IET Intelligent Transport Systems最新文献

{"title":"Trajectory Prediction Model of Electric Vehicle Autonomous Driving Based on Hybrid Attention Transformer Network","authors":"Bo Wang,&nbsp;Yao Liu,&nbsp;Rui Wang,&nbsp;Qiuye Sun","doi":"10.1049/itr2.70022","DOIUrl":"10.1049/itr2.70022","url":null,"abstract":"<p>Current electric vehicle trajectory prediction fails to fully consider the interaction between the target vehicle and other vehicles, resulting in poor prediction results. In order to solve this problem, this paper proposes a hybrid attention transformer network (HATN), which is designed for more accurate trajectory prediction. Firstly, based on the transformer network, this paper introduces a self-attention mechanism and a cross attention mechanism, and proposes a feature embedding and position encoding module as well as an interactive feature extraction module, so as to achieve accurate modelling of vehicle state information. With this approach, the interactive information between traffic participants can be fully extracted by effectively utilizing the map information. Secondly, a trajectory prediction decoder is proposed to expand the solution space of the model and enhance its ability to understand the real driving rules based on the driving intention recognization results of the surrounding vehicles, so that the prediction results can be more reasonable with stronger robustness. Thirdly, according to the experiments and analysis conducted based on the large-scale open datasets BDD100K and Waymo, the results show that the proposed model has a significant improvement in prediction accuracy compared with the comparison models, which verifies the effectiveness of the proposed model.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717082","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":"Smart Steering Wheel: Design of IoMT-Based Non-Invasive Driver Health Monitoring System to Enhance Road Safety","authors":"Muhammad Adil Khan,&nbsp;Mu Chen,&nbsp;Tahir Nawaz,&nbsp;Mohamed Sedky,&nbsp;Muhammad Sheikh,&nbsp;Ali Kashif Bashir,&nbsp;Sohail Hassan","doi":"10.1049/itr2.70012","DOIUrl":"10.1049/itr2.70012","url":null,"abstract":"<p>The integration of Internet of Things (IoT) technology and medical devices in healthcare is termed the Internet of Medical Things (IoMT). This advancement holds promise for numerous applications aimed at mitigating the risk of loss of life through physiological signal monitoring. As the number of road accidents is rapidly increasing, a substantial number of car crashes occur due to medical conditions. Therefore, the need remains to develop an effective solution to enable the prevention of such accidents for enhanced road safety. Unlike existing approaches, this paper proposes a holistic IoMT-based non-invasive driver health monitoring system (DHMS) to monitor important vital signs for detecting abnormal health conditions. The proposed system consists of an embedded system, edge computing, cloud computing, and a mobile application with an alert system, to offer an end-to-end unified solution for driver physiological signal monitoring to detect abnormal health conditions that might lead to a road accident. The system is particularly suited to aid (elderly) people with medical conditions and can also be used for public transport to ensure passenger safety. A detailed experimental evaluation of the proposed system has been performed and its performance accuracy compared with standard medical devices, along with quality factors including usability, portability, and effective sensor placement.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717081","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 Two-Step Method for the Operation Rescheduling Problem at the Railway Marshalling Station","authors":"Liang Ma,&nbsp;Jin Guo,&nbsp;Yuanli Bao,&nbsp;Shumei Tao","doi":"10.1049/itr2.70023","DOIUrl":"10.1049/itr2.70023","url":null,"abstract":"<p>Current research on the operation planning problem at the railway marshalling station are mainly dependent on the assumption of correct and unaltered input data. However, due to force majeure disturbances such as train delays, engine breakdown, and so on, the operation plan may easily fail. To increase the robustness of the operation plan, we propose a two-step method for solving the operation rescheduling problem at the marshalling station, the first step is to create a static model in order to maximise the efficiency of the operation plan, and the second step is to reschedule the static plans and unexecuted plan caused by unexpected disturbances. A lexicographic multi-objective model is built to reduce disturbance perturbation while maximising station benefits. An iteration optimisation approach with initial solutions is designed to solve the whole model iteratively, and each sub-model is solved by the heuristic backtracking algorithm with constraint propagation (CPr) mechanism called CPr-HBT. Some real-life cases from Shijiazhuang marshalling station of the China Railway show that the proposed two-step method increases the robustness of the operation plans effectively, rescheduling the failed operation plans takes 60 s at most, and CPr-HBT is more efficient than the other algorithms without CPr or heuristics.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698706","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":"Global Attention-Based Dynamic Multi-Graph Convolutional Recurrent Network for Traffic Flow Forecasting","authors":"Jinfeng Hou,&nbsp;Shouwen Ji,&nbsp;Lei Chen,&nbsp;Dong Guo","doi":"10.1049/itr2.70018","DOIUrl":"10.1049/itr2.70018","url":null,"abstract":"<p>Accurate traffic flow forecasting is a challenging task in intelligent transportation system. With traffic flow forecasting being formulated as a spatio-temporal graph modelling problem, graph convolution network (GCN) is increasingly used in recent research. However, most approaches employ a single predefined or adaptive graph for convolution, which cannot adequately represent complicated dependencies inherent in real-world traffic flow data. And they are limited in learning relationships between long-distance time steps. To address these concerns, we propose a global attention-based dynamic multi-graph convolutional recurrent network (GA-DMGCRN). Specifically, we design a dynamic multi-graph convolution module based on dynamic graph learning network that generates graphs by adjusting to time-varying input data throughout the training and testing phases, allowing for the effective extraction of dynamic spatial and semantic dependencies. To capture temporal features, we propose the dynamic multi-graph convolution recurrent unit, and multihead ProbSparse self-attention with linear biases is developed to model global temporal dependencies. The proposed GA-DMGCRN is evaluated on three real traffic datasets. Compared with the baseline models, our model achieves an average improvement of 1.97%, 3.11%, and 2.01% under MAE, RMSE, and MAPE metrics, which can provide real-world value by improving traffic efficiency, mitigating congestion, and optimizing route planning.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690090","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":"Optimising Shelter Locations for Bus Evacuation and Relief Supply Under Traffic Congestion","authors":"Seong-Jong Woo,&nbsp;Seungmo Kang","doi":"10.1049/itr2.70020","DOIUrl":"10.1049/itr2.70020","url":null,"abstract":"<p>Effective disaster management requires shelter location optimisation to enhance evacuation efficiency and ensure timely relief distribution. This study integrates human evacuation and relief logistics while accounting for traffic congestion during large-scale evacuations, thereby proposing a model that prioritises bus-based evacuation to mitigate congestion and expedite movement, particularly for transit-dependent populations. Employing a metaheuristic evolutionary algorithm with a local search process, the model is applied to a flood scenario in Ulsan, South Korea and significantly outperforms alternative methods in optimising shelter placement, transportation routes and relief supply distribution. Comparative analysis indicates that the proposed shelter locations reduce total costs by 9.4% relative to manually selected nearest shelters. Additionally, neglecting network congestion was found to underestimate evacuation time by up to 41%. The proposed approach also reduces relief transportation costs by 4.5%. Sensitivity analysis examines the impact of bus availability and evacuation demand variations. This study is the first to fully incorporate city-wide traffic congestion into shelter location optimisation under multimodal evacuation scenarios.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690091","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":"Current Status and Future Prospects of Digital Twin Technology Applications in Intelligent Transportation Infrastructure Management","authors":"Chao Gao,&nbsp;Lei Jia,&nbsp;Maopeng Sun,&nbsp;Junshao Luo","doi":"10.1049/itr2.70011","DOIUrl":"10.1049/itr2.70011","url":null,"abstract":"<p>Digital twin technology has emerged as a promising solution for the digital transformation of transportation infrastructure. This paper presents a comprehensive review of digital twin technology in the transportation industry, analyzing its relationship with key enabling technologies. By examining the development of digital twins across various transportation domains, we summarize the connotation, characteristics, and development trends of digital twins in transportation infrastructure. We propose a conceptual model and a digital system architecture for transportation infrastructure, along with a set of engineering application technical guidelines. Our findings reveal that current digital twin technology still faces challenges in driving the digital transformation of the transportation industry. From a theoretical perspective, the granularity of digital twin models is insufficient, lacking systematic support. In terms of application, the reconstruction of full-cycle digital processes primarily focuses on low-level applications. Future research should focus on theory innovation, data fusion, model integration, and professional applications to promote the development of digital twin technology in transportation infrastructure. Additionally, emphasis should be placed on collaborative design across disciplines and data standardization to build intelligent full-lifecycle management platforms, improve operation and maintenance efficiency, and provide new ideas and methods for sustainable development.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143612388","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":"SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving With Analytic Safety Guarantees","authors":"Tianyu Shi,&nbsp;Ilia Smirnov,&nbsp;Omar ElSamadisy,&nbsp;Baher Abdulhai","doi":"10.1049/itr2.70013","DOIUrl":"10.1049/itr2.70013","url":null,"abstract":"<p>Over the last decade, there has been increasing interest in autonomous driving systems. Reinforcement learning (RL) shows great promise for training autonomous driving controllers, being able to directly optimize a combination of criteria such as efficiency comfort, and stability. However, RL-based controllers typically offer no safety guarantees, making their readiness for real deployment questionable. In this paper, we propose SECRM-2D (the safe, efficient and comfortable RL-based driving model with lane-changing), an RL autonomous driving controller (both longitudinal and lateral) that balances optimization of efficiency and comfort and follows a fixed route, while being subject to hard analytic safety constraints. The aforementioned safety constraints are derived from the criterion that the follower vehicle must have sufficient headway to be able to avoid a crash if the leader vehicle brakes suddenly. We evaluate SECRM-2D against several learning and non-learning baselines in simulated test scenarios, including freeway driving, exiting, merging, and emergency braking. Our results confirm that representative previously published RL AV controllers may crash in both training and testing, even if they are optimizing a safety objective. By contrast, our controller SECRM-2D is successful in avoiding crashes during both training and testing, improves over the baselines in measures of efficiency and comfort, and is more faithful in following the prescribed route. In addition, we achieve a good theoretical understanding of the longitudinal steady-state of a collection of SECRM-2D vehicles.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594924","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":"An Efficient Illumination-Invariant Dynamic Facial Expression Recognition for Driving Scenarios","authors":"Ercheng Pei,&nbsp;Man Guo,&nbsp;Abel Díaz Berenguer,&nbsp;Lang He,&nbsp;HaiFeng Chen","doi":"10.1049/itr2.70009","DOIUrl":"10.1049/itr2.70009","url":null,"abstract":"<p>Facial expression recognition (FER) is significant in many application scenarios, such as driving scenarios with very different lighting conditions between day and night. Existing methods primarily focus on eliminating the negative effects of pose and identity information on FER, but overlook the challenges posed by lighting variations. So, this work proposes an efficient illumination-invariant dynamic FER method. To augment the robustness of FER methods to illumination variance, contrast normalisation is introduced to form a low-level illumination-invariant expression features learningmodule. In addition, to extract dynamic and salient expression features, a two-stage temporal attention mechanism is introduced to form a high-level dynamic salient expression features learning module deciphering dynamic facial expression patterns. Furthermore, additive angular margin loss is incorporated into the training of the proposed model to increase the distances between samples of different categories while reducing the distances between samples belonging to the same category. We conducted comprehensive experiments using the Oulu-CASIA and DFEW datasets. On the Oulu-CASIA VIS and NIR subsets in the normal illumination, the proposed method achieved accuracies of 92.08% and 91.46%, which are 1.01 and 7.06 percentage points higher than the SOTA results from the DCBLSTM and CELDL method, respectively. Based on the Oulu-CASIA NIR subset in the dark illumination, the proposed method achieved an accuracies of 91.25%, which are 4.54 percentage points higher than the SOTA result from the CDLLNet method. On the DFEW dataset, the proposed method achieved a UAR of 60.67% and a WAR of 71.48% with 12M parameters, approaching the SOTA result from the VideoMAE model with 86M parameters. The outcomes of our experiments validate the effectiveness of the proposed dynamic FER method, affirming its ability in addressing the challenges posed by diverse illumination conditions in driving scenarios.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554686","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 Two-Stage Energy-Efficiency Optimization Approach for Conflict-Free Dispatching in Open-Pit Mines","authors":"Zhichao Wang,&nbsp;Jue Yang,&nbsp;Yanbiao Feng,&nbsp;Yiting Kang,&nbsp;Yong Li","doi":"10.1049/itr2.70003","DOIUrl":"10.1049/itr2.70003","url":null,"abstract":"<p>The objective of this paper is to present a novel energy-efficiency conflict-free dispatching algorithm for autonomous mining fleets. In lieu of halting or decelerating the trucks at intersections when conflicts arise, the algorithm facilitates conflict-free dispatching for trucks to operate with the optimal speed trajectory, thereby achieving minimum fuel consumption and mining cost. This work first develops reference speed trajectories for mining trucks, considering their drivetrain characteristics, load status and geographic information pertaining to the path. Second, the total production determination model is based on the MILP model, which determines the total production of each path while taking the travel time into account with the objective of maximizing fleet production. Next, in the fleet allocation model and conflict-free scheduling model, the objectives are to reduce the fleet make span and fleet queuing time, respectively. Finally, a fleet operation timetable is eventually derived. Therefore, all trucks can operate intact according to the speed trajectory, thus minimizing fleet energy consumption and maximizing production efficiency. To verify the advantages of the model in this work, we selected DISPATCH and a multi-objective dispatching model developed by other researchers for comparison on the basis of the historical production data from an open pit coal mine. The results indicated that the proposed model exhibited the capacity to decrease the fleet size by 22.2%, thereby attaining equivalent production levels to those of a real open-pit coal mining fleet. Moreover, the model proposed in this paper can improve the production by about 36.11% to 49.75% compared to DISPATCH under the optimal speed trajectory, whereas the multi-objective dispatching model's improvement is only 9.84% to 21.89%. It also has significant advantages in terms of fleet productivity and fleet profit.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143533568","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":"Fault-Tolerant Robust Output-Feedback Control of a Vehicle Platoon Considering Measurement Noise and Road Disturbances","authors":"Fernando Viadero-Monasterio,&nbsp;Miguel Meléndez-Useros,&nbsp;Manuel Jiménez-Salas,&nbsp;María Jesús López Boada","doi":"10.1049/itr2.70007","DOIUrl":"10.1049/itr2.70007","url":null,"abstract":"<p>As electric vehicle (EV) technology advances, platooning has emerged as a promising strategy to enhance energy efficiency and traffic flow. However, the effective deployment of EV platoons is challenged by the heterogeneous nature of vehicles, measurement noise in sensing systems, and the possibility of faults in the actuation. This study proposes a fault-tolerant control framework for heterogeneous EV platoons, ensuring robustness against measurement noise. The framework integrates fault estimation mechanisms with robust control strategies to mitigate the impact of faults and disturbances, thereby enhancing the reliability and safety of platoon operations. We demonstrate the effectiveness of the proposed approach in maintaining platoon cohesion and stability under diverse operating conditions, including scenarios with varying levels of measurement noise and fault occurrences through extensive simulations and experiments. The findings highlight the capability of our fault-tolerant control framework to promote the extensive implementation of EV platooning technology, thereby enhancing energy efficiency and traffic management in future transportation systems.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143533567","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}