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Found 3 papers, 2 papers with code
Risk of Stochastic Systems for Temporal Logic Specifications
For discrete-time stochastic processes, we show under which conditions the approximate STL robustness risk can even be computed exactly.
Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations
Along with the optimization problem, we provide verifiable conditions in terms of the density of the data, smoothness of the system model and state estimator, and the size of the error bounds that guarantee validity of the obtained ROCBF.
Traffic Forecasting using Vehicle-to-Vehicle Communication
In order to best utilize real-world V2V communication data, we integrate first principle models with deep learning.
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