl":"https://doi.org/10.1049/itr2.12562","url":null,"abstract":"<p>Multispectral pedestrian detection with visible light and infrared images is robust to changes in lighting conditions and therefore is of great importance to numerous applications that require all-day environmental perception. This paper proposes a novel method named FCE-RCNN, which integrates saliency detection as a sub-task and utilizes global information for enhanced feature representation. The approach enhances thermal inputs by incorporating gradients at the raw-data level before feature extraction. Utilizing a dual-stream backbone, a global semantic information extraction module is introduced that combines pooling with horizontal–vertical attention mechanisms, capturing high-quality global semantic information for lower-level feature enrichment and guidance. Additionally, the pedestrian locality enhancement module is designed to enhance spatial locality information of pedestrians through saliency detection. Furthermore, to alleviate the challenges posed by positional shifts between cross-spectral features, deformable convolution is innovatively employed. Experimental results on the KAIST dataset demonstrate that FCE-RCNN significantly improves nighttime detection, achieving a log-average miss rate of 6.92%, outperforming the new method ICAFusion by 0.93%. These results underscore the effectiveness of FCE-RCNN, and the method also maintains competitive inference speed, making it suitable for real-time applications.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 11","pages":"2166-2177"},"PeriodicalIF":2.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A spatiotemporal learning approach to safety-oriented individualized driving risk assessment in a vehicle-to-everything (V2X) environment","authors":"Jing Li, Xuantong Wang, Tong Zhang","doi":"10.1049/itr2.12584","DOIUrl":"https://doi.org/10.1049/itr2.12584","url":null,"abstract":"<p>Advances in real-time basic safety message (BSM) data from sensor-equipped vehicles have created new opportunities for driving risk assessments. This paper presents a machine learning approach using BSM data to provide fine-grained risk assessments, focusing on safety-critical events (SCEs) related to driving profiles, vehicle states, and road conditions. This approach formulates a bi-level risk indicator: one level measures the observable frequency of SCEs, while the other estimates their likelihood. The coarse level calculates risk scores by classifying driving profiles as normal or risky based on SCE frequency. The fine level refines these scores by comparing normal and risky profiles using key features from a feature learning model. This combined system accounts for recent driving behaviours and road/weather conditions within a vehicle-to-everything (V2X) environment, addressing high data dimensionality and imbalance. A comprehensive case study using 1 year of data from pilot V2X infrastructure in Tampa, Florida, demonstrates the efficacy of this approach, showing practical applications of the SCE-based risk indicator and combinatorial feature learning while also highlighting the real-world utility of the assessment method in providing a detailed and actionable view of driving risk based on V2X information.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 12","pages":"2459-2484"},"PeriodicalIF":2.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12584","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}