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Abstract To enhance the tracking accuracy and control stability of virtually coupled train sets (VCTS) in metros under uncertain disturbances, such as track vibrations and adverse weather, a distributed event-triggered model predictive control (MPC) strategy based on a high-gain disturbance observer (HGDO) is developed, with consideration of both the computational efficiency and disturbance rejection capability of the control system.
Abstract Efficient and accurate railway track segmentation is vital for autonomous train operation and early obstacle detection. However, blurred boundaries and complex backgrounds pose challenges for traditional instance segmentation models. In order to address the aforementioned issues, a novel instance segmentation framework, SMDE-YOLO, which builds upon YOLO11n-seg and is designed for railway track instance segmentation in complex environments.
Advanced search Transportation Letters The International Journal of Transportation Research Latest Articles Submit an article Journal homepage Full Article Figures & data References Citations Metrics Reprints & Permissions Read this article /doi/full/10.1080/19427867.2025.2496860?needAccess=true ABSTRACT This paper addresses delay problems from moderate and slight disturbances in high-speed train operation by constructing an optimization model that minimizes total train delay time, solved...
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