IET Intelligent Transport Systems最新文献

{"title":"A Highway Litter Detection Method Based on Multi-scale Feature Fusion and Dynamic Feature Enhancement","authors":"Changlu Guo,&nbsp;Yecai Guo,&nbsp;Songbin Li","doi":"10.1049/itr2.70141","DOIUrl":"https://doi.org/10.1049/itr2.70141","url":null,"abstract":"<p>To address the issues of missed detections, false positives and feature degradation in road debris detection caused by small and irregular targets, this study proposes a novel framework integrating multi-scale feature fusion and dynamic feature enhancement mechanisms. It also constructs a dedicated road debris dataset to fill the gap in public benchmark datasets in this field. Firstly, a cross-layer connection-optimized feature fusion network is designed in the neck network, addressing the limitation of insufficient fusion of shallow and deep features in existing methods, realizing efficient linkage between shallow texture features and deep semantic information, and significantly improving the detection capability for small targets. Secondly, a context-aware anchor attention module integrating reparameterized convolution and adaptive weight allocation is embedded into the backbone network. Compared with traditional fixed receptive field convolution, it can dynamically enhance target features and suppress background interference, effectively solving the problem of feature degradation in complex environments. Thirdly, an improved spatial pyramid fast pooling module based on global pooling and Ghost convolution is proposed, overcoming the defect of prone detail loss in traditional max-pooling and preserving key information of small-sized road debris to the greatest extent. Finally, a weighted fusion loss function integrating corner distance loss, focal loss, cross-scale correlation loss and CIoU loss is designed, breaking the limitation of insufficient attention to irregular targets in a single loss function and enhancing the model's adaptability to complex scenes. Experimental results show that the framework outperforms existing mainstream methods in road debris detection scenarios, achieving a precision of 91.5%, a recall of 82.0% and an mAP50 of 88.7%.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057735","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148088114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148088113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LSTM-Based Centralized/Decentralized Controller Design for Vehicular Platooning","authors":"Ryota Nakai,&nbsp;Kazumune Hashimoto,&nbsp;Xun Shen,&nbsp;Shigemasa Takai","doi":"10.1049/itr2.70151","DOIUrl":"https://doi.org/10.1049/itr2.70151","url":null,"abstract":"<p>Cooperative adaptive cruise control, also known as vehicular platooning, has gained significant interest for its ability to enhance fuel efficiency and comfort in vehicle operations. This study proposes novel control strategies for vehicular platooning based on long short-term memory (LSTM) neural networks. By learning temporal dependencies in vehicle behaviour, the proposed LSTM-based controllers improve string stability within the platoon, particularly under varying velocity patterns of the lead vehicle. Two distinct frameworks are investigated: centralized and decentralized control models. The centralized model makes use of the states of all vehicles within the platoon, whereas the decentralized model focuses on the states of only a limited number of preceding vehicles. Simulation experiments demonstrate that both the centralized and decentralized LSTM controllers significantly outperform traditional, non-LSTM-based controllers in minimizing cumulative inter-vehicle error. This study contributes a novel controller training methodology that integrates LSTM-based architectures with optimal control principles, offering improved adaptability and flexibility in real-time platoon management.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146099433","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148086701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on Intersection Delay Prediction Based on Spatiotemporal Graph Convolutional Network Using Delay Big Data","authors":"Bohang Liu,&nbsp;Yahang Wang,&nbsp;Jiashun Wu,&nbsp;Chenglin Wei,&nbsp;Huiyao Gao","doi":"10.1049/itr2.70143","DOIUrl":"https://doi.org/10.1049/itr2.70143","url":null,"abstract":"<p>Road intersections are the key nodes in urban road networks, and their operational efficiency affects the dynamic balance of regional traffic flow. However, classical intersection delay models are unable to quickly and efficiently predict large-scale intersection delays. This paper proposes a spatiotemporal graph convolutional network (STGCN) intersection delay prediction model based on delay big data. By introducing a temporal attention mechanism, the model can capture key temporal information and enhance the ability to model the spatio-temporal evolution law of intersection delays. First, based on traffic platform delay big data, external factor data, and road network structure, the historical delay matrix, the external factor matrix, and intersection adjacency matrix are constructed, respectively. Next, the graph convolutional network (GCN) is used to extract spatial features of intersection delay. A temporal attention mechanism is then introduced to assign weights to different time steps, thereby enhancing the model's perception of critical time steps. Finally, the temporal convolutional network (TCN) is employed to extract temporal features of intersection delay, which are used to predict future intersection delays. Experimental results show that, compared with the optimal benchmark model STGCN, the proposed model reduces the MAE, RMSE, and MAPE metrics by 3.80%, 2.75%, and 4.23%, respectively. This study on intersection delay prediction using a STGCN based on delay big data not only improves the efficiency of intersection delay prediction but also provides a theoretical basis for traffic management departments to formulate measures.This study focuses on the delay prediction problem of intersections, key nodes in urban road networks. Aiming at the deficiency that classical models are difficult to efficiently predict the delays of large-scale intersections, a STGCN model based on delay big data is proposed. This model constructs the historical delay matrix, the external factor matrix and the intersection adjacency matrix, uses the GCN to extract spatial features, combines the temporal attention mechanism to enhance the perception of key periods, and then uses the TCN to mine temporal features to achieve delay prediction. Experiments show that, compared with the optimal benchmark model STGCN, its MAE, RMSE, and MAPE indicators are reduced by 3.80%, 2.75%, and 4.23%, respectively, providing theoretical support for improving the efficiency of intersection delay prediction and traffic management decision-making.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146002191","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148078435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Improved Rapidly-Exploring Approach to Off-Road Path Planning by Leveraging Dynamic Velocity Constraints and Trajectory Smoothing","authors":"Jiang Song,&nbsp;Shucai Xu,&nbsp;Chun Feng,&nbsp;Liqun Peng","doi":"10.1049/itr2.70148","DOIUrl":"https://doi.org/10.1049/itr2.70148","url":null,"abstract":"<p>Off-road path planning and navigation often struggle with complex challenges, such as diverse surface conditions that demand adaptability, stability-sensitive vehicle dynamics on low-adhesion terrain, and the persistent trade-off between real-time performance and path quality. To address these challenges, an improved rapidly-exploring random tree (IRRT) algorithm is developed to adjust the dynamic exploration domain considering the vehicle's design speed and local terrain features, which can affect vehicle's operational stability, thereby increasing path feasibility and environmental adaptability. Furthermore, a nonlinear model predictive controller (NMPC) is deployed in the lower layer of the proposed RRT path planning framework, smoothing the generated path and enhancing ride comfort through terrain-aware adjustments. Both a 100 × 100 meter simulated environment and a real-world 1:10 scale test site, featuring distinct terrain types, i.e., hard roads, natural terrain, and low hills, with obstacles. The results show that the proposed two-layer path planning framework, improved RRT algorithm integrating with NMPC, reduces path length by 6.9% and total turning angle by 12.3% compared to RRT, while maintaining a maximum curvature of 0.134 m⁻¹ (well within the safety limit of 0.2 m⁻¹) and improving ride comfort by 80.4%. On the other hand, although the computation time increases by 272.2%, the resulting gains in path quality and stability justify the trade-off. The proposed method demonstrates a viable solution for off-road vehicle navigation across diverse terrains, effectively balancing path feasibility, ride smoothness, and computational efficiency.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145970008","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148075280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"XGBoost–LSTM Regional Traffic Congestion Ratio Prediction Integrating Spatio-Temporal and Weather Features","authors":"Bohang Liu,&nbsp;Xudong Zhang,&nbsp;Chengcheng Liang,&nbsp;Tongchuang Zhang,&nbsp;Keyi Xiang","doi":"10.1049/itr2.70145","DOIUrl":"https://doi.org/10.1049/itr2.70145","url":null,"abstract":"<p>Urban traffic prediction is of great significance for traffic management and optimisation. Although research on predicting indicators such as traffic flow and speed is relatively sufficient, research on forecasting congestion ratios in different urban regions remains inadequate. Based on traffic big data, this paper proposes a fusion regional congestion ratio prediction model integrating eXtreme gradient boosting tree (XGBoost) and long short-term memory (LSTM), which integrates multi-source features, including temporal, meteorological, and spatial factors. First, the XGBoost algorithm is used to model the historical congestion ratios and related features of each region, obtaining preliminary prediction results and extracting regional residual sequences; subsequently, the residual sequences are input into the LSTM network for error correction. Finally, the prediction results of the two stages are fused to obtain more refined regional congestion ratio predictions. Experimental results show that during peak hours on weekdays, taking Region 49 as an example, the MAE of the fusion model is 0.062, the mean absolute percentage error is below 30%, and the comprehensive prediction accuracy reaches up to 72%; under complex weather conditions, for the same region, the RMSE values of the fusion model are 0.048, 0.058, and 0.043, respectively, which are 37%–63% lower than those of the XGBoost model used alone. Feature ablation experiments further verify the key role of temporal, meteorological, and spatial features in improving prediction performance, among which spatial features contribute the most to performance optimisation. This study improves the research framework in the field of urban traffic prediction and provides a theoretical basis and methodological support for regional traffic management practices.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145983674","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}