ModellingParallel branch MPC on GPUsarXiv — Systems and Control (eess.SY) · yesterdayModellingImpact of shared autonomous vehicles in microtransit: Atlanta casearXiv — Systems and Control (eess.SY) · yesterdayModellingHybrid simulation of circular supply chains in healthcarearXiv — Systems and Control (eess.SY) · yesterdayDigital twinFDF tool simplifies building AI digital twinsarXiv API — полнотекстовый поиск "digital twin" · yesterdayForecastingMPC and genetic algorithm for flying pendulum controlarXiv — Systems and Control (eess.SY) · yesterdayForecastingHybrid control architecture for safe vehicle lateral controlarXiv — Systems and Control (eess.SY) · yesterdayForecastingAdaptive MPC for ground vehicles: review and implementationarXiv — Systems and Control (eess.SY) · yesterdayDigital twinML-assisted Bayesian Calibration of Accelerator Digital TwinarXiv API — полнотекстовый поиск "digital twin" · yesterdayDigital twinDigital Twin Testbed for Cyberattack and Defense Analysis in Hospital IT/OT EnvironmentsarXiv API — полнотекстовый поиск "digital twin" · yesterdayModellingGenerative Agent-based Simulation of Mobility Policy Preferences from SurveysarXiv — Computers and Society (cs.CY) · 2dForecastingScalable Gaussian Process with Trigonometric Features for Safe MPCarXiv — Systems and Control (eess.SY) · 2dForecastingTwo-Layer MPC for Sustainable Data Centers with Workload Flexibility and Heat RecoveryarXiv — Systems and Control (eess.SY) · 2d
Hierarchical federated learning for digital twins of vehicular networks
US & West ·
A hierarchical federated transfer learning (HFTL) method for digital twins of VANETs is proposed. It combines federated learning with transfer learning, clustering vehicles by type to address data heterogeneity and sparsity. Algorithms for cloud server model updates and intra-cluster federated learning are developed within DT-VANET, improving global model accuracy.
Why it matters
The method is relevant to the agenda as it improves accuracy of predictive models of digital twins under heterogeneous and sparse data, which is relevant for urban transport management.
A GPU-based solver for trajectory planning problems using branch model predictive control is presented. The solver is based on iterative LQR, multiple-shooting formulation, and augmented Lagrangian method for constraint handling. Numerical experiments show superiority over a CPU-based solver on large-scale problems.
The integration of shared autonomous vehicles (SAVs) into microtransit systems, which address the last-mile problem, is investigated. The Atlanta case study shows that SAVs can improve sustainability, convenience, and reliability compared to conventional fixed-route transit.
Function+Data Flow (FDF), a visual domain-specific language for specifying and validating AI/ML pipelines used in building real-time digital twins, is presented. Implemented in DesCartes Builder, it enables composition and reuse of models. An empirical study found FDF makes AI-based twin development more accessible and reliable.
