13 papers
Clustered Randomized Smoothing for Stochastic Prediction Functions
Eduardo Figueiredo, Frederik Mathiesen, Julian Schumann +3
Modern stochastic predictors can model rich, multi-modal outcome distributions. However, this expressive power comes with challenges in ensuring robust predictions a critical r…
StochasticBarrier.jl: A Toolbox for Stochastic Barrier Function Synthesis
Rayan Mazouz, Frederik Baymler Mathiesen, Luca Laurenti +1
We present StochasticBarrier.jl, an open-source Julia-based toolbox for generating Stochastic Barrier Functions (SBFs) for safety verification of discrete-time stochastic systems w…
Certified Neural Approximations of Nonlinear Dynamics
Frederik Baymler Mathiesen, Nikolaus Vertovec, Francesco Fabiano +2
Neural networks hold great potential to act as approximate models of nonlinear dynamical systems, with the resulting neural approximations enabling verification and control of such…
Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation
Nikolaus Vertovec, Frederik Baymler Mathiesen, Thom Badings +2
Control barrier functions (CBFs) are a popular tool for safety certification of nonlinear dynamical control systems. Recently, CBFs represented as neural networks have shown great…
Evaluating randomized smoothing as a defense against adversarial attacks in trajectory prediction
Julian F. Schumann, Eduardo Figueiredo, Frederik Baymler Mathiesen +3
Accurate and robust trajectory prediction is essential for safe and efficient autonomous driving, yet recent work has shown that even state-of-the-art prediction models are highly…
ARCH-COMP25 Category Report: Stochastic Models
Alessandro Abate, Omid Akbarzadeh, Henk A. P. Blom +14
This report is concerned with a friendly competition for formal verification and policy synthesis of stochastic models. The main goal of the report is to introduce new benchmarks a…