IET Intelligent Transport Systems最新文献

{"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":"https://doi.org/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":2.3,"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":"https://doi.org/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":2.3,"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":"https://doi.org/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":2.3,"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":"https://doi.org/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":2.3,"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":"https://doi.org/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":2.3,"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}

{"title":"Prescribed Performance Ship Tracking Control With a Novel Predefined-Time Performance Function","authors":"Han Xue,&nbsp;Xiangtao Wang","doi":"10.1049/itr2.70014","DOIUrl":"https://doi.org/10.1049/itr2.70014","url":null,"abstract":"<p>How to accurately process and achieve good transient performance in a short period of time is a key consideration factor for the system. A hyperbolic sine function is used to construct a novel predefined-time convergent prescribed performance function. This algorithm introduces a set of new predefined standards for time convergence assessment based on gamma functions and Riemann zeta functions. By integrating performance indicators of speed, stability and efficiency into the design of the prescribed performance function, the performance framework ensures the achievement of establishing a comprehensive performance optimization model. The upper limit of the settling time is studied, and sufficient conditions for achieving predetermined time convergence are established, validated through experiments using unmanned surface vessels.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143475632","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 complete in-cabin monitoring framework for autonomous vehicles in public transportation","authors":"Dimitris Tsiktsiris,&nbsp;Antonios Lalas,&nbsp;Minas Dasygenis,&nbsp;Konstantinos Votis","doi":"10.1049/itr2.12612","DOIUrl":"https://doi.org/10.1049/itr2.12612","url":null,"abstract":"<p>Autonomous vehicles (AVs), driven by state-of-the-art deep learning and computer vision technologies, can revolutionize current mobility systems in modern transportation. Driverless AVs are slowly integrated into public transportation with significant advantages for the passengers and public transport operators. However, passenger safety and comfort are two of the main challenges that need to be addressed. This work presents a complete in-cabin monitoring framework with a suite of services, employing deep learning algorithms using a variety of onboard sensors at the edge. This proposed framework offers various innovative services aimed at enhancing security, monitoring passenger presence, accommodating diverse needs, and personalizing the passengers' travel experience, while also reducing the workload of human safety officers. Experimental results demonstrate the framework's effectiveness in identifying abnormal events with a high accuracy, employing multiple datasets and custom in-cabin scenarios. Additionally, the system effectively conducts automated passenger counting and facial identification, ensuring real-time responsiveness under diverse operational conditions. Overall, the novelty of this work lies in the framework's multimodal approach, integrating visual and audio analysis, to achieve robust performance across various scenarios, significantly contributing to the advancement of autonomous driving technologies.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12612","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481532","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":"Anti-Saturation Sliding Mode Control for Virtually Coupled HHTs Under Saturation Constraints","authors":"Jing He,&nbsp;Yu Long,&nbsp;Changfan Zhang","doi":"10.1049/itr2.70008","DOIUrl":"https://doi.org/10.1049/itr2.70008","url":null,"abstract":"<p>Maintaining an appropriate distance between trains is key to the normal operation of multiple trains in the virtual coupling mode. However, owing to physical limitations, the saturation of the control system is prone to occur during actual train operations, which makes it difficult to maintain a safe distance between adjacent trains when the speed changes. An anti-saturation sliding mode control algorithm for multiple virtually coupled trains was proposed to address this issue. First, according to the virtual coupling dynamics model of multiple heavy-haul trains (HHTs), an improved finite-time anti-windup compensator (FAWC) suitable for the train model was designed such that the compensation factor rapidly converged within a finite time. Second, the FAWC was introduced into the controller to suppress the input saturation phenomenon of trains. Then, a finite-time dual anti-saturation sliding mode controller (FDA-SMC) was constructed based on the barrier Lyapunov function in combination with the sliding mode algorithm against input constraints to suppress the impact of input and output saturation on the tracking accuracy for the relative position between adjacent HHTs. The stability of the closed-loop system was verified using the Lyapunov stability theory. Finally, the simulation and experimental results showed that the proposed algorithm demonstrated advantages in terms of anti-saturation performance and maintained a safe distance between adjacent HHTs.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481546","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":"Evaluating Driver Readiness in Conditionally Automated Vehicles From Eye-Tracking Data and Head Pose","authors":"Mostafa Kazemi,&nbsp;Mahdi Rezaei,&nbsp;Mohsen Azarmi","doi":"10.1049/itr2.70006","DOIUrl":"https://doi.org/10.1049/itr2.70006","url":null,"abstract":"<p>As automated driving technology advances, the role of the driver to resume control of the vehicle in conditionally automated vehicles becomes increasingly critical. In the SAE level 3 or partly automated vehicles, the driver needs to be available and ready to intervene when necessary. This makes it essential to evaluate their readiness accurately. This article presents a comprehensive analysis of driver readiness assessment by combining head pose features and eye-tracking data. The study explores the effectiveness of predictive models in evaluating driver readiness, addressing the challenges of dataset limitations and limited ground truth labels. Machine learning techniques, including LSTM architectures, are utilised to model driver readiness based on the spatio-temporal status of the driver's head pose and eye gaze. The experiments in this article revealed that a bidirectional LSTM architecture, combining both feature sets, achieves a mean absolute error of 0.363 on the DMD dataset, demonstrating superior performance in assessing driver readiness. The modular architecture of the proposed model also allows the integration of additional driver-specific features, such as steering wheel activity, enhancing its adaptability and real-world applicability.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456135","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":"KTnet: Hazy weather object detection based on knowledge transfer","authors":"Haigang Deng,&nbsp;Zhiheng Lu,&nbsp;Chengwei Li,&nbsp;Tong Wang,&nbsp;Changshi Liu,&nbsp;Qian Xiong","doi":"10.1049/itr2.12606","DOIUrl":"https://doi.org/10.1049/itr2.12606","url":null,"abstract":"<p>The current method to address the reduced accuracy of target detection algorithms in hazy weather scenes is mainly to first use image dehazing algorithms to restore hazy images, and then input the restored images into target detection algorithms to obtain detection results. However, the images restored by the image dehazing model deviate from real clear images, and do not completely recover the features required by the target detection algorithm, thus limiting the improvement of the detection accuracy of the target detection model. This paper proposes a hazy weather target detection algorithm based on large convolution kernels and knowledge transfer (KTnet). First, a large convolution attention dehazing module is embedded into the backbone network of faster R-CNN to form a dehazing backbone network. Considering the high-dimensional features of the deep backbone network, a lightweight fusion attention module is designed. A loss function is also designed and the adapter model is employed to devise training methods for knowledge transfer and fine-tuning. Extensive experimental results on various hazy weather target detection datasets show that KTnet has achieved significant effectiveness.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439208","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}